datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
louisbrulenaudet/code-voirie-routiere | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la voirie routière
source_datasets:
- original
pretty_name: Code de la voirie routière
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de la voirie routière, 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). |
HenriVocals/Lairvoz | ---
license: openrail
---
|
maidalun1020/CrosslingualRetrievalWikiZh2En-qrels | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 840211
num_examples: 34291
download_size: 457460
dataset_size: 840211
---
|
baryfamily/forex | ---
license: mit
---
|
pratapswati/rana-doly-mini | ---
license: mit
---
|
lonestar108/enlightenedllm | ---
language:
- en
license: openrail
task_categories:
- text-generation
pretty_name: Enlightened LLM
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 458879
num_examples: 2070
- name: test
num_bytes: 123454
num_examples: 574
- name: validation
num_bytes: 50830
num_examples: 229
download_size: 337213
dataset_size: 633163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
tags:
- religion
- spirituality
---
# Dataset Card for "enlightenedllm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
omp | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: one-million-posts-corpus
pretty_name: One Million Posts
dataset_info:
- config_name: posts_labeled
features:
- name: ID_Post
dtype: string
- name: ID_Parent_Post
dtype: string
- name: ID_Article
dtype: string
- name: ID_User
dtype: string
- name: CreatedAt
dtype: string
- name: Status
dtype: string
- name: Headline
dtype: string
- name: Body
dtype: string
- name: PositiveVotes
dtype: int32
- name: NegativeVotes
dtype: int32
- name: Category
dtype:
class_label:
names:
'0': ArgumentsUsed
'1': Discriminating
'2': Inappropriate
'3': OffTopic
'4': PersonalStories
'5': PossiblyFeedback
'6': SentimentNegative
'7': SentimentNeutral
'8': SentimentPositive
- name: Value
dtype: int32
- name: Fold
dtype: int32
splits:
- name: train
num_bytes: 13955964
num_examples: 40567
download_size: 1329892
dataset_size: 13955964
- config_name: posts_unlabeled
features:
- name: ID_Post
dtype: string
- name: ID_Parent_Post
dtype: string
- name: ID_Article
dtype: string
- name: ID_User
dtype: string
- name: CreatedAt
dtype: string
- name: Status
dtype: string
- name: Headline
dtype: string
- name: Body
dtype: string
- name: PositiveVotes
dtype: int32
- name: NegativeVotes
dtype: int32
splits:
- name: train
num_bytes: 305770324
num_examples: 1000000
download_size: 79296188
dataset_size: 305770324
- config_name: articles
features:
- name: ID_Article
dtype: string
- name: Path
dtype: string
- name: publishingDate
dtype: string
- name: Title
dtype: string
- name: Body
dtype: string
splits:
- name: train
num_bytes: 43529400
num_examples: 12087
download_size: 10681288
dataset_size: 43529400
---
# Dataset Card for One Million Posts Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ofai.github.io/million-post-corpus/
- **Repository:** https://github.com/OFAI/million-post-corpus
- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper.
The data set contains the following data for each post:
* Post ID
* Article ID
* Headline (max. 250 characters)
* Main Body (max. 750 characters)
* User ID (the user names used by the website have been re-mapped to new numeric IDs)
* Time stamp
* Parent post (replies give rise to tree-like discussion thread structures)
* Status (online or deleted by a moderator)
* Number of positive votes by other community members
* Number of negative votes by other community members
For each article, the data set contains the following data:
* Article ID
* Publishing date
* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
* Title
* Body
Detailed descriptions of the post selection and annotation procedures are given in the paper.
#### Annotated Categories
Potentially undesirable content:
* Sentiment (negative/neutral/positive)
An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., the location within the fora and the point in time where a turn from positive/neutral sentiment to negative sentiment takes place.
* Off-Topic (yes/no)
Posts which digress too far from the topic of the corresponding article.
* Inappropriate (yes/no)
Swearwords, suggestive and obscene language, insults, threats etc.
* Discriminating (yes/no)
Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.
Neutral content that requires a reaction:
* Feedback (yes/no)
Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction.
Potentially desirable content:
* Personal Stories (yes/no)
In certain fora, users are encouraged to share their personal stories, experiences, anecdotes etc. regarding the respective topic.
* Arguments Used (yes/no)
It is desirable for users to back their statements with rational argumentation, reasoning and sources.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Austrian German
## Dataset Structure
### Data Instances
An example from the `posts_labeled` config:
```json
{
"ID_Post": "79",
"ID_Parent_Post": "",
"ID_Article": "1",
"ID_User": "12071",
"CreatedAt": "2015-06-01 08:58:32.363",
"Status": "online",
"Headline": "",
"Body": "ich kann keinen hinweis finden, wo man sich hinwenden muss, sollte man als abonnent des standard, die zeitung nicht bekommt, ist dass bewusst so arrangiert?",
"PositiveVotes": 0,
"NegativeVotes": 0,
"Category": 5,
"Value": 1,
"Fold": 1
}
```
An example from the `posts_unlabeled` config:
```json
{
"ID_Post": "51",
"ID_Parent_Post": "",
"ID_Article": "1",
"ID_User": "11125",
"CreatedAt": "2011-05-15 08:37:11.313",
"Status": "online",
"Headline": "Ich würde es sehr begrüßen, wenn",
"Body": "Antworten erst beim Erscheinen als e-Mail dem Poster zugestellt würden.\r\n\r\nEs gibt User, die ihre Kommentare sofort nach Mail-Eingang irgendwo hinposten. Dadurch wird \r\n1. vor allem für andere Unser die Lesbarkeit wesentlich beeinträchtigt,\r\n2. kann das Post verdreht wiedergegeben werden,\r\n3. man ist immer wieder gezwungen die Antwort richtig zu stellen.\r\n\r\nPrivatfehden von Usern sollten, wenn schon zugelassen, für alle User nachvollziehbar sein.\r\n\r\nDanke!",
"PositiveVotes": 1,
"NegativeVotes": 0
}
```
An example from the `articles` config:
```json
{
"ID_Article": "41",
"Path": "Newsroom/Wirtschaft/Wirtschaftpolitik/Energiemarkt",
"publishingDate": "2015-06-01 12:39:35.00",
"Title": "Öl- und Gas-Riesen fordern weltweite CO2-Preise",
"Body": '<div class="section" id="content-main" itemprop="articleBody"><div class="copytext"><h2 itemprop="description">Brief von BP, Total, Shell, Statoil, BG Group und Eni unterzeichnet</h2><p>Paris/London/La Defense - Sechs große Öl- und Gaskonzerne haben mit Blick auf die Verhandlungen über einen neuen Welt-Klimavertrag ein globales Preissystem für CO2-Emissionen gefordert. Wenn der Ausstoß von CO2 Geld kostet, sei dies ein Anreiz für die Nutzung von Erdgas statt Kohle, mehr Energieeffizienz und Investitionen zur Vermeidung des Treibhausgases, heißt es in einem am Montag veröffentlichten Brief.</p>\n<p>Das Schreiben ist unterzeichnet von BP, Total, Shell, Statoil, BG Group und Eni. Die Unternehmen versicherten, sie seien bereit, ihren Teil zum Kampf gegen den <a href="/r1937/Klimawandel">Klimawandel</a> beizutragen. Dafür sei aber ein klarer und verlässlicher Politik-Rahmen nötig. (APA, 1.6.2015)</p> </div></div>'
}
```
### Data Fields
The data set contains the following data for each post:
* **ID_Post**: Post ID
* **ID_Parent_Post**: Parent post (replies give rise to tree-like discussion thread structures)
* **ID_Article**: Article ID
* **ID_User**: User ID (the user names used by the website have been re-mapped to new numeric IDs)
* **Headline**: Headline (max. 250 characters)
* **Body**: Main Body (max. 750 characters)
* **CreatedAt**: Time stamp
* **Status**: Status (online or deleted by a moderator)
* **PositiveVotes**: Number of positive votes by other community members
* **NegativeVotes**: Number of negative votes by other community members
Labeled posts also contain:
* **Category**: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive
* **Value**: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of `ArgumentsUsed` with value of `0` means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label).
* **Fold**: a number between [0-9] from a 10-fold split by the authors
For each article, the data set contains the following data:
* **ID_Article**: Article ID
* **publishingDate**: Publishing date
* **Path**: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
* **Title**: Title
* **Body**: Body
### Data Splits
Training split only.
| name | train |
|-----------------|--------:|
| posts_labeled | 40567 |
| posts_unlabeled | 1000000 |
| articles | 12087 |
## 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
This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
### Citation Information
```
@InProceedings{Schabus2018,
author = {Dietmar Schabus and Marcin Skowron},
title = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website},
booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)},
year = {2018},
address = {Miyazaki, Japan},
month = may,
pages = {1602-1605},
abstract = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.},
url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html},
}
```
### Contributions
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset. |
unigram/fol-02b | ---
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: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
- name: proof
dtype: string
- name: premise_tptp
dtype: string
- name: hypothesis_tptp
dtype: string
- name: deberta_pred
dtype: string
- name: deberta_pred_r1_label
dtype: string
- name: deberta_pred_r2_label
dtype: string
splits:
- name: train
num_bytes: 39286182
num_examples: 5520
- name: validation
num_bytes: 5297126
num_examples: 777
- name: test
num_bytes: 5548296
num_examples: 768
download_size: 8560940
dataset_size: 50131604
---
# Dataset Card for "fol-02b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lamm-mit/x-lora-dataset | ---
license: apache-2.0
---
Paper, see: arxiv.org/abs/2402.07148 |
louisbrulenaudet/code-expropriation-utilite-publique | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de l'expropriation pour cause d'utilité publique
source_datasets:
- original
pretty_name: Code de l'expropriation pour cause d'utilité publique
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de l'expropriation pour cause d'utilité publique, 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). |
argilla/10k_prompts_SPIN_iter2_zephyr_top | ---
dataset_info:
features:
- name: generated
list:
- name: content
dtype: string
- name: role
dtype: string
- name: real
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 9187059.362445414
num_examples: 1648
- name: test
num_bytes: 1025739.6375545851
num_examples: 184
download_size: 5809205
dataset_size: 10212799.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
infCapital/finance-alpaca_vi | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 48252402
num_examples: 66665
download_size: 24622108
dataset_size: 48252402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- vi
---
# Dataset Card for "finance-alpaca_vi"
+ Origin dataset [finance-alpaca](https://huggingface.co/datasets/gbharti/finance-alpaca )
+ Translated into Vietnamese using OpenAI GPT3.5 API |
LUOHANYU/test_ragas | ---
license: other
license_name: ragas
license_link: LICENSE
language:
- en
--- |
CyberHarem/shiomi_shuuko_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shiomi_shuuko/塩見周子/시오미슈코 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of shiomi_shuuko/塩見周子/시오미슈코 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `short_hair, grey_hair, hair_between_eyes, breasts, black_eyes, earrings, bangs, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 680.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiomi_shuuko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 384.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiomi_shuuko_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1191 | 808.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiomi_shuuko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 596.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiomi_shuuko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1191 | 1.14 GiB | [Download](https://huggingface.co/datasets/CyberHarem/shiomi_shuuko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shiomi_shuuko_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, collarbone, smile, upper_body, blush, necklace, simple_background, cleavage, white_background, bare_shoulders, off_shoulder, closed_mouth, dress, white_shirt |
| 1 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, cleavage, collarbone, navel, smile, black_bikini, jewelry, simple_background, sitting, white_background, large_breasts, o-ring |
| 2 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, side-tie_bikini_bottom, simple_background, solo, white_background, black_bikini, navel, smile, cleavage, micro_bikini, bare_shoulders, black_gloves, black_thighhighs, collarbone, elbow_gloves, black_choker, jewelry, large_breasts, open_mouth, thighs |
| 3 | 8 |  |  |  |  |  | 1girl, cleavage, collarbone, denim_shorts, hair_flower, looking_at_viewer, navel, solo, striped_bikini, short_shorts, smile, belt, blush, cutoffs, necklace, bare_shoulders, choker, layered_bikini, outdoors, bikini_top_only, blue_sky, brown_eyes, cloud, cowboy_shot, day, front-tie_bikini_top, ocean, crown_braid, flower_bracelet, leaning_forward, standing, water, yellow_flower |
| 4 | 5 |  |  |  |  |  | 1girl, belt, cleavage, collarbone, cowboy_shot, cutoffs, denim_shorts, front-tie_bikini_top, hair_flower, looking_at_viewer, navel, short_shorts, solo, striped_bikini, bare_shoulders, bikini_top_only, blush, choker, highleg_bikini, layered_bikini, necklace, white_background, :d, blonde_hair, crown_braid, o-ring, open_mouth, simple_background, arm_up, armpits, arms_behind_back, bikini_under_clothes, bracelet, chain, upper_teeth_only |
| 5 | 6 |  |  |  |  |  | 1girl, hair_ornament, looking_at_viewer, solo, blue_dress, flower, open_mouth, bracelet, sleeveless_dress, :d, blonde_hair, blush, butterfly, high_heels, night_sky |
| 6 | 13 |  |  |  |  |  | looking_at_viewer, open_cardigan, 1girl, bare_shoulders, blush, camisole, cleavage, smile, solo, collarbone, off_shoulder, short_shorts, denim_shorts, necklace, simple_background, long_sleeves, white_background, midriff, lying, navel, parted_lips |
| 7 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, detached_sleeves, hair_flower, jewelry, obi, folding_fan, wide_sleeves, bare_shoulders, blush, brown_eyes, floral_print, cleavage, holding_fan, long_sleeves, petals, cherry_blossoms, short_kimono, white_background |
| 8 | 9 |  |  |  |  |  | 1girl, fox_ears, fox_tail, kimono, looking_at_viewer, solo, wide_sleeves, blush, extra_ears, fox_mask, hair_flower, jewelry, smile, bare_shoulders, cleavage, fox_shadow_puppet, jingle_bell, detached_sleeves, obi, tabi |
| 9 | 6 |  |  |  |  |  | 1girl, detached_sleeves, extra_ears, fox_ears, fox_tail, solo, fox_mask, fox_shadow_puppet, looking_at_viewer, blush, jewelry, smile, blonde_hair, japanese_clothes, nail_polish, navel |
| 10 | 9 |  |  |  |  |  | 1girl, smile, solo, fingerless_gloves, looking_at_viewer, bracelet, garter_straps, mini_hat, short_sleeves, thighhighs, black_gloves, folding_fan, holding_fan, skirt, vertical_stripes |
| 11 | 5 |  |  |  |  |  | 2girls, blush, solo_focus, smile, blonde_hair, collarbone, large_breasts, looking_at_viewer, nipples, completely_nude, jewelry, lying, navel |
| 12 | 11 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, solo_focus, penis, sex, large_breasts, looking_at_viewer, spread_legs, vaginal, missionary, navel, on_back, open_mouth, sweat, pillow, pov, pussy, collarbone, completely_nude, cum, jewelry, male_pubic_hair, mosaic_censoring, on_bed |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | collarbone | smile | upper_body | blush | necklace | simple_background | cleavage | white_background | bare_shoulders | off_shoulder | closed_mouth | dress | white_shirt | navel | black_bikini | jewelry | sitting | large_breasts | o-ring | side-tie_bikini_bottom | micro_bikini | black_gloves | black_thighhighs | elbow_gloves | black_choker | open_mouth | thighs | denim_shorts | hair_flower | striped_bikini | short_shorts | belt | cutoffs | choker | layered_bikini | outdoors | bikini_top_only | blue_sky | brown_eyes | cloud | cowboy_shot | day | front-tie_bikini_top | ocean | crown_braid | flower_bracelet | leaning_forward | standing | water | yellow_flower | highleg_bikini | :d | blonde_hair | arm_up | armpits | arms_behind_back | bikini_under_clothes | bracelet | chain | upper_teeth_only | hair_ornament | blue_dress | flower | sleeveless_dress | butterfly | high_heels | night_sky | open_cardigan | camisole | long_sleeves | midriff | lying | parted_lips | detached_sleeves | obi | folding_fan | wide_sleeves | floral_print | holding_fan | petals | cherry_blossoms | short_kimono | fox_ears | fox_tail | kimono | extra_ears | fox_mask | fox_shadow_puppet | jingle_bell | tabi | japanese_clothes | nail_polish | fingerless_gloves | garter_straps | mini_hat | short_sleeves | thighhighs | skirt | vertical_stripes | 2girls | solo_focus | nipples | completely_nude | 1boy | hetero | penis | sex | spread_legs | vaginal | missionary | on_back | sweat | pillow | pov | pussy | cum | male_pubic_hair | mosaic_censoring | on_bed |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-------|:-------------|:--------|:-------------|:--------|:-----------|:--------------------|:-----------|:-------------------|:-----------------|:---------------|:---------------|:--------|:--------------|:--------|:---------------|:----------|:----------|:----------------|:---------|:-------------------------|:---------------|:---------------|:-------------------|:---------------|:---------------|:-------------|:---------|:---------------|:--------------|:-----------------|:---------------|:-------|:----------|:---------|:-----------------|:-----------|:------------------|:-----------|:-------------|:--------|:--------------|:------|:-----------------------|:--------|:--------------|:------------------|:------------------|:-----------|:--------|:----------------|:-----------------|:-----|:--------------|:---------|:----------|:-------------------|:-----------------------|:-----------|:--------|:-------------------|:----------------|:-------------|:---------|:-------------------|:------------|:-------------|:------------|:----------------|:-----------|:---------------|:----------|:--------|:--------------|:-------------------|:------|:--------------|:---------------|:---------------|:--------------|:---------|:------------------|:---------------|:-----------|:-----------|:---------|:-------------|:-----------|:--------------------|:--------------|:-------|:-------------------|:--------------|:--------------------|:----------------|:-----------|:----------------|:-------------|:--------|:-------------------|:---------|:-------------|:----------|:------------------|:-------|:---------|:--------|:------|:--------------|:----------|:-------------|:----------|:--------|:---------|:------|:--------|:------|:------------------|:-------------------|:---------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | X | | | | | X | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | X | X | X | X | X | | X | X | | X | | X | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | X | | | X | X | X | X | X | X | | | | | X | | | | | X | | | | | | | X | | X | X | X | X | X | X | X | X | | X | | | | X | | X | | X | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | X | X | X | | | | X | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 13 |  |  |  |  |  | X | X | X | | X | | X | | | X | X | X | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | X | X | | X | | X | | | X | | X | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | X | X | X | | X | | X | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 9 |  |  |  |  |  | X | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | | X | | X | X | | X | | | | | | | | | | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | |
| 12 | 11 |  |  |  |  |  | X | X | | X | | | X | | | | | | | | | | X | | X | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
TrainingDataPro/cows-detection-dataset | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- image-classification
- object-detection
tags:
- biology
- code
dataset_info:
features:
- name: id
dtype: int32
- name: image
dtype: image
- name: mask
dtype: image
- name: bboxes
dtype: string
splits:
- name: train
num_bytes: 184108240
num_examples: 51
download_size: 183666433
dataset_size: 184108240
---
# Cows Detection Dataset
The dataset is a collection of images along with corresponding bounding box annotations that are specifically curated for **detecting cows** in images. The dataset covers different *cows breeds, sizes, and orientations*, providing a comprehensive representation of cows appearances and positions. Additionally, the visibility of each cow is presented in the .xml file.
The cow detection dataset provides a valuable resource for researchers working on detection tasks. It offers a diverse collection of annotated images, allowing for comprehensive algorithm development, evaluation, and benchmarking, ultimately aiding in the development of accurate and robust models.
# 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/cows-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=cows-detection-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of cows
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for cows detection. For each point, the x and y coordinates are provided. Visibility of the cow is also provided by the label **is_visible** (true, false).
# Example of XML file structure
.png?generation=1692032268744062&alt=media)
# Cows Detection might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market/cows-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=cows-detection-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** |
AIRI-NLP/quality_counter_new_4096 | ---
dataset_info:
features:
- name: context
dtype: string
- name: word
dtype: string
- name: claim
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 553063030
num_examples: 20000
- name: validation
num_bytes: 222441398
num_examples: 8000
- name: test
num_bytes: 56237814
num_examples: 2300
download_size: 26258815
dataset_size: 831742242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
pradeep239/wipro_shuffle_300_dates | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 416677298.0
num_examples: 833
- name: validation
num_bytes: 47080838.0
num_examples: 98
- name: test
num_bytes: 23364187.0
num_examples: 49
download_size: 406729675
dataset_size: 487122323.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
Eitanli/clean_title | ---
dataset_info:
features:
- name: id
dtype: int64
- name: recipe
dtype: string
- name: title_cleaned
dtype: string
splits:
- name: train
num_bytes: 108673191
num_examples: 74465
download_size: 55560085
dataset_size: 108673191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "clean_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ShoukanLabs/OpenNiji-485001_520000 | ---
dataset_info:
features:
- name: image
dtype: image
- name: url
dtype: string
- name: prompt
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 62387737569.534
num_examples: 34999
download_size: 53232911218
dataset_size: 62387737569.534
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "OpenNiji-485001_520000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
varunbel/crossway_ducks | ---
license: mit
arxiv: 2307.01849
task_categories:
- robotics
---
# Crossway Ducks
This is the dataset repo for real world experiments presented in [ICRA'24](https://2024.ieee-icra.org/) paper [Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning](https://arxiv.org/abs/2307.01849).
These datasets are collected in the same format as [robomimic](https://robomimic.github.io/docs/datasets/overview.html).
To train on this dataset (and your own datasets), please modify your config file as follows:
1. Change `dataset_path` to the path of `hdf5` file.
2. Use the following `shape_meta`.
```
shape_meta:
action:
shape:
- 4
obs:
pos:
shape:
- 4
type: low_dim
camera0:
shape:
- 3
- 120
- 160
type: rgb
camera1:
shape:
- 3
- 120
- 160
type: rgb
```
For the training and evaluation code, please visit our [github repo](https://github.com/LostXine/crossway_diffusion).
Watch [🎬 video presentation](https://youtu.be/9deKHueZBuk)!
For questions, please raise a [github issue](https://github.com/LostXine/crossway_diffusion/issues).
|
nikhil-c-r/mllm-dataset | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 107786270
num_examples: 95000
download_size: 64078576
dataset_size: 107786270
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vlsp-2023-vllm/lambada_vi | ---
dataset_info:
features:
- name: text
dtype: string
- name: context
dtype: string
- name: target_word
dtype: string
- name: metadata
struct:
- name: num_sents
dtype: int64
- name: target_word
struct:
- name: appeared_in_prev_sents
dtype: bool
- name: pos_tag
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: word_type
dtype: string
splits:
- name: test
num_bytes: 18460415.77200859
num_examples: 10000
- name: validation
num_bytes: 454126.2279914113
num_examples: 246
download_size: 10704436
dataset_size: 18914542.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
# Lambada (Vietnamese)
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `lambada_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks lambada_vi \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks lambada_vi \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring. |
PatoDonaldo/BRVozes | ---
license: mit
---
|
Stopwolf/ms-marco-v2.1-sr-500k | ---
license: gpl-3.0
---
|
nova-x/jp-legal-corpus | ---
dataset_info:
features:
- name: code
dtype: string
- name: url
dtype: string
- name: law_title
dtype: string
- name: articles
list:
- name: article_content
dtype: string
- name: article_number
dtype: string
- name: article_title
dtype: string
- name: summary
dtype: string
- name: document_type
dtype: 'null'
splits:
- name: train
num_bytes: 1160282630
num_examples: 11484
download_size: 324027952
dataset_size: 1160282630
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "jp-legal-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-cnn_dailymail-3.0.0-01441a-15506143 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
liuyanchen1015/MULTI_VALUE_sst2_remove_det_indefinite | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 30266
num_examples: 206
- name: test
num_bytes: 63725
num_examples: 442
- name: train
num_bytes: 978802
num_examples: 9076
download_size: 628451
dataset_size: 1072793
---
# Dataset Card for "MULTI_VALUE_sst2_remove_det_indefinite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/disks45_nocr_trec-robust-2004 | ---
pretty_name: '`disks45/nocr/trec-robust-2004`'
viewer: false
source_datasets: ['irds/disks45_nocr']
task_categories:
- text-retrieval
---
# Dataset Card for `disks45/nocr/trec-robust-2004`
The `disks45/nocr/trec-robust-2004` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/disks45#disks45/nocr/trec-robust-2004).
# Data
This dataset provides:
- `queries` (i.e., topics); count=250
- `qrels`: (relevance assessments); count=311,410
- For `docs`, use [`irds/disks45_nocr`](https://huggingface.co/datasets/irds/disks45_nocr)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/disks45_nocr_trec-robust-2004', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/disks45_nocr_trec-robust-2004', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@misc{Voorhees1996Disks45,
title = {NIST TREC Disks 4 and 5: Retrieval Test Collections Document Set},
author = {Ellen M. Voorhees},
doi = {10.18434/t47g6m},
year = {1996},
publisher = {National Institute of Standards and Technology}
}
@inproceedings{Voorhees2004Robust,
title={Overview of the TREC 2004 Robust Retrieval Track},
author={Ellen Voorhees},
booktitle={TREC},
year={2004}
}
@inproceedings{Huston2014ACO,
title={A Comparison of Retrieval Models using Term Dependencies},
author={Samuel Huston and W. Bruce Croft},
booktitle={CIKM},
year={2014}
}
```
|
liuyanchen1015/MULTI_VALUE_wnli_fronting_pobj | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 6380
num_examples: 33
- name: test
num_bytes: 26291
num_examples: 91
- name: train
num_bytes: 61321
num_examples: 320
download_size: 40267
dataset_size: 93992
---
# Dataset Card for "MULTI_VALUE_wnli_fronting_pobj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maloyan/wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: wiki_id
dtype: int32
- name: views
dtype: float32
- name: paragraph_id
dtype: int32
- name: langs
dtype: int32
- name: emb
sequence: float32
splits:
- name: train
num_bytes: 72128274660
num_examples: 35167920
download_size: 85877047254
dataset_size: 72128274660
---
# Dataset Card for "wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
meisin123/iban_speech_corpus | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1014986154.58
num_examples: 3132
download_size: 981436514
dataset_size: 1014986154.58
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "iban_speech_corpus"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** The original dataset is found on [Sarah Juan's github link](https://github.com/sarahjuan/iban)
- **Paper:** "Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban"
### Dataset Summary
This Iban speech corpus is used for training of a Automatic Speech Recognition (ASR) model. This dataset contains the audio files (wav files) with its corresponding transcription.
For other resources such as pronunciation dictionary and Iban language model, please refer to the original dataset respository [here](https://github.com/sarahjuan/iban).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
```python
from datasets import load_dataset
dataset = load_dataset("meisin123/iban_speech_corpus", split="train")
```
## Dataset Structure
### Data Instances
```
{'audio': {'path': 'ibf_001_001.wav',
'array': array([ 5.72814941e-01, 5.49011230e-01, -1.82495117e-02, ...,
-2.31628418e-02, -1.26342773e-02, -3.05175781e-05]),
'sampling_rate': 16000},
'transcription': 'pukul sepuluh malam'}
```
### Data Fields
- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate.
- transcription: the transcription of the audio file.
## Dataset Creation
- Iban Data collected by Sarah Samson Juan and Laurent Besacier
### Source Data
The audio files are news data provided by a local radio station in Sarawak, Malaysia.
## Additional Information
### Citation Information
Details on the corpora and the experiments on iban ASR can be found in the following list of publication. The original authors appreciate if you cite them if you intend to publish.
```
@inproceedings{Juan14,
Author = {Sarah Samson Juan and Laurent Besacier and Solange Rossato},
Booktitle = {Proceedings of Workshop for Spoken Language Technology for Under-resourced (SLTU)},
Month = {May},
Title = {Semi-supervised G2P bootstrapping and its application to ASR for a very under-resourced language: Iban},
Year = {2014}}
@inproceedings{Juan2015,
Title = {Using Resources from a closely-Related language to develop ASR for a very under-resourced Language: A case study for Iban},
Author = {Sarah Samson Juan and Laurent Besacier and Benjamin Lecouteux and Mohamed Dyab},
Booktitle = {Proceedings of INTERSPEECH},
Year = {2015},
Address = {Dresden, Germany},
Month = {September}}
```
### Contributions
Thanks to [meisin](https://github.com/meisin) for adding this dataset.
|
Anusha64/BHADataSet | ---
license: mit
---
|
vaibhav1/Mongolian_FakeNews_Comprehendo_dataset | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'FALSE'
'1': MISLEADING
'2': MISSING CONTEXT
'3': 'TRUE'
'4': UNVERIFIABLE
- name: Category
dtype: string
splits:
- name: train
num_bytes: 36303
num_examples: 85
- name: test
num_bytes: 4038
num_examples: 10
download_size: 30011
dataset_size: 40341
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Falah/retro_style_high_angle_shots_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 1814191
num_examples: 10000
download_size: 170897
dataset_size: 1814191
---
# Dataset Card for "retro_style_high_angle_shots_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vasaicrow/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: 'null'
- name: closed_at
dtype: 'null'
- name: comments
dtype: int64
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 18492570
num_examples: 5500
download_size: 3332903
dataset_size: 18492570
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yejin776/autotrain-data-intent | ---
language:
- ko
task_categories:
- text-classification
---
# AutoTrain Dataset for project: intent
## Dataset Description
This dataset has been automatically processed by AutoTrain for project intent.
### Languages
The BCP-47 code for the dataset's language is ko.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\uc878\ub824\uc11c \uc274 \uc218 \uc788\ub294 \uacf3\uc744 \ucc3e\uc544\uc918",
"target": 1
},
{
"text": "\uad50\ud1b5 \uccb4\uc99d\uc774 \uc2ec\ud55c \uc774\uc720\uac00 \ubb50\uc8e0",
"target": 3
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['11', '12', '13', '14', '15'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 182 |
| valid | 48 |
|
huggan/anime-faces | ---
license: cc0-1.0
---
# Dataset Card for anime-faces
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.kaggle.com/soumikrakshit/anime-faces
- **Repository:** https://www.kaggle.com/soumikrakshit/anime-faces
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://github.com/Mckinsey666
### Dataset Summary
This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm in https://github.com/nagadomi/lbpcascade_animeface. All images are resized to 64 * 64 for the sake of convenience. Please also cite the two sources when using this dataset.
Some outliers are still present in the dataset:
Bad cropping results
Some non-human faces.
Feel free to contribute to this dataset by adding images of similar quality or adding image labels.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
Has a data folder with png files inside.
### Data Splits
Only training set
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
---
annotations_creators:
- found
language_creators:
- found
languages:
- unknown
licenses:
- unknown
multilinguality:
- unknown
pretty_name: anime-faces
size_categories:
- unknown
source_datasets:
- original
task_categories:
- image-classification
task_ids: []
--- |
IndonesiaAI/stack-split-0-translated-cleaned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: qid
dtype: string
- name: question
dtype: string
- name: response_j
dtype: string
- name: response_k
dtype: string
splits:
- name: train
num_bytes: 3038797375
num_examples: 1050257
download_size: 940630859
dataset_size: 3038797375
---
# Dataset Card for "stack-split-0-translated-cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-tweet_eval-offensive-93ad2d-30713144950 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- tweet_eval
eval_info:
task: multi_class_classification
model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
metrics: ['bertscore']
dataset_name: tweet_eval
dataset_config: offensive
dataset_split: train
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-2021-124m-offensive
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@fabeelaalirawther@gmail.com](https://huggingface.co/fabeelaalirawther@gmail.com) for evaluating this model. |
mHossain/final_train_v4_test_1060000 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 7384662.0
num_examples: 18000
- name: test
num_bytes: 820518.0
num_examples: 2000
download_size: 3543931
dataset_size: 8205180.0
---
# Dataset Card for "final_train_v4_test_1060000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
graceebc/diffusers_GF_FloodNet | ---
license: mit
---
|
Sifal/Kabyle-French | ---
license: cc
language:
- fr
- kab
--- |
MruganKulkarni/restaurant_conversation | ---
license: mit
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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] |
tyzhu/squad_qa_wrong_title_v5_full_random_permute_1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 4372749.8651079135
num_examples: 2875
- name: validation
num_bytes: 361864
num_examples: 300
download_size: 1218491
dataset_size: 4734613.8651079135
---
# Dataset Card for "squad_qa_wrong_title_v5_full_random_permute_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-m2.0 | ---
pretty_name: Evaluation run of jondurbin/airoboros-l2-7b-gpt4-m2.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-l2-7b-gpt4-m2.0](https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-m2.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-m2.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T20:05:57.910651](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-m2.0/blob/main/results_2023-10-18T20-05-57.910651.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\": 0.16799496644295303,\n\
\ \"em_stderr\": 0.0038286949270672057,\n \"f1\": 0.24476510067114088,\n\
\ \"f1_stderr\": 0.003911929321827723,\n \"acc\": 0.3681417184217313,\n\
\ \"acc_stderr\": 0.009196861647809822\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.16799496644295303,\n \"em_stderr\": 0.0038286949270672057,\n\
\ \"f1\": 0.24476510067114088,\n \"f1_stderr\": 0.003911929321827723\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04094010614101592,\n \
\ \"acc_stderr\": 0.005458076796294343\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6953433307024467,\n \"acc_stderr\": 0.012935646499325302\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-m2.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|arc:challenge|25_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T20_05_57.910651
path:
- '**/details_harness|drop|3_2023-10-18T20-05-57.910651.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T20-05-57.910651.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T20_05_57.910651
path:
- '**/details_harness|gsm8k|5_2023-10-18T20-05-57.910651.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T20-05-57.910651.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hellaswag|10_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T12:14:57.901258.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T12:14:57.901258.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T12:14:57.901258.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T20_05_57.910651
path:
- '**/details_harness|winogrande|5_2023-10-18T20-05-57.910651.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T20-05-57.910651.parquet'
- config_name: results
data_files:
- split: 2023_08_18T12_14_57.901258
path:
- results_2023-08-18T12:14:57.901258.parquet
- split: 2023_10_18T20_05_57.910651
path:
- results_2023-10-18T20-05-57.910651.parquet
- split: latest
path:
- results_2023-10-18T20-05-57.910651.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-l2-7b-gpt4-m2.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-m2.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [jondurbin/airoboros-l2-7b-gpt4-m2.0](https://huggingface.co/jondurbin/airoboros-l2-7b-gpt4-m2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-m2.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T20:05:57.910651](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-l2-7b-gpt4-m2.0/blob/main/results_2023-10-18T20-05-57.910651.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": 0.16799496644295303,
"em_stderr": 0.0038286949270672057,
"f1": 0.24476510067114088,
"f1_stderr": 0.003911929321827723,
"acc": 0.3681417184217313,
"acc_stderr": 0.009196861647809822
},
"harness|drop|3": {
"em": 0.16799496644295303,
"em_stderr": 0.0038286949270672057,
"f1": 0.24476510067114088,
"f1_stderr": 0.003911929321827723
},
"harness|gsm8k|5": {
"acc": 0.04094010614101592,
"acc_stderr": 0.005458076796294343
},
"harness|winogrande|5": {
"acc": 0.6953433307024467,
"acc_stderr": 0.012935646499325302
}
}
```
### 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] |
sam2ai/bengali_alpaca_dolly_67k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 116369973
num_examples: 67017
download_size: 44110061
dataset_size: 116369973
---
# Dataset Card for "bengali_alpaca_dolly_67k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mda/aha | ---
license: gpl-3.0
---
|
JavierLopetegui/chia-ner-french | ---
license: mit
dataset_info:
features:
- name: tokens
sequence: string
- name: annotated_labels
sequence: int64
- name: annotated_labels_max
sequence: int64
- name: file
dtype: string
splits:
- name: train
num_bytes: 4977276
num_examples: 12423
download_size: 952960
dataset_size: 4977276
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- token-classification
language:
- fr
tags:
- medical
pretty_name: Dataset for NER in medical trials eligibility criteria in french.
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
### 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]
- Javier Alejandro Lopetegui González: [github](https://github.com/jlopetegui98)
- Carlos Cuevas Villarmín: [github](https://github.com/cuevascarlos)
- José Felipe Espinosa Orjuela: [github](https://github.com/Pipe1213) |
chrisgg1/keywords_verbinden | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype:
class_label:
names:
'0': silence
'1': unknown
'2': verbinden
splits:
- name: train
num_bytes: 2065979538.822
num_examples: 46449
download_size: 1388817984
dataset_size: 2065979538.822
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Samhita/flyte-slack-data-new | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2391980
num_examples: 3708
download_size: 1274797
dataset_size: 2391980
---
# Dataset Card for "flyte-slack-data-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
crumb/Wizard-EvolInstruct70k-k64 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 131460545
num_examples: 70000
download_size: 69258716
dataset_size: 131460545
---
# Dataset Card for "Wizard-EvolInstruct70k-k64"
`centers.pt` in the files is a 64x384 matrix including the centers of each cluster. I use `sentence-transformers/all-MiniLM-L6-v2` to encode text.
```python
import torch
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = torch.tensor(model.encode(sentences))
centers = torch.load("centers.pt")
# mse based cluster choice
clusters = (embeddings - centers).pow(2).mean(1).argmin().tolist()
# or you could load the sklearn kmeans classifier
# todo: documentation for that
# todo: figure out how to do that
# todo: cant you push sklearn classifiers to the hub with some weird code introduced earlier this year or something
``` |
Crystalcareai/MoD-Alpaca | ---
license: apache-2.0
---
|
abacusai/SystemChat | ---
license: apache-2.0
---
This dataset by AbacusAI was crafted by Eric Hartford
This is a synthetic dataset, generated mainly with Mistral-Medium and [dolphin-2.7-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.7-mixtral-8x7b)
The purpose of this dataset is to train the model to respect the System Prompt throughout the entire conversation, no matter how unconventional the system prompt might be.
This dataset is under continued development - my intent is to grow it to 100k conversations.
But, for now, it is good enough to start using. |
lmms-lab/TextCaps | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: image
dtype: image
- name: image_id
dtype: string
- name: image_classes
sequence: string
- name: flickr_original_url
dtype: string
- name: flickr_300k_url
dtype: string
- name: image_width
dtype: int64
- name: image_height
dtype: int64
- name: set_name
dtype: string
- name: image_name
dtype: string
- name: image_path
dtype: string
- name: caption_id
sequence: int64
- name: caption_str
sequence: string
- name: reference_strs
sequence: string
splits:
- name: train
num_bytes: 6201208209.0
num_examples: 21953
- name: val
num_bytes: 919878416.0
num_examples: 3166
- name: test
num_bytes: 959971875.0
num_examples: 3289
download_size: 8064165124
dataset_size: 8081058500.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [TextCaps](https://textvqa.org/textcaps/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@inproceedings{sidorov2019textcaps,
title={TextCaps: a Dataset for Image Captioningwith Reading Comprehension},
author={Sidorov, Oleksii and Hu, Ronghang and Rohrbach, Marcus and Singh, Amanpreet},
journal={European Conference on Computer Vision},
year={2020}
}
```
|
Madhubala/Arun | ---
license: apache-2.0
---
|
Cauthess/batman | ---
license: openrail
---
|
wilsonorozco/naomi | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_mnli_remove_det_definite | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 1303457
num_examples: 5963
- name: dev_mismatched
num_bytes: 1357403
num_examples: 5989
- name: test_matched
num_bytes: 1325070
num_examples: 6083
- name: test_mismatched
num_bytes: 1383081
num_examples: 6107
- name: train
num_bytes: 52818902
num_examples: 240340
download_size: 38882553
dataset_size: 58187913
---
# Dataset Card for "MULTI_VALUE_mnli_remove_det_definite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NoahMartinez/RAVDESS_Bai | ---
license: apache-2.0
---
|
CyberHarem/javelin_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of javelin/ジャベリン/标枪 (Azur Lane)
This is the dataset of javelin/ジャベリン/标枪 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `purple_hair, hair_ornament, ponytail, bangs, breasts, ribbon, hair_between_eyes, green_eyes, hair_ribbon, crown, mini_crown, blue_eyes, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 716.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/javelin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 398.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/javelin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1257 | 882.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/javelin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 628.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/javelin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1257 | 1.25 GiB | [Download](https://huggingface.co/datasets/CyberHarem/javelin_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/javelin_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, black_ribbon, blush, looking_at_viewer, open_mouth, plaid_skirt, pleated_skirt, purple_skirt, single_glove, solo, white_camisole, white_gloves, bracelet, simple_background, white_background, :d, bare_shoulders, cleavage, high_ponytail, shirt, small_breasts, tilted_headwear |
| 1 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, :d, simple_background, white_background, blush, gloves, sleeveless, white_dress, cross_hair_ornament, sailor_collar, pink_neckerchief, black_ribbon, bracelet, collarbone, medium_hair |
| 2 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, wedding_dress, white_dress, bare_shoulders, bridal_veil, open_mouth, smile, strapless_dress, flower, blush, choker, elbow_gloves, petals, bride, cleavage, collarbone, tiara, white_gloves |
| 3 | 5 |  |  |  |  |  | 1girl, blue_bikini, hair_flower, looking_at_viewer, navel, open_mouth, small_breasts, solo, bare_shoulders, blush, red_flower, :d, bracelet, halterneck, armpits, arms_up, collarbone, cross_hair_ornament, feet_out_of_frame, frilled_bikini, simple_background, water, white_background |
| 4 | 9 |  |  |  |  |  | blue_bikini, bracelet, navel, open_mouth, bare_shoulders, blue_sky, day, looking_at_viewer, outdoors, 1girl, ocean, :d, collarbone, hair_flower, halterneck, innertube, sidelocks, solo, beach, blush, cross_hair_ornament, frilled_bikini, holding, small_breasts, cloudy_sky, cowboy_shot, standing, umbrella |
| 5 | 6 |  |  |  |  |  | 1girl, blue_shirt, blush, long_hair, looking_at_viewer, plaid_skirt, pleated_skirt, school_uniform, short_sleeves, solo, sweater_vest, tilted_headwear, collared_shirt, plaid_bow, purple_skirt, :d, black_socks, blue_skirt, bracelet, open_mouth, white_shirt, alternate_hairstyle, blue_bow, hair_down, indoors, sitting, white_background, window |
| 6 | 9 |  |  |  |  |  | 1girl, blue_shirt, blush, collared_shirt, long_hair, looking_at_viewer, plaid_skirt, short_sleeves, solo, sweater_vest, pleated_skirt, cross_hair_ornament, plaid_bow, purple_skirt, school_uniform, simple_background, smile, alternate_hairstyle, white_background, blue_skirt, closed_mouth, hair_down, purple_bowtie, sitting |
| 7 | 6 |  |  |  |  |  | 1girl, holding_weapon, looking_at_viewer, solo, white_thighhighs, dress, retrofit_(azur_lane), sleeveless, blush, chain, polearm, sailor_collar, smile, bracelet, skirt, zettai_ryouiki |
| 8 | 7 |  |  |  |  |  | 1girl, blue_shirt, blue_skirt, looking_at_viewer, navel, open_mouth, solo, wrist_cuffs, :d, bare_shoulders, midriff, pleated_skirt, sleeveless_shirt, white_sailor_collar, armpits, simple_background, yellow_bow, arm_up, blue_serafuku, blush, miniskirt, white_background, white_socks, alternate_costume, collarbone, cowboy_shot, crop_top_overhang, full_body, loafers, sidelocks, stomach, white_ribbon |
| 9 | 14 |  |  |  |  |  | midriff, open_mouth, plaid_skirt, purple_skirt, bare_shoulders, navel, white_shirt, crop_top, hair_bow, looking_at_viewer, pantyhose, 1girl, blush, idol, sleeveless_shirt, solo, star_hair_ornament, :d, plaid_bow, pleated_skirt, purple_bow, suspenders, long_hair, sidelocks, standing, miniskirt, wrist_cuffs, headset, high_ponytail, aiguillette, arm_up, collarbone, cowboy_shot, gloves, stage_lights |
| 10 | 5 |  |  |  |  |  | 2girls, bare_shoulders, collarbone, cross_hair_ornament, open_mouth, :d, blush, solo_focus, bikini, cleavage, looking_at_viewer, long_hair, sidelocks, sitting, teeth |
| 11 | 9 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, open_mouth, penis, blush, solo_focus, bar_censor, sweat, sex, spread_legs, vaginal, looking_at_viewer, cross_hair_ornament, cum_in_pussy, jewelry, missionary, on_back, short_hair, single_glove, white_gloves |
| 12 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, looking_at_viewer, sideboob, sitting, backless_dress, bare_back, indoors, open_mouth, profile, :d, from_behind, looking_back, median_furrow, back_focus, black_footwear, cross_hair_ornament, knee_boots, official_alternate_costume, solo_focus |
| 13 | 8 |  |  |  |  |  | 1girl, short_shorts, solo, striped_thighhighs, blush, looking_at_viewer, navel, pillow, camisole, frilled_shorts, ribbon_trim, underboob, bare_shoulders, lying, open_mouth, sleep_mask, :d, arm_up, collarbone, no_shoes, official_alternate_costume, pajamas, purple_ribbon, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_ribbon | blush | looking_at_viewer | open_mouth | plaid_skirt | pleated_skirt | purple_skirt | single_glove | solo | white_camisole | white_gloves | bracelet | simple_background | white_background | :d | bare_shoulders | cleavage | high_ponytail | shirt | small_breasts | tilted_headwear | gloves | sleeveless | white_dress | cross_hair_ornament | sailor_collar | pink_neckerchief | collarbone | medium_hair | wedding_dress | bridal_veil | smile | strapless_dress | flower | choker | elbow_gloves | petals | bride | tiara | blue_bikini | hair_flower | navel | red_flower | halterneck | armpits | arms_up | feet_out_of_frame | frilled_bikini | water | blue_sky | day | outdoors | ocean | innertube | sidelocks | beach | holding | cloudy_sky | cowboy_shot | standing | umbrella | blue_shirt | long_hair | school_uniform | short_sleeves | sweater_vest | collared_shirt | plaid_bow | black_socks | blue_skirt | white_shirt | alternate_hairstyle | blue_bow | hair_down | indoors | sitting | window | closed_mouth | purple_bowtie | holding_weapon | white_thighhighs | dress | retrofit_(azur_lane) | chain | polearm | skirt | zettai_ryouiki | wrist_cuffs | midriff | sleeveless_shirt | white_sailor_collar | yellow_bow | arm_up | blue_serafuku | miniskirt | white_socks | alternate_costume | crop_top_overhang | full_body | loafers | stomach | white_ribbon | crop_top | hair_bow | pantyhose | idol | star_hair_ornament | purple_bow | suspenders | headset | aiguillette | stage_lights | 2girls | solo_focus | bikini | teeth | 1boy | hetero | nipples | penis | bar_censor | sweat | sex | spread_legs | vaginal | cum_in_pussy | jewelry | missionary | on_back | short_hair | black_dress | sideboob | backless_dress | bare_back | profile | from_behind | looking_back | median_furrow | back_focus | black_footwear | knee_boots | official_alternate_costume | short_shorts | striped_thighhighs | pillow | camisole | frilled_shorts | ribbon_trim | underboob | lying | sleep_mask | no_shoes | pajamas | purple_ribbon |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:--------|:--------------------|:-------------|:--------------|:----------------|:---------------|:---------------|:-------|:-----------------|:---------------|:-----------|:--------------------|:-------------------|:-----|:-----------------|:-----------|:----------------|:--------|:----------------|:------------------|:---------|:-------------|:--------------|:----------------------|:----------------|:-------------------|:-------------|:--------------|:----------------|:--------------|:--------|:------------------|:---------|:---------|:---------------|:---------|:--------|:--------|:--------------|:--------------|:--------|:-------------|:-------------|:----------|:----------|:--------------------|:-----------------|:--------|:-----------|:------|:-----------|:--------|:------------|:------------|:--------|:----------|:-------------|:--------------|:-----------|:-----------|:-------------|:------------|:-----------------|:----------------|:---------------|:-----------------|:------------|:--------------|:-------------|:--------------|:----------------------|:-----------|:------------|:----------|:----------|:---------|:---------------|:----------------|:-----------------|:-------------------|:--------|:-----------------------|:--------|:----------|:--------|:-----------------|:--------------|:----------|:-------------------|:----------------------|:-------------|:---------|:----------------|:------------|:--------------|:--------------------|:--------------------|:------------|:----------|:----------|:---------------|:-----------|:-----------|:------------|:-------|:---------------------|:-------------|:-------------|:----------|:--------------|:---------------|:---------|:-------------|:---------|:--------|:-------|:---------|:----------|:--------|:-------------|:--------|:------|:--------------|:----------|:---------------|:----------|:-------------|:----------|:-------------|:--------------|:-----------|:-----------------|:------------|:----------|:--------------|:---------------|:----------------|:-------------|:-----------------|:-------------|:-----------------------------|:---------------|:---------------------|:---------|:-----------|:-----------------|:--------------|:------------|:--------|:-------------|:-----------|:----------|:----------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | X | X | | | | | X | | | X | X | X | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | X | X | | | | | X | | X | | | | | X | X | | | | | | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | X | X | | | | | X | | | X | X | X | X | X | | | | X | | | | | X | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | X | X | X | | | | | X | | | X | | | X | X | | | | X | | | | | X | | | X | | | | | | | | | | | | X | X | X | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | X | X | X | X | X | X | | X | | | X | | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | X | X | | X | X | X | | X | | | | X | X | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | X | | X | | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | X | X | | | | | | X | | | X | | | | | | | | | | | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | X | X | X | | X | | | X | | | | X | X | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | X | | | | | | | | | | X | | | | X | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | | X | X | X | X | X | X | | X | | | | | | X | X | | X | | | | X | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | X | X | | | X | | | | | X | | | X | | | | | | | | | | | | | | | | | X | X | X | | | X | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | | | X | X | X | | | | | | | | | | | X | X | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 9 |  |  |  |  |  | X | | X | X | X | | | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 12 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 13 | 8 |  |  |  |  |  | X | | X | X | X | | | | | X | | | | X | | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-39000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1144069
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MiyazonoKaori137/Anime-Audio | ---
license: apache-2.0
language:
- ja
--- |
Lkhagvasurenam/STTmn | ---
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 7998858.1
num_examples: 9
- name: test
num_bytes: 1145369.0
num_examples: 1
download_size: 9066089
dataset_size: 9144227.1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_JCX-kcuf__Llama-2-7b-hf-gpt-4-80k | ---
pretty_name: Evaluation run of JCX-kcuf/Llama-2-7b-hf-gpt-4-80k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [JCX-kcuf/Llama-2-7b-hf-gpt-4-80k](https://huggingface.co/JCX-kcuf/Llama-2-7b-hf-gpt-4-80k)\
\ 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_JCX-kcuf__Llama-2-7b-hf-gpt-4-80k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-11T18:19:30.657181](https://huggingface.co/datasets/open-llm-leaderboard/details_JCX-kcuf__Llama-2-7b-hf-gpt-4-80k/blob/main/results_2024-03-11T18-19-30.657181.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\": 0.4691427623076365,\n\
\ \"acc_stderr\": 0.03458340541870251,\n \"acc_norm\": 0.4741335558049342,\n\
\ \"acc_norm_stderr\": 0.035370693026626564,\n \"mc1\": 0.3390452876376989,\n\
\ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.4862513962984137,\n\
\ \"mc2_stderr\": 0.014967511131756355\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.01460779491401305,\n\
\ \"acc_norm\": 0.5554607508532423,\n \"acc_norm_stderr\": 0.01452122640562708\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5802628958374826,\n\
\ \"acc_stderr\": 0.0049250721597238365,\n \"acc_norm\": 0.7726548496315475,\n\
\ \"acc_norm_stderr\": 0.004182607685205692\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\": 0.3925925925925926,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.40789473684210525,\n \"acc_stderr\": 0.03999309712777471,\n\
\ \"acc_norm\": 0.40789473684210525,\n \"acc_norm_stderr\": 0.03999309712777471\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.49056603773584906,\n \"acc_stderr\": 0.0307673947078081,\n\
\ \"acc_norm\": 0.49056603773584906,\n \"acc_norm_stderr\": 0.0307673947078081\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04155319955593146,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04155319955593146\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\
: 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n\
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.44508670520231214,\n\
\ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.44508670520231214,\n\
\ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4085106382978723,\n \"acc_stderr\": 0.03213418026701576,\n\
\ \"acc_norm\": 0.4085106382978723,\n \"acc_norm_stderr\": 0.03213418026701576\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.043391383225798615,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.043391383225798615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2962962962962963,\n \"acc_stderr\": 0.023517294335963283,\n \"\
acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.023517294335963283\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.5161290322580645,\n \"acc_stderr\": 0.028429203176724555,\n \"\
acc_norm\": 0.5161290322580645,\n \"acc_norm_stderr\": 0.028429203176724555\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3103448275862069,\n \"acc_stderr\": 0.032550867699701024,\n \"\
acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.032550867699701024\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\
: 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.593939393939394,\n \"acc_stderr\": 0.03834816355401181,\n\
\ \"acc_norm\": 0.593939393939394,\n \"acc_norm_stderr\": 0.03834816355401181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5505050505050505,\n \"acc_stderr\": 0.0354413249194797,\n \"acc_norm\"\
: 0.5505050505050505,\n \"acc_norm_stderr\": 0.0354413249194797\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.6321243523316062,\n \"acc_stderr\": 0.034801756684660366,\n\
\ \"acc_norm\": 0.6321243523316062,\n \"acc_norm_stderr\": 0.034801756684660366\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.025294608023986472,\n\
\ \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.025294608023986472\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844072,\n \
\ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844072\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.032252942323996406,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.032252942323996406\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6146788990825688,\n \"acc_stderr\": 0.020865850852794125,\n \"\
acc_norm\": 0.6146788990825688,\n \"acc_norm_stderr\": 0.020865850852794125\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3101851851851852,\n \"acc_stderr\": 0.03154696285656628,\n \"\
acc_norm\": 0.3101851851851852,\n \"acc_norm_stderr\": 0.03154696285656628\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5196078431372549,\n \"acc_stderr\": 0.03506612560524866,\n \"\
acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.03506612560524866\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.620253164556962,\n \"acc_stderr\": 0.031591887529658504,\n \
\ \"acc_norm\": 0.620253164556962,\n \"acc_norm_stderr\": 0.031591887529658504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5515695067264574,\n\
\ \"acc_stderr\": 0.033378837362550984,\n \"acc_norm\": 0.5515695067264574,\n\
\ \"acc_norm_stderr\": 0.033378837362550984\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5572519083969466,\n \"acc_stderr\": 0.043564472026650695,\n\
\ \"acc_norm\": 0.5572519083969466,\n \"acc_norm_stderr\": 0.043564472026650695\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"\
acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5462962962962963,\n\
\ \"acc_stderr\": 0.04812917324536823,\n \"acc_norm\": 0.5462962962962963,\n\
\ \"acc_norm_stderr\": 0.04812917324536823\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\
\ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.5825242718446602,\n \"acc_stderr\": 0.048828405482122375,\n\
\ \"acc_norm\": 0.5825242718446602,\n \"acc_norm_stderr\": 0.048828405482122375\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7136752136752137,\n\
\ \"acc_stderr\": 0.029614323690456648,\n \"acc_norm\": 0.7136752136752137,\n\
\ \"acc_norm_stderr\": 0.029614323690456648\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.017268607560005794,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.017268607560005794\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.4913294797687861,\n \"acc_stderr\": 0.0269150473553698,\n\
\ \"acc_norm\": 0.4913294797687861,\n \"acc_norm_stderr\": 0.0269150473553698\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02861462475280544,\n\
\ \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02861462475280544\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5627009646302251,\n\
\ \"acc_stderr\": 0.028173917761762906,\n \"acc_norm\": 0.5627009646302251,\n\
\ \"acc_norm_stderr\": 0.028173917761762906\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.027801656212323667,\n\
\ \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.027801656212323667\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.36524822695035464,\n \"acc_stderr\": 0.028723863853281274,\n \
\ \"acc_norm\": 0.36524822695035464,\n \"acc_norm_stderr\": 0.028723863853281274\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35267275097783574,\n\
\ \"acc_stderr\": 0.012203286846053886,\n \"acc_norm\": 0.35267275097783574,\n\
\ \"acc_norm_stderr\": 0.012203286846053886\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\
\ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4362745098039216,\n \"acc_stderr\": 0.020062874243539128,\n \
\ \"acc_norm\": 0.4362745098039216,\n \"acc_norm_stderr\": 0.020062874243539128\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5454545454545454,\n\
\ \"acc_stderr\": 0.04769300568972744,\n \"acc_norm\": 0.5454545454545454,\n\
\ \"acc_norm_stderr\": 0.04769300568972744\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\
\ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6716417910447762,\n\
\ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\
\ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.41566265060240964,\n\
\ \"acc_stderr\": 0.038367221765980515,\n \"acc_norm\": 0.41566265060240964,\n\
\ \"acc_norm_stderr\": 0.038367221765980515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488905,\n\
\ \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488905\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3390452876376989,\n\
\ \"mc1_stderr\": 0.016571797910626615,\n \"mc2\": 0.4862513962984137,\n\
\ \"mc2_stderr\": 0.014967511131756355\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14025777103866566,\n \
\ \"acc_stderr\": 0.009565108281428663\n }\n}\n```"
repo_url: https://huggingface.co/JCX-kcuf/Llama-2-7b-hf-gpt-4-80k
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_11T18_19_30.657181
path:
- '**/details_harness|arc:challenge|25_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|gsm8k|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hellaswag|10_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T18-19-30.657181.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T18-19-30.657181.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- '**/details_harness|winogrande|5_2024-03-11T18-19-30.657181.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-11T18-19-30.657181.parquet'
- config_name: results
data_files:
- split: 2024_03_11T18_19_30.657181
path:
- results_2024-03-11T18-19-30.657181.parquet
- split: latest
path:
- results_2024-03-11T18-19-30.657181.parquet
---
# Dataset Card for Evaluation run of JCX-kcuf/Llama-2-7b-hf-gpt-4-80k
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [JCX-kcuf/Llama-2-7b-hf-gpt-4-80k](https://huggingface.co/JCX-kcuf/Llama-2-7b-hf-gpt-4-80k) 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_JCX-kcuf__Llama-2-7b-hf-gpt-4-80k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-11T18:19:30.657181](https://huggingface.co/datasets/open-llm-leaderboard/details_JCX-kcuf__Llama-2-7b-hf-gpt-4-80k/blob/main/results_2024-03-11T18-19-30.657181.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": 0.4691427623076365,
"acc_stderr": 0.03458340541870251,
"acc_norm": 0.4741335558049342,
"acc_norm_stderr": 0.035370693026626564,
"mc1": 0.3390452876376989,
"mc1_stderr": 0.016571797910626615,
"mc2": 0.4862513962984137,
"mc2_stderr": 0.014967511131756355
},
"harness|arc:challenge|25": {
"acc": 0.5110921501706485,
"acc_stderr": 0.01460779491401305,
"acc_norm": 0.5554607508532423,
"acc_norm_stderr": 0.01452122640562708
},
"harness|hellaswag|10": {
"acc": 0.5802628958374826,
"acc_stderr": 0.0049250721597238365,
"acc_norm": 0.7726548496315475,
"acc_norm_stderr": 0.004182607685205692
},
"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": 0.3925925925925926,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.40789473684210525,
"acc_stderr": 0.03999309712777471,
"acc_norm": 0.40789473684210525,
"acc_norm_stderr": 0.03999309712777471
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.49056603773584906,
"acc_stderr": 0.0307673947078081,
"acc_norm": 0.49056603773584906,
"acc_norm_stderr": 0.0307673947078081
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04155319955593146,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04155319955593146
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.44508670520231214,
"acc_stderr": 0.03789401760283647,
"acc_norm": 0.44508670520231214,
"acc_norm_stderr": 0.03789401760283647
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4085106382978723,
"acc_stderr": 0.03213418026701576,
"acc_norm": 0.4085106382978723,
"acc_norm_stderr": 0.03213418026701576
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.30701754385964913,
"acc_stderr": 0.043391383225798615,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.043391383225798615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.45517241379310347,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.45517241379310347,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.023517294335963283,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.023517294335963283
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04216370213557835,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04216370213557835
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5161290322580645,
"acc_stderr": 0.028429203176724555,
"acc_norm": 0.5161290322580645,
"acc_norm_stderr": 0.028429203176724555
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3103448275862069,
"acc_stderr": 0.032550867699701024,
"acc_norm": 0.3103448275862069,
"acc_norm_stderr": 0.032550867699701024
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.593939393939394,
"acc_stderr": 0.03834816355401181,
"acc_norm": 0.593939393939394,
"acc_norm_stderr": 0.03834816355401181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5505050505050505,
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"acc_norm": 0.5505050505050505,
"acc_norm_stderr": 0.0354413249194797
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6321243523316062,
"acc_stderr": 0.034801756684660366,
"acc_norm": 0.6321243523316062,
"acc_norm_stderr": 0.034801756684660366
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.025294608023986472,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.025294608023986472
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25555555555555554,
"acc_stderr": 0.026593939101844072,
"acc_norm": 0.25555555555555554,
"acc_norm_stderr": 0.026593939101844072
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.032252942323996406,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.032252942323996406
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.038227469376587525,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.038227469376587525
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6146788990825688,
"acc_stderr": 0.020865850852794125,
"acc_norm": 0.6146788990825688,
"acc_norm_stderr": 0.020865850852794125
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3101851851851852,
"acc_stderr": 0.03154696285656628,
"acc_norm": 0.3101851851851852,
"acc_norm_stderr": 0.03154696285656628
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.03506612560524866,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.03506612560524866
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.620253164556962,
"acc_stderr": 0.031591887529658504,
"acc_norm": 0.620253164556962,
"acc_norm_stderr": 0.031591887529658504
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5515695067264574,
"acc_stderr": 0.033378837362550984,
"acc_norm": 0.5515695067264574,
"acc_norm_stderr": 0.033378837362550984
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5572519083969466,
"acc_stderr": 0.043564472026650695,
"acc_norm": 0.5572519083969466,
"acc_norm_stderr": 0.043564472026650695
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6033057851239669,
"acc_stderr": 0.044658697805310094,
"acc_norm": 0.6033057851239669,
"acc_norm_stderr": 0.044658697805310094
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5462962962962963,
"acc_stderr": 0.04812917324536823,
"acc_norm": 0.5462962962962963,
"acc_norm_stderr": 0.04812917324536823
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5398773006134969,
"acc_stderr": 0.03915857291436971,
"acc_norm": 0.5398773006134969,
"acc_norm_stderr": 0.03915857291436971
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.375,
"acc_stderr": 0.04595091388086298,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04595091388086298
},
"harness|hendrycksTest-management|5": {
"acc": 0.5825242718446602,
"acc_stderr": 0.048828405482122375,
"acc_norm": 0.5825242718446602,
"acc_norm_stderr": 0.048828405482122375
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7136752136752137,
"acc_stderr": 0.029614323690456648,
"acc_norm": 0.7136752136752137,
"acc_norm_stderr": 0.029614323690456648
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.017268607560005794,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.017268607560005794
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.4913294797687861,
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"acc_norm": 0.4913294797687861,
"acc_norm_stderr": 0.0269150473553698
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23798882681564246,
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"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5163398692810458,
"acc_stderr": 0.02861462475280544,
"acc_norm": 0.5163398692810458,
"acc_norm_stderr": 0.02861462475280544
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5627009646302251,
"acc_stderr": 0.028173917761762906,
"acc_norm": 0.5627009646302251,
"acc_norm_stderr": 0.028173917761762906
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.027801656212323667,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.027801656212323667
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.36524822695035464,
"acc_stderr": 0.028723863853281274,
"acc_norm": 0.36524822695035464,
"acc_norm_stderr": 0.028723863853281274
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.35267275097783574,
"acc_stderr": 0.012203286846053886,
"acc_norm": 0.35267275097783574,
"acc_norm_stderr": 0.012203286846053886
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5183823529411765,
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"acc_norm": 0.5183823529411765,
"acc_norm_stderr": 0.030352303395351964
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.4362745098039216,
"acc_norm_stderr": 0.020062874243539128
},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.5454545454545454,
"acc_norm_stderr": 0.04769300568972744
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5183673469387755,
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"acc_norm": 0.5183673469387755,
"acc_norm_stderr": 0.03198761546763127
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6716417910447762,
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"acc_norm": 0.6716417910447762,
"acc_norm_stderr": 0.033206858897443244
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.61,
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"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-virology|5": {
"acc": 0.41566265060240964,
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"acc_norm": 0.41566265060240964,
"acc_norm_stderr": 0.038367221765980515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6608187134502924,
"acc_stderr": 0.03631053496488905,
"acc_norm": 0.6608187134502924,
"acc_norm_stderr": 0.03631053496488905
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3390452876376989,
"mc1_stderr": 0.016571797910626615,
"mc2": 0.4862513962984137,
"mc2_stderr": 0.014967511131756355
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
},
"harness|gsm8k|5": {
"acc": 0.14025777103866566,
"acc_stderr": 0.009565108281428663
}
}
```
## 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. -->
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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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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
gsstein/75-percent-human-dataset-llama-og | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: generated
dtype: bool
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 86033257
num_examples: 15326
- name: test
num_bytes: 3055340
num_examples: 576
- name: validation
num_bytes: 3252533
num_examples: 576
download_size: 57122017
dataset_size: 92341130
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
CyberHarem/sonia_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sonia (Pokémon)
This is the dataset of sonia (Pokémon), containing 500 images and their tags.
The core tags of this character are `orange_hair, long_hair, side_ponytail, hair_ornament, heart_hair_ornament, breasts, green_eyes, eyewear_on_head, sunglasses, large_breasts, eyelashes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 578.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 337.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1250 | 731.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 514.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1250 | 1.00 GiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_pokemon/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/sonia_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, aqua_eyes, collarbone, heart, long_sleeves, looking_at_viewer, nail_polish, smile, solo, strap_between_breasts, brown_coat, open_mouth, ribbed_shirt, simple_background, teeth, white_background, aqua_nails, bag, blush, cleavage, green_nails, hand_in_pocket, holding, pants, tongue, trench_coat |
| 1 | 5 |  |  |  |  |  | 1girl, :d, brown_coat, collarbone, green_nails, green_shirt, hand_up, heart, looking_at_viewer, nail_polish, open_mouth, ribbed_shirt, solo, upper_body, blush, long_sleeves, trench_coat, simple_background, strap_between_breasts |
| 2 | 8 |  |  |  |  |  | 1girl, brown_coat, buttons, green_footwear, green_shirt, handbag, heart, long_sleeves, nail_polish, ribbed_shirt, green_nails, high_heel_boots, open_mouth, pants, solo, full_body, hand_up, :d, looking_at_viewer, standing_on_one_leg, tongue, collarbone, strap_between_breasts, upper_teeth_only |
| 3 | 5 |  |  |  |  |  | 1girl, aqua_eyes, brown_coat, collarbone, hand_up, heart, long_sleeves, looking_at_viewer, ribbed_shirt, solo, blush, green_shirt, pants, buttons, cleavage, strap_between_breasts, teeth, waving, :d, bag, hand_in_pocket, open_mouth |
| 4 | 16 |  |  |  |  |  | 1girl, heart, nipples, collarbone, looking_at_viewer, navel, solo, blush, completely_nude, pussy, smile, aqua_eyes, bangs, thighs, closed_mouth, mosaic_censoring, sitting |
| 5 | 12 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, heart, nipples, open_mouth, penis, collarbone, looking_at_viewer, pussy, sex, vaginal, navel, cowgirl_position, spread_legs, sweat, completely_nude, girl_on_top, tongue, aqua_eyes, mosaic_censoring, smile, uncensored |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, :>=, blush, erection, fellatio, heart, hetero, looking_at_viewer, pov, saliva, solo_focus, uncensored, veiny_penis, blue_eyes, fingernails, long_sleeves, male_pubic_hair, lips, nail_polish, aqua_eyes, half-closed_eyes |
| 7 | 8 |  |  |  |  |  | 1girl, dress, looking_at_viewer, mini_crown, hair_down, heart, official_alternate_costume, solo, elbow_gloves, smile, alternate_hairstyle, upper_body, black_choker, black_gloves, closed_mouth, collarbone, pantyhose, pendant_choker |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | aqua_eyes | collarbone | heart | long_sleeves | looking_at_viewer | nail_polish | smile | solo | strap_between_breasts | brown_coat | open_mouth | ribbed_shirt | simple_background | teeth | white_background | aqua_nails | bag | blush | cleavage | green_nails | hand_in_pocket | holding | pants | tongue | trench_coat | :d | green_shirt | hand_up | upper_body | buttons | green_footwear | handbag | high_heel_boots | full_body | standing_on_one_leg | upper_teeth_only | waving | nipples | navel | completely_nude | pussy | bangs | thighs | closed_mouth | mosaic_censoring | sitting | hetero | solo_focus | 1boy | penis | sex | vaginal | cowgirl_position | spread_legs | sweat | girl_on_top | uncensored | :>= | erection | fellatio | pov | saliva | veiny_penis | blue_eyes | fingernails | male_pubic_hair | lips | half-closed_eyes | dress | mini_crown | hair_down | official_alternate_costume | elbow_gloves | alternate_hairstyle | black_choker | black_gloves | pantyhose | pendant_choker |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------------|:--------|:---------------|:--------------------|:--------------|:--------|:-------|:------------------------|:-------------|:-------------|:---------------|:--------------------|:--------|:-------------------|:-------------|:------|:--------|:-----------|:--------------|:-----------------|:----------|:--------|:---------|:--------------|:-----|:--------------|:----------|:-------------|:----------|:-----------------|:----------|:------------------|:------------|:----------------------|:-------------------|:---------|:----------|:--------|:------------------|:--------|:--------|:---------|:---------------|:-------------------|:----------|:---------|:-------------|:-------|:--------|:------|:----------|:-------------------|:--------------|:--------|:--------------|:-------------|:------|:-----------|:-----------|:------|:---------|:--------------|:------------|:--------------|:------------------|:-------|:-------------------|:--------|:-------------|:------------|:-----------------------------|:---------------|:----------------------|:---------------|:---------------|:------------|:-----------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | X | X | X | | X | X | X | X | X | X | | | | | X | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | X | X | X | X | | X | X | X | X | X | | | | | | | | X | | | X | X | | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | X | X | X | X | | | X | X | X | X | X | | X | | | X | X | X | | X | | X | | | X | X | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | X | X | X | X | | X | | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | X | X | X | X | | X | | X | | | | X | | | | | | | X | | | | | | X | | | | | | | | | | | | | | X | X | X | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | | X | X | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
ztla/M4singer | ---
license: cc-by-nc-sa-4.0
---
|
yardeny/tokenized_t5_context_len_64 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 10163799114
num_examples: 80462898
download_size: 3657002292
dataset_size: 10163799114
---
# Dataset Card for "tokenized_t5_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_TheBloke__Nous-Hermes-13B-SuperHOT-8K-fp16 | ---
pretty_name: Evaluation run of TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16)\
\ 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_TheBloke__Nous-Hermes-13B-SuperHOT-8K-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T21:24:49.496203](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Nous-Hermes-13B-SuperHOT-8K-fp16/blob/main/results_2023-10-22T21-24-49.496203.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\": 0.24779781879194632,\n\
\ \"em_stderr\": 0.004421358038007316,\n \"f1\": 0.3203208892617463,\n\
\ \"f1_stderr\": 0.004418252169927022,\n \"acc\": 0.3825450746272229,\n\
\ \"acc_stderr\": 0.007568348592873263\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.24779781879194632,\n \"em_stderr\": 0.004421358038007316,\n\
\ \"f1\": 0.3203208892617463,\n \"f1_stderr\": 0.004418252169927022\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \
\ \"acc_stderr\": 0.003015294242890953\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7529597474348856,\n \"acc_stderr\": 0.012121402942855573\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|arc:challenge|25_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T21_24_49.496203
path:
- '**/details_harness|drop|3_2023-10-22T21-24-49.496203.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T21-24-49.496203.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T21_24_49.496203
path:
- '**/details_harness|gsm8k|5_2023-10-22T21-24-49.496203.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T21-24-49.496203.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hellaswag|10_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T13:07:54.585648.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T13:07:54.585648.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T13:07:54.585648.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T21_24_49.496203
path:
- '**/details_harness|winogrande|5_2023-10-22T21-24-49.496203.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T21-24-49.496203.parquet'
- config_name: results
data_files:
- split: 2023_08_01T13_07_54.585648
path:
- results_2023-08-01T13:07:54.585648.parquet
- split: 2023_10_22T21_24_49.496203
path:
- results_2023-10-22T21-24-49.496203.parquet
- split: latest
path:
- results_2023-10-22T21-24-49.496203.parquet
---
# Dataset Card for Evaluation run of TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16
- **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 [TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16) 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_TheBloke__Nous-Hermes-13B-SuperHOT-8K-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T21:24:49.496203](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Nous-Hermes-13B-SuperHOT-8K-fp16/blob/main/results_2023-10-22T21-24-49.496203.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": 0.24779781879194632,
"em_stderr": 0.004421358038007316,
"f1": 0.3203208892617463,
"f1_stderr": 0.004418252169927022,
"acc": 0.3825450746272229,
"acc_stderr": 0.007568348592873263
},
"harness|drop|3": {
"em": 0.24779781879194632,
"em_stderr": 0.004421358038007316,
"f1": 0.3203208892617463,
"f1_stderr": 0.004418252169927022
},
"harness|gsm8k|5": {
"acc": 0.012130401819560273,
"acc_stderr": 0.003015294242890953
},
"harness|winogrande|5": {
"acc": 0.7529597474348856,
"acc_stderr": 0.012121402942855573
}
}
```
### 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] |
huggingnft/etherbears | ---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
task:
- unconditional-image-generation
datasets:
- huggingnft/etherbears
license: mit
---
# Dataset Card
## Disclaimer
All rights belong to their owners.
Models and datasets can be removed from the site at the request of the copyright holder.
## 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/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingnft](https://github.com/AlekseyKorshuk/huggingnft)
- **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)
### Dataset Summary
NFT images dataset for unconditional generation.
NFT collection available [here](https://opensea.io/collection/etherbears).
Model is available [here](https://huggingface.co/huggingnft/etherbears).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingnft/etherbears")
```
## Dataset Structure
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
- `image`: an `image` feature.
- `id`: an `int` feature.
- `token_metadata`: a `str` feature.
- `image_original_url`: a `str` feature.
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 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{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
## 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/huggingnft)
|
eezy/basic_shapes_1000 | ---
dataset_info:
- config_name: mixed
features:
- name: svg
dtype: string
- name: png
dtype: image
- name: layer_mask
dtype: image
- name: object_mask
dtype: image
- name: segments
dtype:
array3_d:
shape:
- -1
- 512
- 512
dtype: bool
splits:
- name: train
num_bytes: 825420657
num_examples: 3200
- name: validation
num_bytes: 103491703
num_examples: 400
- name: test
num_bytes: 14362883
num_examples: 400
download_size: 79499715
dataset_size: 943275243
- config_name: circles
features:
- name: svg
dtype: string
- name: png
dtype: image
- name: layer_mask
dtype: image
- name: object_mask
dtype: image
- name: segments
dtype:
array3_d:
shape:
- -1
- 512
- 512
dtype: bool
splits:
- name: train
num_bytes: 202172900
num_examples: 800
- name: validation
num_bytes: 25380696
num_examples: 100
- name: test
num_bytes: 3587893
num_examples: 100
download_size: 28664837
dataset_size: 231141489
- config_name: squares
features:
- name: svg
dtype: string
- name: png
dtype: image
- name: layer_mask
dtype: image
- name: object_mask
dtype: image
- name: segments
dtype:
array3_d:
shape:
- -1
- 512
- 512
dtype: bool
splits:
- name: train
num_bytes: 209226435
num_examples: 800
- name: validation
num_bytes: 26362720
num_examples: 100
- name: test
num_bytes: 3590905
num_examples: 100
download_size: 10376213
dataset_size: 239180060
- config_name: squares_and_circles
features:
- name: svg
dtype: string
- name: png
dtype: image
- name: layer_mask
dtype: image
- name: object_mask
dtype: image
- name: segments
dtype:
array3_d:
shape:
- -1
- 512
- 512
dtype: bool
splits:
- name: train
num_bytes: 207141741
num_examples: 800
- name: validation
num_bytes: 25735545
num_examples: 100
- name: test
num_bytes: 3590235
num_examples: 100
download_size: 20138547
dataset_size: 236467521
- config_name: scer
features:
- name: svg
dtype: string
- name: png
dtype: image
- name: layer_mask
dtype: image
- name: object_mask
dtype: image
- name: segments
dtype:
array3_d:
shape:
- -1
- 512
- 512
dtype: bool
splits:
- name: train
num_bytes: 206879581
num_examples: 800
- name: validation
num_bytes: 26012748
num_examples: 100
- name: test
num_bytes: 3593856
num_examples: 100
download_size: 20320118
dataset_size: 236486185
---
# Dataset Card for BasicShapes1000
## 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://eezy.com
### Dataset Summary
This is a synthetic dataset containing randomly-generated SVGs with various shapes
### Supported Tasks and Leaderboards
NA
### Languages
NA
## Dataset Structure
The dataset is composed of 4 base domains, plus a 'mixed' domain that is a superset of the other 4:
* `circles` - only circles
* `squares` - only squares
* `squares_and_circles` - circles and squares present in the same svg
* `scer` - squares, circles, ellipses, and rectangles present in the same svg
* `mixed` - an aggregation of all of the above
### Data Instances
There's stuff there
### Data Fields
Each example has 4 fields:
* `svg` - the raw svg as a string
* `png` - a raster rendering of the svg with a white background
* `object_mask` - a black/white mask that defines the outlines of the svg objects
* `layer_mask` - a greyscale mask that defines layers of svg objects - overlap regions are brighter. Created by making all the objects white and semi-transparent
* `segments` - a numpy array in the shape `(N,512,512), dtype='bool'` where N is the number of svg objects. The array is a mask of each object with `True` in the area of the object
### Data Splits
Train & validation include the layer and object masks, test does not
## Dataset Creation
Generated by randomly inserting objects into an SVG.
### Curation Rationale
Objects should have at least 50% of their bounding box visible - i.e. no big circle completely obscuring a little circle
### Source Data
`/dev/urandom`
#### Initial Data Collection and Normalization
NA
#### Who are the source language producers?
NA
### Annotations
see [Data Fields](#data-fields)
#### Annotation process
see [Data Fields](#data-fields)
#### Who are the annotators?
Imagemagick/pysvg
### Personal and Sensitive Information
Unlikely
## Considerations for Using the Data
Please do not use for world domination.
### Social Impact of Dataset
NA
### Discussion of Biases
Dataset is highly biased against triangles and concave shapes
### Other Known Limitations
Color selection is pretty limited.
## Additional Information
### Dataset Curators
[Aleks Clark](https://github.com/aleksclark)
### Licensing Information
CC-BY
### Citation Information
Link it I guess?
### Contributions
Thanks to [@aleksclark](https://github.com/aleksclark) for adding this dataset.
|
lpannocchi/n2sql_dataset | ---
dataset_info:
features:
- name: db_id
dtype: string
- name: question
dtype: string
- name: evidence
dtype: string
- name: context
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 60098835
num_examples: 19621
download_size: 0
dataset_size: 60098835
---
# Dataset Card for "n2sql_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5 | ---
pretty_name: Evaluation run of lmsys/vicuna-7b-v1.5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) 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 3 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_lmsys__vicuna-7b-v1.5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T21:05:37.153515](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5/blob/main/results_2023-10-21T21-05-37.153515.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\": 0.017932046979865772,\n\
\ \"em_stderr\": 0.0013590184569504276,\n \"f1\": 0.08961094798657747,\n\
\ \"f1_stderr\": 0.002014243406072028,\n \"acc\": 0.4016346602057357,\n\
\ \"acc_stderr\": 0.010076117588605417\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.017932046979865772,\n \"em_stderr\": 0.0013590184569504276,\n\
\ \"f1\": 0.08961094798657747,\n \"f1_stderr\": 0.002014243406072028\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08188021228203184,\n \
\ \"acc_stderr\": 0.007552338527716956\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7213891081294396,\n \"acc_stderr\": 0.012599896649493878\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lmsys/vicuna-7b-v1.5
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|arc:challenge|25_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T12_56_49.814418
path:
- '**/details_harness|drop|3_2023-10-19T12-56-49.814418.parquet'
- split: 2023_10_21T21_05_37.153515
path:
- '**/details_harness|drop|3_2023-10-21T21-05-37.153515.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T21-05-37.153515.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T12_56_49.814418
path:
- '**/details_harness|gsm8k|5_2023-10-19T12-56-49.814418.parquet'
- split: 2023_10_21T21_05_37.153515
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-05-37.153515.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-05-37.153515.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hellaswag|10_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:09:52.202468.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T12:09:52.202468.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T12:09:52.202468.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T12_56_49.814418
path:
- '**/details_harness|winogrande|5_2023-10-19T12-56-49.814418.parquet'
- split: 2023_10_21T21_05_37.153515
path:
- '**/details_harness|winogrande|5_2023-10-21T21-05-37.153515.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T21-05-37.153515.parquet'
- config_name: results
data_files:
- split: 2023_08_17T12_09_52.202468
path:
- results_2023-08-17T12:09:52.202468.parquet
- split: 2023_10_19T12_56_49.814418
path:
- results_2023-10-19T12-56-49.814418.parquet
- split: 2023_10_21T21_05_37.153515
path:
- results_2023-10-21T21-05-37.153515.parquet
- split: latest
path:
- results_2023-10-21T21-05-37.153515.parquet
---
# Dataset Card for Evaluation run of lmsys/vicuna-7b-v1.5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/vicuna-7b-v1.5
- **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 [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) 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 3 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_lmsys__vicuna-7b-v1.5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T21:05:37.153515](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5/blob/main/results_2023-10-21T21-05-37.153515.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": 0.017932046979865772,
"em_stderr": 0.0013590184569504276,
"f1": 0.08961094798657747,
"f1_stderr": 0.002014243406072028,
"acc": 0.4016346602057357,
"acc_stderr": 0.010076117588605417
},
"harness|drop|3": {
"em": 0.017932046979865772,
"em_stderr": 0.0013590184569504276,
"f1": 0.08961094798657747,
"f1_stderr": 0.002014243406072028
},
"harness|gsm8k|5": {
"acc": 0.08188021228203184,
"acc_stderr": 0.007552338527716956
},
"harness|winogrande|5": {
"acc": 0.7213891081294396,
"acc_stderr": 0.012599896649493878
}
}
```
### 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] |
andyflinn/heidiland | ---
license: cc-by-nc-sa-4.0
---
|
tyzhu/lmind_hotpot_train500_eval300_v1_doc | ---
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: 84812
num_examples: 500
- name: train_recite_qa
num_bytes: 525773
num_examples: 500
- name: eval_qa
num_bytes: 49916
num_examples: 300
- name: eval_recite_qa
num_bytes: 324839
num_examples: 300
- name: all_docs
num_bytes: 738612
num_examples: 1594
- name: all_docs_eval
num_bytes: 738503
num_examples: 1594
- name: train
num_bytes: 738612
num_examples: 1594
- name: validation
num_bytes: 738612
num_examples: 1594
download_size: 2429329
dataset_size: 3939679
---
# Dataset Card for "lmind_hotpot_train500_eval300_v1_doc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_25_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: 9801621
num_examples: 15849
download_size: 4934729
dataset_size: 9801621
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_25_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hac541309/the_stack_smol_all_merge_ws | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2367385476
num_examples: 300000
download_size: 820069138
dataset_size: 2367385476
---
# Dataset Card for "the_stack_smol_all_merge_ws"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
navjordj/VG_summarization | ---
task_categories:
- summarization
- text2text-generation
language:
- 'no'
- nb
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: title
dtype: string
- name: url
dtype: string
- name: published
dtype: string
- name: classes
dtype: string
- name: article
dtype: string
- name: ingress
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 482362411.09144986
num_examples: 157038
- name: validation
num_bytes: 36309721.60567524
num_examples: 11821
- name: test
num_bytes: 57632967.30287493
num_examples: 18763
download_size: 364433583
dataset_size: 576305100.0
---
# VG Summarization Dataset
The source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used
the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper "VG".
The quality of the summary-article pairs has not been evaluated.
# License
Please refer to the license of Norsk Aviskorpus
# Citation
If you are using this dataset in your work, please cite our master thesis which this dataset was a part of
```
@mastersthesis{navjord2023beyond,
title={Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers},
author={Navjord, J{\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen},
year={2023},
school={Norwegian University of Life Sciences, {\AA}s}
}
``` |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_7 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1139024388.0
num_examples: 223689
download_size: 1159307084
dataset_size: 1139024388.0
---
# Dataset Card for "chunk_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sachinkelenjaguri/sentiment_classification | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: sachin-test-summarizer
## Dataset Description
This dataset has been automatically processed by AutoTrain for project sachin-test-summarizer.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u2212 Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.",
"target": 1
},
{
"text": "The Group is not aware of any noise pollution that could negatively impact the environment, nor is it aware of any impact on biodiversity. With regards to land use, the Group is only a commercial user, and the Group is not aware of any local constraints with regards to water supply. The Group does not believe that it is at risk with regards to climate change in the near-or mid-term.",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1', '2'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1000 |
| valid | 320 |
|
nirantk/dbpedia-entities-splade-ensembledistil-10K | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: openai
sequence: float32
- name: embed_text
dtype: string
- name: vec
sequence: float32
splits:
- name: train
num_bytes: 1289801820
num_examples: 10000
download_size: 0
dataset_size: 1289801820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- question-answering
- feature-extraction
language:
- en
pretty_name: 'DBPedia SPLADE + OpenAI: 10,000 SPLADE Sparse Vectors + OpenAI Embedding'
size_categories:
- 1K<n<10K
---
# Dataset Card for "dbpedia-entities-splade-10K"
This dataset has both OpenAI and SPLADE vectors for 10,000 DBPedia entries. This adds SPLADE Vectors to [KShivendu/dbpedia-entities-openai-1M/](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M/)
Model id used to make these vectors:
```python
model_id = "naver/splade-cocondenser-ensembledistil"
```
This is available on Huggingface.
If you'd like to extract the indices and weights/values from the vectors, you can do so using the following snippet:
```python
import numpy as np
vec = np.array(ds[0]['vec']) # where ds is the dataset
sparse_indices = vec.nonzero()
sparse_values = vec[sparse_indices]
sparse_indices, sparse_values
``` |
version-control/tf-1.0-1.13-oss-seed-1.0 | ---
dataset_info:
features:
- name: seed
dtype: string
- name: seed_api
dtype: string
- name: index
dtype: int64
splits:
- name: train
num_bytes: 381163
num_examples: 524
download_size: 174809
dataset_size: 381163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/VALUE_wnli_null_relcl | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 1129
num_examples: 5
- name: test
num_bytes: 6167
num_examples: 18
- name: train
num_bytes: 11060
num_examples: 38
download_size: 16096
dataset_size: 18356
---
# Dataset Card for "VALUE_wnli_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dim/what_where_when_ru | ---
license: cc-by-nc-nd-4.0
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: explanation
dtype: string
- name: url
dtype: string
- name: uuid
dtype: string
splits:
- name: train
num_bytes: 192540438
num_examples: 227996
download_size: 107153363
dataset_size: 192540438
---
|
aeromaki/WOS46985 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label
sequence: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 59600414.67012876
num_examples: 42286
- name: test
num_bytes: 3312230.394806853
num_examples: 2350
- name: validation
num_bytes: 3310820.9350643824
num_examples: 2349
download_size: 37635945
dataset_size: 66223466.0
---
# Dataset Card for "WOS46985"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thanhduycao/soict_train_dataset_filter | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: intent
dtype: string
- name: sentence_annotation
dtype: string
- name: entities
list:
- name: type
dtype: string
- name: filler
dtype: string
- name: file
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: origin_transcription
dtype: string
- name: sentence_norm
dtype: string
- name: sentence_norm_v2
dtype: string
- name: w2v2_large_transcription
dtype: string
- name: wer
dtype: float64
splits:
- name: train
num_bytes: 3205296038.433596
num_examples: 6184
- name: test
num_bytes: 566006350.9006286
num_examples: 1092
download_size: 902006355
dataset_size: 3771302389.3342247
---
# Dataset Card for "soict_train_dataset_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aaeagal/Reveal | ---
license: mit
---
|
open-llm-leaderboard/details_nbeerbower__bophades-v2-mistral-7B | ---
pretty_name: Evaluation run of nbeerbower/bophades-v2-mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nbeerbower/bophades-v2-mistral-7B](https://huggingface.co/nbeerbower/bophades-v2-mistral-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_nbeerbower__bophades-v2-mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-05T21:13:13.764254](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__bophades-v2-mistral-7B/blob/main/results_2024-04-05T21-13-13.764254.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\": 0.6546029785617975,\n\
\ \"acc_stderr\": 0.03205422260171908,\n \"acc_norm\": 0.6538543488631021,\n\
\ \"acc_norm_stderr\": 0.032728786059846776,\n \"mc1\": 0.6144430844553244,\n\
\ \"mc1_stderr\": 0.017038839010591663,\n \"mc2\": 0.7682328726360895,\n\
\ \"mc2_stderr\": 0.01387938426516886\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7150170648464164,\n \"acc_stderr\": 0.013191348179838793,\n\
\ \"acc_norm\": 0.7337883959044369,\n \"acc_norm_stderr\": 0.012915774781523198\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7182832105158335,\n\
\ \"acc_stderr\": 0.004489166767430659,\n \"acc_norm\": 0.8915554670384386,\n\
\ \"acc_norm_stderr\": 0.0031030554162430534\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\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\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\": 0.64,\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\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\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\": 0.3,\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\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\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\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\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\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\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\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\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\": 0.7636363636363637,\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\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097112,\n \
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097112\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\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\"\
: 0.5092592592592593,\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\": 0.02552472232455335,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.046840993210771065\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\": 0.8760683760683761,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546836,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546836\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43798882681564244,\n\
\ \"acc_stderr\": 0.016593394227564843,\n \"acc_norm\": 0.43798882681564244,\n\
\ \"acc_norm_stderr\": 0.016593394227564843\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035457,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035457\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.47392438070404175,\n \"acc_stderr\": 0.01275285834653313,\n\
\ \"acc_norm\": 0.47392438070404175,\n \"acc_norm_stderr\": 0.01275285834653313\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.02815637344037142\n },\n \"harness|hendrycksTest-professional_psychology|5\"\
: {\n \"acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n\
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\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\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685516\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\": 0.6144430844553244,\n\
\ \"mc1_stderr\": 0.017038839010591663,\n \"mc2\": 0.7682328726360895,\n\
\ \"mc2_stderr\": 0.01387938426516886\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8539857932123125,\n \"acc_stderr\": 0.009924440374585244\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6914329037149356,\n \
\ \"acc_stderr\": 0.012723076049815898\n }\n}\n```"
repo_url: https://huggingface.co/nbeerbower/bophades-v2-mistral-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_04_05T21_13_13.764254
path:
- '**/details_harness|arc:challenge|25_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|gsm8k|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hellaswag|10_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T21-13-13.764254.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T21-13-13.764254.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- '**/details_harness|winogrande|5_2024-04-05T21-13-13.764254.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-05T21-13-13.764254.parquet'
- config_name: results
data_files:
- split: 2024_04_05T21_13_13.764254
path:
- results_2024-04-05T21-13-13.764254.parquet
- split: latest
path:
- results_2024-04-05T21-13-13.764254.parquet
---
# Dataset Card for Evaluation run of nbeerbower/bophades-v2-mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [nbeerbower/bophades-v2-mistral-7B](https://huggingface.co/nbeerbower/bophades-v2-mistral-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_nbeerbower__bophades-v2-mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-05T21:13:13.764254](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__bophades-v2-mistral-7B/blob/main/results_2024-04-05T21-13-13.764254.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": 0.6546029785617975,
"acc_stderr": 0.03205422260171908,
"acc_norm": 0.6538543488631021,
"acc_norm_stderr": 0.032728786059846776,
"mc1": 0.6144430844553244,
"mc1_stderr": 0.017038839010591663,
"mc2": 0.7682328726360895,
"mc2_stderr": 0.01387938426516886
},
"harness|arc:challenge|25": {
"acc": 0.7150170648464164,
"acc_stderr": 0.013191348179838793,
"acc_norm": 0.7337883959044369,
"acc_norm_stderr": 0.012915774781523198
},
"harness|hellaswag|10": {
"acc": 0.7182832105158335,
"acc_stderr": 0.004489166767430659,
"acc_norm": 0.8915554670384386,
"acc_norm_stderr": 0.0031030554162430534
},
"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": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"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": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"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": 0.3,
"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": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"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": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"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": 0.4126984126984127,
"acc_stderr": 0.02535574126305527,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.02535574126305527
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"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": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"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": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
"acc_stderr": 0.028057791672989017,
"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.028057791672989017
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033456,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.02385479568097112,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.02385479568097112
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066485,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066485
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.03958027231121569,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"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": 0.5092592592592593,
"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": 0.02552472232455335,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8091603053435115,
"acc_stderr": 0.03446513350752598,
"acc_norm": 0.8091603053435115,
"acc_norm_stderr": 0.03446513350752598
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7791411042944786,
"acc_stderr": 0.03259177392742178,
"acc_norm": 0.7791411042944786,
"acc_norm_stderr": 0.03259177392742178
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.046840993210771065,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.046840993210771065
},
"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": 0.8760683760683761,
"acc_stderr": 0.021586494001281365,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.021586494001281365
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8212005108556832,
"acc_stderr": 0.013702643715368983,
"acc_norm": 0.8212005108556832,
"acc_norm_stderr": 0.013702643715368983
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7283236994219653,
"acc_stderr": 0.02394851290546836,
"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.02394851290546836
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.43798882681564244,
"acc_stderr": 0.016593394227564843,
"acc_norm": 0.43798882681564244,
"acc_norm_stderr": 0.016593394227564843
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7156862745098039,
"acc_stderr": 0.02582916327275748,
"acc_norm": 0.7156862745098039,
"acc_norm_stderr": 0.02582916327275748
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188933,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188933
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.024383665531035457,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.024383665531035457
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5,
"acc_stderr": 0.029827499313594685,
"acc_norm": 0.5,
"acc_norm_stderr": 0.029827499313594685
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47392438070404175,
"acc_stderr": 0.01275285834653313,
"acc_norm": 0.47392438070404175,
"acc_norm_stderr": 0.01275285834653313
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6875,
"acc_stderr": 0.02815637344037142,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.02815637344037142
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6813725490196079,
"acc_stderr": 0.01885008469646872,
"acc_norm": 0.6813725490196079,
"acc_norm_stderr": 0.01885008469646872
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"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": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685516,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685516
},
"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": 0.6144430844553244,
"mc1_stderr": 0.017038839010591663,
"mc2": 0.7682328726360895,
"mc2_stderr": 0.01387938426516886
},
"harness|winogrande|5": {
"acc": 0.8539857932123125,
"acc_stderr": 0.009924440374585244
},
"harness|gsm8k|5": {
"acc": 0.6914329037149356,
"acc_stderr": 0.012723076049815898
}
}
```
## 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. -->
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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. -->
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
fhatje/chatgpt_curated_last_dataset | ---
dataset_info:
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data_files:
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path: data/train-*
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path: data/test-*
---
|
Hackpk/TEST | ---
license: apache-2.0
---
|
bdsaglam/musique-jerx-sft-multi-turn-multi-sentence-openai | ---
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---
|
alvarobartt/ultrafeedback-multi-binarized-quality-preferences-cleaned | ---
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---
|
hippocrates/OphthoQA_test | ---
dataset_info:
features:
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path: data/train-*
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|
distilled-from-one-sec-cv12/chunk_198 | ---
dataset_info:
features:
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sequence: float64
splits:
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download_size: 1103309698
dataset_size: 1080075588
---
# Dataset Card for "chunk_198"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danielz01/cowc-m | ---
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data_files:
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path: Utah/train-*
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data_files:
- split: train
path: Vaihingen/train-*
---
# Dataset Card for "cowc-m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Konthee/translated-th-coco2017-test | ---
dataset_info:
features:
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configs:
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data_files:
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---
|
DEFT-2023/DEFT2023 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- fr
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1k<n<10k
source_datasets:
- original
task_categories:
- question-answering
- multiple-choice
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: frenchmedmcqa
pretty_name: FrenchMedMCQA
---
# Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
## Table of Contents
- [Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain](#dataset-card-for-frenchmedmcqa--a-french-multiple-choice-question-answering-corpus-for-medical-domain)
- [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)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact](#contact)
## Dataset Description
- **Homepage:** https://deft2023.univ-avignon.fr/
- **Repository:** https://deft2023.univ-avignon.fr/
- **Paper:** [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://hal.science/hal-03824241/document)
- **Leaderboard:** Coming soon
- **Point of Contact:** [Yanis LABRAK](mailto:yanis.labrak@univ-avignon.fr)
### Dataset Summary
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.
Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).
We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
### Supported Tasks and Leaderboards
Multiple-Choice Question Answering (MCQA)
### Languages
The questions and answers are available in French.
## Dataset Structure
### Data Instances
```json
{
"id": "1863462668476003678",
"question": "Parmi les propositions suivantes, laquelle (lesquelles) est (sont) exacte(s) ? Les chylomicrons plasmatiques :",
"answers": {
"a": "Sont plus riches en cholestérol estérifié qu'en triglycérides",
"b": "Sont synthétisés par le foie",
"c": "Contiennent de l'apolipoprotéine B48",
"d": "Contiennent de l'apolipoprotéine E",
"e": "Sont transformés par action de la lipoprotéine lipase"
},
"correct_answers": [
"c",
"d",
"e"
],
"subject_name": "pharmacie",
"type": "multiple"
}
```
### Data Fields
- `id` : a string question identifier for each example
- `question` : question text (a string)
- `answer_a` : Option A
- `answer_b` : Option B
- `answer_c` : Option C
- `answer_d` : Option D
- `answer_e` : Option E
- `correct_answers` : Correct options, i.e., A, D and E
- `choice_type` ({"single", "multiple"}): Question choice type.
- "single": Single-choice question, where each choice contains a single option.
- "multiple": Multi-choice question, where each choice contains a combination of multiple options.
### Data Splits
| # Answers | Training | Validation | Test | Total |
|:---------:|:--------:|:----------:|:----:|:-----:|
| 1 | 595 | 164 | 321 | 1,080 |
| 2 | 528 | 45 | 97 | 670 |
| 3 | 718 | 71 | 141 | 930 |
| 4 | 296 | 30 | 56 | 382 |
| 5 | 34 | 2 | 7 | 43 |
| Total | 2171 | 312 | 622 | 3,105 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.
### Licensing Information
Apache 2.0
### Citation Information
If you find this useful in your research, please consider citing the dataset paper :
```latex
@inproceedings{labrak-etal-2022-frenchmedmcqa,
title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain",
author = "Labrak, Yanis and
Bazoge, Adrien and
Dufour, Richard and
Daille, Beatrice and
Gourraud, Pierre-Antoine and
Morin, Emmanuel and
Rouvier, Mickael",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.5",
pages = "41--46",
abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
}
```
### Contact
Thanks to contact [Yanis LABRAK](https://github.com/qanastek) for more information about this dataset.
|
joey234/mmlu-abstract_algebra | ---
dataset_info:
features:
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dtype: string
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sequence: string
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dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
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configs:
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data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-abstract_algebra"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
farcasclaudiu/autotrain-data-myfirstproject | ---
language:
- en
tags:
- finance
size_categories:
- 10K<n<100K
dataset_info:
features:
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splits:
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num_examples: 67229
download_size: 12188906
dataset_size: 35340915
---
|
liuyanchen1015/MULTI_VALUE_sst2_participle_past_tense | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
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dtype: int64
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dtype: int64
splits:
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num_examples: 15
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num_bytes: 5566
num_examples: 37
- name: train
num_bytes: 67836
num_examples: 569
download_size: 35073
dataset_size: 75579
---
# Dataset Card for "MULTI_VALUE_sst2_participle_past_tense"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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