datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Leon-Leee/WizardLM_evol_instruct_V2_only_code | ---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---
filtered from (WizardLM/WizardLM_evol_instruct_V2_196k)[https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k] using "```" |
BangumiBase/tengentoppa | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Tengen Toppa
This is the image base of bangumi Tengen Toppa, we detected 40 characters, 3081 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 107 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 137 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 104 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 29 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 33 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 36 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 28 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 359 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 73 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 133 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 151 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 32 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 44 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 78 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 25 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 22 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 17 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 44 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 104 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 51 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 339 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 32 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 16 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 16 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 53 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 50 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 59 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 36 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 28 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 19 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 9 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 73 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 13 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 616 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Totomixor/Dataset-1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## 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] |
tushar117/xalign | ---
annotations_creators:
- found
configs:
- release_v1
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
- en
language_creators:
- crowdsourced
license:
- cc-by-nc-sa-4.0
- mit
multilinguality:
- multilingual
paperswithcode_id: xalign
pretty_name: 'XAlign'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- xalign
- NLG
- low-resource
- LRL
task_categories:
- table-to-text
task_ids:
- rdf-to-text
---
# Dataset Card for XAlign
## 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)
- [Known Limitations](#known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [XAlign homepage](https://github.com/tushar117/XAlign)
- **Repository:** [XAlign repo](https://github.com/tushar117/XAlign)
- **Paper:** [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://arxiv.org/abs/2202.00291)
- **Leaderboard:** [Papers With Code Leaderboard for XAlign](https://paperswithcode.com/sota/data-to-text-generation-on-xalign)
- **Point of Contact:** [Tushar Abhishek](tushar.abhishek@research.iiit.ac.in)
### Dataset Summary
It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset where facts are in English and corresponding sentences are in native language for person biographies. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.
### Supported Tasks and Leaderboards
- 'Data-to-text Generation': XAlign dataset can be used to train cross-lingual data-to-text generation models. The model performance can measured through any text generation evaluation metrics by taking average across all the languages. [Sagare et al. (2022)](https://arxiv.org/abs/2209.11252) reported average BLEU score of 29.27 and average METEOR score of 53.64 over the test set.
- 'Relation Extraction': XAlign could also be used for cross-lingual relation extraction where relations in English can be extracted from associated native sentence.
See [Papers With Code Leaderboard](https://paperswithcode.com/sota/data-to-text-generation-on-xalign) for more models.
### Languages
Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and English (en).
## Dataset Structure
### Data Fields
Each record consist of the following entries:
- sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.)
- `facts` (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.
- language (string) : Language identifier.
The `facts` key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:
- subject (string) : central entity.
- object (string) : entity or a piece of information about the subject.
- predicate (string) : relationship that connects the subject and the object.
- qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier_predicate to represent property of qualifer and qualifier_object to store value for the qualifier's predicate.
### Data Instances
Example from English
```
{
"sentence": "Mark Paul Briers (born 21 April 1968) is a former English cricketer.",
"facts": [
{
"subject": "Mark Briers",
"predicate": "date of birth",
"object": "21 April 1968",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "occupation",
"object": "cricketer",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "country of citizenship",
"object": "United Kingdom",
"qualifiers": []
}
],
"language": "en"
}
```
Example from one of the low-resource languages (i.e. Hindi)
```
{
"sentence": "बोरिस पास्तेरनाक १९५८ में साहित्य के क्षेत्र में नोबेल पुरस्कार विजेता रहे हैं।",
"facts": [
{
"subject": "Boris Pasternak",
"predicate": "nominated for",
"object": "Nobel Prize in Literature",
"qualifiers": [
{
"qualifier_predicate": "point in time",
"qualifier_subject": "1958"
}
]
}
],
"language": "hi"
}
```
### Data Splits
The XAlign dataset has 3 splits: train, validation, and test. Below are the statistics the dataset.
| Dataset splits | Number of Instances in Split |
| --- | --- |
| Train | 499155 |
| Validation | 55469 |
| Test | 7425 |
## Dataset Creation
### Curation Rationale
Most of the existing Data-to-Text datasets are available in English. Also, the structured Wikidata entries for person entities in low resource languages are minuscule in number compared to that in English. Thus, monolingual Data-to-Text for low resource languages suffers from data sparsity. XAlign dataset would be useful in creation of cross-lingual Data-to-Text generation systems that take a set of English facts as input and generates a sentence capturing the fact-semantics in the specified language.
### Source Data
#### Initial Data Collection and Normalization
The dataset creation process starts with an intial list of ~95K person entities selected from Wikidata and each of which has a link to a corresponding Wikipedia page in at least one of our 11 low resource languages. This leads to a dataset where every instance is a tuple containing entityID, English Wikidata facts, language identifier, Wikipedia URL for the entityID. The facts (in English) are extracted from the 20201221 WikiData dump for each entity using the [WikiData](https://query.wikidata.org) APIs. The facts are gathered only for the speficied Wikidata property (or relation) types that captures most useful factual information for person entities: WikibaseItem, Time, Quantity, and Monolingualtext.This leads to overall ~0.55M data instances across all the 12 languages. Also, for each language, the sentences (along with section information) are extracted from 20210520 Wikipedia XML dump using the pre-processing steps as described [here](https://arxiv.org/abs/2202.00291).
For every (entity, language) pair, the pre-processed dataset contains a set of English Wikidata facts and a set of Wikipedia sentences in that language. In order to create train and validation dataset, these are later passed through a two-stage automatic aligner as proposed in [abhishek et al. (2022)](https://arxiv.org/abs/2202.00291) to associate a sentence with a subset of facts.
#### Who are the source language producers?
The text are extracted from Wikipedia and facts are retrieved from Wikidata.
### Annotations
#### Annotation process
The Manual annotation of Test dataset was done in two phases. For both the phases, the annotators were presented with (low resource language sentence, list of English facts). They were asked to mark facts present in the given sentence. There were also specific guidelines to ignore redundant facts, handle abbreviations, etc. More detailed annotation guidelines and ethical statement are mentioned [here](https://docs.google.com/document/d/1ucGlf-Jm1ywQ_Fjw9f2UqPeMWPlBnlZA46UY7KuZ0EE/edit)
. In the first phase, we got 60 instances labeled per language by a set of 8 expert annotators (trusted graduate students who understood the task very well). In phase 2, we selected 8 annotators per language from the [National Register of Translators](https://www.ntm.org.in/languages/english/nrtdb.aspx}). We tested these annotators using phase 1 data as golden control set, and shortlisted up to 4 annotators per language who scored highest (on Kappa score with golden annotations).
#### Who are the annotators?
Human annotators were selected appropriately (after screening) from [National Translation Mission](https://www.ntm.org.in) for Test set creation.
### Personal and Sensitive Information
The dataset does not involve collection or storage of any personally identifiable information or offensive information at any stage.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of the this dataset is to help develop cross-lingual Data-to-Text generation systems that are vital in many downstream Natural Language Processing (NLP) applications like automated dialog systems, domain-specific chatbots, open domain question answering, authoring sports reports, etc. These systems will be useful for powering business applications like Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc.
### Known Limitations
The XAlign dataset focus only on person biographies and system developed on this dataset might not be generalized to other domains.
## Additional Information
### Dataset Curators
This dataset is collected by Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma of Information Retrieval and Extraction Lab (IREL), Hyderabad, India. They released [scripts](https://github.com/tushar117/xalign) to collect and process the data into the Data-to-Text format.
### Licensing Information
The XAlign dataset is released under the [MIT License](https://github.com/tushar117/XAlign/blob/main/LICENSE).
### Citation Information
```
@article{abhishek2022xalign,
title={XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages},
author={Abhishek, Tushar and Sagare, Shivprasad and Singh, Bhavyajeet and Sharma, Anubhav and Gupta, Manish and Varma, Vasudeva},
journal={arXiv preprint arXiv:2202.00291},
year={2022}
}
```
### Contributions
Thanks to [Tushar Abhishek](https://github.com/tushar117), [Shivprasad Sagare](https://github.com/ShivprasadSagare), [Bhavyajeet Singh](https://github.com/bhavyajeet), [Anubhav Sharma](https://github.com/anubhav-sharma13), [Manish Gupta](https://github.com/blitzprecision) and [Vasudeva Varma](vv@iiit.ac.in) for adding this dataset.
Additional thanks to the annotators from National Translation Mission for their crucial contributions to creation of the test dataset: Bhaswati Bhattacharya, Aditi Sarkar, Raghunandan B. S., Satish M., Rashmi G.Rao, Vidyarashmi PN, Neelima Bhide, Anand Bapat, Krishna Rao N V, Nagalakshmi DV, Aditya Bhardwaj
Vuppula, Nirupama Patel, Asir. T, Sneha Gupta, Dinesh Kumar, Jasmin Gilani, Vivek R, Sivaprasad S, Pranoy J, Ashutosh Bharadwaj, Balaji Venkateshwar, Vinkesh Bansal, Vaishnavi Udyavara, Ramandeep Singh, Khushi Goyal, Yashasvi LN Pasumarthy and Naren Akash. |
irds/medline_2017_trec-pm-2018 | ---
pretty_name: '`medline/2017/trec-pm-2018`'
viewer: false
source_datasets: ['irds/medline_2017']
task_categories:
- text-retrieval
---
# Dataset Card for `medline/2017/trec-pm-2018`
The `medline/2017/trec-pm-2018` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/medline#medline/2017/trec-pm-2018).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=22,429
- For `docs`, use [`irds/medline_2017`](https://huggingface.co/datasets/irds/medline_2017)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/medline_2017_trec-pm-2018', 'queries')
for record in queries:
record # {'query_id': ..., 'disease': ..., 'gene': ..., 'demographic': ...}
qrels = load_dataset('irds/medline_2017_trec-pm-2018', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Roberts2018TrecPm,
title={Overview of the TREC 2018 Precision Medicine Track},
author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar},
booktitle={TREC},
year={2018}
}
```
|
livinNector/ta_ner | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
splits:
- name: train
num_bytes: 191152277.0
num_examples: 518348
- name: validation
num_bytes: 1637859.0
num_examples: 5381
- name: test
num_bytes: 905672.0
num_examples: 3369
download_size: 50425637
dataset_size: 193695808.0
---
# Dataset Card for "ta_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mncai/MedGPT-5k-ko | ---
license: gpl-3.0
task_categories:
- conversational
language:
- ko
tags:
- medical
--- |
GEM-submissions/Leo__mbart-large-cc25__1645802644 | ---
benchmark: gem
type: prediction
submission_name: mbart-large-cc25
---
|
dmrau/cqadupstack-english-qrels | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 100171
num_examples: 3765
download_size: 0
dataset_size: 100171
---
# Dataset Card for "cqadupstack-english-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Alexator26/400_second_face_stickers_cleared | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 140556677.0
num_examples: 227
download_size: 140560331
dataset_size: 140556677.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sebinbusra/turkishReviews-ds-mini | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1252876.2642514652
num_examples: 3378
- name: validation
num_bytes: 139455.7357485349
num_examples: 376
download_size: 896649
dataset_size: 1392332.0
---
# Dataset Card for "turkishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
diwank/orca_minis_uncensored-chatml | ---
dataset_info:
features:
- name: chatml
list:
- name: content
dtype: string
- name: name
dtype: string
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 325812780
num_examples: 83087
download_size: 0
dataset_size: 325812780
---
# Dataset Card for "orca_minis_uncensored-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
odunola/french-preprocessed-test | ---
dataset_info:
features:
- name: english_transcript
dtype: string
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 96047703
num_examples: 100
download_size: 15798024
dataset_size: 96047703
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Andaleciomusic/bebebigpen | ---
license: openrail
---
|
lewtun/hamburgers | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 22977927.0
num_examples: 10
download_size: 22973038
dataset_size: 22977927.0
---
# Dataset Card for "hamburgers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Medradome/Taina | ---
license: apache-2.0
---
|
japanese-asr/whisper_transcriptions.reazonspeech.small.wer_10.0 | ---
dataset_info:
config_name: small
features:
- name: audio
dtype:
audio:
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configs:
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data_files:
- split: train
path: small/train-*
---
|
liaad/math_dataset_portuguese | ---
license: mit
dataset_info:
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---
To run generation code within 'mathematics_dataset\mathematics_dataset\':
- Activate python venv ``` .\.venv\Scripts\activate ```
- Requirements defined in requires.txt
- Run ```python generate_to_file.py --output_dir ds``` to generate dataset to directory \ds
Had to change enconding when opening files to utf-8 so that some characters are allowed (ã õ é)
To obtain dataset with the correct amount of rows:
- python ```generate_to_file.py --output_dir ds --per_train_module 1999998 --per_test_module 10000```
This dataset creates train set (train-easy,train-medium,train-hard) and the creates extrapolation ("measure generalization along various axes of difficulty to beyond that seen during training") and interpolation ("test questions are distinct
from the train questions") tests.
On HugginFace only interpolate tests are used as test set.
Some tests will not work, since they rely on english terms. |
Marchanjo/spider-en-extra-3enr-1enb | ---
license: cc-by-sa-4.0
---
Distributed under the Creative Commons-by-sa-4.0 respecting the ShareAlike of the [Spider Dataset](https://yale-lily.github.io/spider).
Code explanations and links for the model's checkpoints and datasets are on Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql)
Here is the [Hugging Face collection](https://huggingface.co/collections/Marchanjo/mrat-sql-65a671743bb0e70b416561f6), you can download the model's checkpoints and datasets, but to understand is better to go to Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
# mRAT-SQL-FIT
## A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention
Marcelo Archanjo Jose, Fabio Gagliardi Cozman
Long sequences of text are challenging in the context of transformers, due to quadratic memory increase in the self-attention mechanism. As this issue directly affects the translation from natural language to SQL queries (as techniques usually take as input a concatenated text with the question and the database schema), we present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. We propose a training process with database schema pruning (removal of tables and columns names that are useless for the query of interest). In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously: English, Portuguese, Spanish, and French. Our proposed technique used the Spider dataset and increased the exact set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev). Source code, evaluations, and checkpoints are available at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
[paper published in Springer-Nature - International Journal of Information Technology](https://doi.org/10.1007/s41870-023-01342-3), [here the SharedIt link](https://rdcu.be/dff19). [here the pre-print in arXiv](https://arxiv.org/abs/2306.14256).
# mRAT-SQL+GAP
## mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer
Marcelo Archanjo José, Fabio Gagliardi Cozman
The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large number of techniques are geared towards the English language; in this work, we thus investigated translation to SQL when input questions are given in the Portuguese language. To do so, we properly adapted state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by relying on a multilingual BART model (we report tests with other language models), and we produced a translated version of the Spider dataset. Our experiments expose interesting phenomena that arise when non-English languages are targeted; in particular, it is better to train with original and translated training datasets together, even if a single target language is desired. This multilingual BART model fine-tuned with a double-size training dataset (English and Portuguese) achieved 83% of the baseline, making inferences for the Portuguese test dataset. This investigation can help other researchers to produce results in Machine Learning in a language different from English. Our multilingual ready version of RAT-SQL+GAP and the data are available, open-sourced as mRAT-SQL+GAP at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql).
BRACIS 2021: [paper published in Springer Lecture Notes in Computer Science](https://link.springer.com/chapter/10.1007%2F978-3-030-91699-2_35), [here the pre-print in arXiv](https://arxiv.org/abs/2110.03546).
Based on: RAT-SQL+GAP: [Github](https://github.com/awslabs/gap-text2sql). Paper: [AAAI 2021 paper](https://arxiv.org/abs/2012.10309) |
alang-fortinet/whois_full_ipv4.csv | ---
size_categories:
- 1M<n<10M
--- |
davanstrien/fuego-20230502-130233-6cfaa1 | ---
tags:
- fuego
fuego:
id: 20230502-130233-6cfaa1
status: running
script: script.py
requirements_file: requirements.txt
space_id: davanstrien/fuego-20230502-130233-6cfaa1
space_hardware: cpu-basic
---
|
DigitalUmuganda/common-voice-kinyarwanda-text-dataset | ---
pretty_name: kinyarwanda text corpus
annotations_creators:
- crowd-sourced
language_creators:
- Digital Umuganda
language:
- rw
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<3M
source_datasets:
- original
task_categories:
- Language-model
- Automatic-Speech-Recognition
task_ids:
- Language-model
---
# Dataset Card for DigitalUmuganda/common-voice-kinyarwanda-text-dataset
|
stodipro/blenderaddon | ---
license: unknown
---
|
dvsth/LEGIT | ---
dataset_info:
features:
- name: choice
dtype: int64
- name: k
dtype: int64
- name: k1
dtype: int64
- name: n
dtype: float64
- name: n1
dtype: float64
- name: word
dtype: string
- name: word0
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: image
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splits:
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num_examples: 3712
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num_examples: 14283
- name: valid
num_bytes: 3184961.75
num_examples: 3237
download_size: 17726271
dataset_size: 20895290.0
---
# Dataset Card for "LEGIT-2023"
Label key:
- 0 or 1: word 0 or 1 is more legible, other unknown
- 2: both words are equally legible
- 3: neither word is legible |
moezzzzzzzzz/PaLM_Ara | ---
license: cc-by-nc-3.0
---
|
drcostco/hmn-race | ---
license: other
---
|
tr416/v2_dataset_20231008_002613 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 75203880.0
num_examples: 29285
- name: test
num_bytes: 760128.0
num_examples: 296
download_size: 12818386
dataset_size: 75964008.0
---
# Dataset Card for "v2_dataset_20231008_002613"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cakiki/fortran_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 5773596
num_examples: 243762
download_size: 1463437
dataset_size: 5773596
---
# Dataset Card for "fortran_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jamesagilesoda/dummy-text-10k | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: lang
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 18471871.818181816
num_examples: 10000
- name: test
num_bytes: 1847187.1818181819
num_examples: 1000
download_size: 11742741
dataset_size: 20319059.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
shinonomelab/cleanvid-15m_map | ---
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: description
dtype: string
- name: duration
dtype: float64
- name: aspectratio
dtype: string
- name: videourl
dtype: string
- name: author
dtype: string
- name: categories
dtype: string
- name: framerate
dtype: float64
- name: r18
dtype: int64
splits:
- name: train
num_bytes: 16755833083
num_examples: 14394510
download_size: 5410262648
dataset_size: 16755833083
task_categories:
- text-to-video
- video-classification
language:
- en
tags:
- captions
- metadata
pretty_name: CleanVid Map (15M)
size_categories:
- 10M<n<100M
---
# CleanVid Map (15M) 🎥
### TempoFunk Video Generation Project
CleanVid-15M is a large-scale dataset of videos with multiple metadata entries such as:
- Textual Descriptions 📃
- Recording Equipment 📹
- Categories 🔠
- Framerate 🎞️
- Aspect Ratio 📺
CleanVid aim is to improve the quality of WebVid-10M dataset by adding more data and cleaning the dataset by dewatermarking the videos in it.
This dataset includes only the map with the urls and metadata, with 3,694,510 more entries than the original WebVid-10M dataset.
Note that the videos are low-resolution, ranging from 240p to 480p. But this shouldn't be a problem as resolution scaling is difficult in Text-To-Video models.
More Datasets to come for high-res use cases.
CleanVid is the foundation dataset for the TempoFunk Video Generation project.
Built from a crawl of Shutterstock from June 25, 2023.
## Format 📊
- id: Integer (int64) - Shutterstock video ID
- description: String - Description of the video
- duration: Float(64) - Duration of the video in seconds
- aspectratio: String - Aspect Ratio of the video separated by colons (":")
- videourl: String - Video URL for the video in the entry, MP4 format. WEBM format is also available most of the times (by changing the extension at the end of the URL.).
- author: String - JSON-String containing information of the author such as `Recording Equipment`, `Style`, `Nationality` and others.
- categories: String - JSON-String containing the categories of the videos. (Values from shutterstock, not by us.)
- framerate: Float(64) - Framerate of the video
- r18: Bit (int64) - Wether the video is marked as mature content. 0 = Safe For Work; 1 = Mature Content
## Code 👩💻
If you want to re-create this dataset on your own, code is available here:
https://github.com/chavinlo/tempofunk-scrapper/tree/refractor1/sites/shutterstock
Due to rate-limitations, you might need to obtain a proxy. Functionality for proxies is included in the repository.
## Sample 🧪
```json
{
"id": 1056934082,
"description": "Rio, Brazil - February 24, 2020: parade of the samba school Mangueira, at the Marques de Sapucai Sambodromo",
"duration": 9.76,
"aspectratio": "16:9",
"videourl": "https://www.shutterstock.com/shutterstock/videos/1056934082/preview/stock-footage-rio-brazil-february-parade-of-the-samba-school-mangueira-at-the-marques-de-sapucai.mp4",
"author": {
"accountsId": 101974372,
"contributorId": 62154,
"bio": "Sempre produzindo mais",
"location": "br",
"website": "www.dcpress.com.br",
"contributorTypeList": [
"photographer"
],
"equipmentList": [
"300mm f2.8",
"24-70mm",
"70-200mm",
"Nikon D7500 ",
"Nikon Df",
"Flashs Godox"
],
"styleList": [
"editorial",
"food",
"landscape"
],
"subjectMatterList": [
"photographer",
"people",
"nature",
"healthcare",
"food_and_drink"
],
"facebookUsername": "celso.pupo",
"googlePlusUsername": "celsopupo",
"twitterUsername": "celsopupo",
"storageKey": "/contributors/62154/avatars/thumb.jpg",
"cdnThumbPath": "/contributors/62154/avatars/thumb.jpg",
"displayName": "Celso Pupo",
"vanityUrlUsername": "rodrigues",
"portfolioUrlSuffix": "rodrigues",
"portfolioUrl": "https://www.shutterstock.com/g/rodrigues",
"instagramUsername": "celsopupo",
"hasPublicSets": true,
"instagramUrl": "https://www.instagram.com/celsopupo",
"facebookUrl": "https://www.facebook.com/celso.pupo",
"twitterUrl": "https://twitter.com/celsopupo"
},
"categories": [
"People"
],
"framerate": 29.97,
"r18": 0
}
```
## Credits 👥
### Main
- Lopho - Part of TempoFunk Video Generation
- Chavinlo - Part of TempoFunk Video Generation & CleanVid Crawling, Scraping and Formatting
```
@InProceedings{Bain21,
author = "Max Bain and Arsha Nagrani and G{\"u}l Varol and Andrew Zisserman",
title = "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval",
booktitle = "IEEE International Conference on Computer Vision",
year = "2021",
}
```
### Extra
- Salt - Base Threading Code (2022) |
bbaaaa/iwslt14-de-en | ---
annotations_creators:
- crowdsourced
language:
- de
- en
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: IWSLT 2014
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: iwslt-2014
---
# Dataset Card for IWSLT 2014
## Dataset Description
- **Homepage:** [https://sites.google.com/site/iwsltevaluation2014](https://sites.google.com/site/iwsltevaluation2014)
dataset_info:
- config_name: de-en
features:
- name: translation
languages:
- de
- en
splits:
- name: train
num_examples: 171721
- name: test
num_examples: 4698
- name: validation
num_examples: 887
|
iocuydi/amharic-dolly-15k | ---
license: cc-by-sa-3.0
---
Amharic version of the Dolly dataset (https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm)
Translated with this https://github.com/iocuydi/amharic-llama-llava/blob/main/data/prepare_amharic_data.py
More details: https://arxiv.org/abs/2403.06354 |
SINAI/COAH | ---
language:
- es
license: cc-by-nc-sa-4.0
---
# COAH
## Descripción
Corpus de opiniones de hoteles destinado a la investigación en el ámbito de la clasificación de la polaridad a nivel de documento, y se circunscribe en el dominio de alojamiento hotelero (turismo-hoteles). El corpus está formado por 1816 opiniones extraídas de TripAdvisor, las cuales están catalogadas en una escala de cinco niveles de opinión (1 (negativo) – 5 (positivo)). El número de opiniones por clase es:
| Puntuación | 1 | 2 | 3 | 4 | 5 | Total |
|------------ |:---:|:---:|:---:|:---:|:---:|:---: |
| Num. Opiniones | 312 | 199 | 285 | 489 | 531 | 1816 |
Algunos datos lingüísticos del corpus son:
| Característica | Dato |
| --- | ---: |
| Número de opiniones | 1816 |
| Número de tokens | 272446 |
| Número de palabras | 239749 |
| Número de palabras únicas | 154297 |
| Diversidad léxica | 0,6435 |
| Número de caracteres | 1372737 |
| Número de caracteres sin espacios | 1135306 |
| Número de nombres | 55530 |
| Número de verbos | 40318 |
| Número de adjetivos | 19935 |
| Número de adverbios | 16629 |
| Número de lemas | 239749 |
| Número de lemas únicos | 138549 |
| Diversidad de lemmas | 0,577 |
| Número de sentidos | 106205 |
| Número de sentidos únicos | 77397 |
| Longitud media de sentencia | 23,245 |
| Número medio de nombres | 0,231 |
| Número medio de verbos | 0,168 |
| Número medio de adjetivos | 0,083 |
| Número medio de adverbios | 0,069 |
## Cómo citar
Molina-González, M. D., Martínez-Cámara, E., Martín-Valdivia, M. T., Ureña-López, L. A. (2014). Cross-domain sentiment analysis using spanish opinionated words. Natural Language Processing and Information Systems, Lecture Notes in Computer Science, vol. 8455, pp. 214-219. Springer International Publishing. DOI: [10.1007/978-3-319-07983-7_28](http://dx.doi.org/10.1007/978-3-319-07983-7_28)
```
@InProceedings{10.1007/978-3-319-07983-7_28,
author="Molina-Gonz{\'a}lez, M. Dolores
and Mart{\'i}nez-C{\'a}mara, Eugenio
and Mart{\'i}n-Valdivia, M. Teresa
and Ure{\~{n}}a-L{\'o}pez, L. Alfonso",
editor="M{\'e}tais, Elisabeth
and Roche, Mathieu
and Teisseire, Maguelonne",
title="Cross-Domain Sentiment Analysis Using Spanish Opinionated Words",
booktitle="Natural Language Processing and Information Systems",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="214--219",
isbn="978-3-319-07983-7"
}
``` |
zoohun/low_test_small_dataset | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12674
num_examples: 49
download_size: 7188
dataset_size: 12674
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sleoruiz/discursos-cuarta-class-separated-by-idx | ---
dataset_info:
features:
- name: text
dtype: string
- name: name
dtype: string
- name: comision
dtype: string
- name: gaceta_numero
dtype: string
- name: fecha_gaceta
dtype: string
- name: labels
sequence: string
- name: scores
sequence: float64
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 8401183
num_examples: 5661
download_size: 3939766
dataset_size: 8401183
---
# Dataset Card for "discursos-cuarta-class-separated-by-idx"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Ba2han__BruinsV2-OpHermesNeu-11B | ---
pretty_name: Evaluation run of Ba2han/BruinsV2-OpHermesNeu-11B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Ba2han/BruinsV2-OpHermesNeu-11B](https://huggingface.co/Ba2han/BruinsV2-OpHermesNeu-11B)\
\ 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_Ba2han__BruinsV2-OpHermesNeu-11B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T12:38:08.853335](https://huggingface.co/datasets/open-llm-leaderboard/details_Ba2han__BruinsV2-OpHermesNeu-11B/blob/main/results_2023-12-16T12-38-08.853335.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\": 0.6442797430736888,\n\
\ \"acc_stderr\": 0.032189382292323196,\n \"acc_norm\": 0.646076779376777,\n\
\ \"acc_norm_stderr\": 0.0328357234803993,\n \"mc1\": 0.46266829865361075,\n\
\ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6276115895198878,\n\
\ \"mc2_stderr\": 0.015378567971079934\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6552901023890785,\n \"acc_stderr\": 0.01388881628678211,\n\
\ \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.01362169611917331\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.659928301135232,\n\
\ \"acc_stderr\": 0.0047276480578979235,\n \"acc_norm\": 0.847042421828321,\n\
\ \"acc_norm_stderr\": 0.003592109743628618\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337135,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337135\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n\
\ \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406796,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406796\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"\
acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"\
acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.0315841532404771,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.0315841532404771\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \
\ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465066,\n \
\ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465066\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251735,\n \"\
acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251735\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621115,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621115\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\
\ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\
\ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990945,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990945\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.01385372417092253,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.01385372417092253\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.024332146779134128,\n\
\ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.024332146779134128\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n\
\ \"acc_stderr\": 0.0162690886639594,\n \"acc_norm\": 0.3843575418994413,\n\
\ \"acc_norm_stderr\": 0.0162690886639594\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729474,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729474\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.02623696588115327,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.02623696588115327\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4471968709256845,\n\
\ \"acc_stderr\": 0.012698825252435108,\n \"acc_norm\": 0.4471968709256845,\n\
\ \"acc_norm_stderr\": 0.012698825252435108\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\": 0.6552287581699346,\n \"acc_stderr\": 0.01922832201869664,\n \
\ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.01922832201869664\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.710204081632653,\n \"acc_stderr\": 0.02904308868330433,\n\
\ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.02904308868330433\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.02954774168764004,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.02954774168764004\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46266829865361075,\n\
\ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6276115895198878,\n\
\ \"mc2_stderr\": 0.015378567971079934\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.011350315707462057\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6004548900682335,\n \
\ \"acc_stderr\": 0.01349166029881599\n }\n}\n```"
repo_url: https://huggingface.co/Ba2han/BruinsV2-OpHermesNeu-11B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|arc:challenge|25_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|gsm8k|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hellaswag|10_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T12-38-08.853335.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T12-38-08.853335.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- '**/details_harness|winogrande|5_2023-12-16T12-38-08.853335.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T12-38-08.853335.parquet'
- config_name: results
data_files:
- split: 2023_12_16T12_38_08.853335
path:
- results_2023-12-16T12-38-08.853335.parquet
- split: latest
path:
- results_2023-12-16T12-38-08.853335.parquet
---
# Dataset Card for Evaluation run of Ba2han/BruinsV2-OpHermesNeu-11B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Ba2han/BruinsV2-OpHermesNeu-11B](https://huggingface.co/Ba2han/BruinsV2-OpHermesNeu-11B) 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_Ba2han__BruinsV2-OpHermesNeu-11B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T12:38:08.853335](https://huggingface.co/datasets/open-llm-leaderboard/details_Ba2han__BruinsV2-OpHermesNeu-11B/blob/main/results_2023-12-16T12-38-08.853335.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": 0.6442797430736888,
"acc_stderr": 0.032189382292323196,
"acc_norm": 0.646076779376777,
"acc_norm_stderr": 0.0328357234803993,
"mc1": 0.46266829865361075,
"mc1_stderr": 0.017454645150970588,
"mc2": 0.6276115895198878,
"mc2_stderr": 0.015378567971079934
},
"harness|arc:challenge|25": {
"acc": 0.6552901023890785,
"acc_stderr": 0.01388881628678211,
"acc_norm": 0.6808873720136519,
"acc_norm_stderr": 0.01362169611917331
},
"harness|hellaswag|10": {
"acc": 0.659928301135232,
"acc_stderr": 0.0047276480578979235,
"acc_norm": 0.847042421828321,
"acc_norm_stderr": 0.003592109743628618
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.04115324610336953,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.04115324610336953
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337135
},
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"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
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"acc_norm": 0.46,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
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"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
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"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.04959859966384181
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.548936170212766,
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"acc_norm_stderr": 0.032529096196131965
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
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"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.025446365634406796
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"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.75,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6743589743589744,
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"acc_norm": 0.6743589743589744,
"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.36666666666666664,
"acc_norm_stderr": 0.029381620726465066
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
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"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.03006676158297793
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33112582781456956,
"acc_stderr": 0.038425817186598696,
"acc_norm": 0.33112582781456956,
"acc_norm_stderr": 0.038425817186598696
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8495412844036697,
"acc_stderr": 0.015328563932669237,
"acc_norm": 0.8495412844036697,
"acc_norm_stderr": 0.015328563932669237
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5416666666666666,
"acc_stderr": 0.03398110890294636,
"acc_norm": 0.5416666666666666,
"acc_norm_stderr": 0.03398110890294636
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7745098039215687,
"acc_stderr": 0.029331162294251735,
"acc_norm": 0.7745098039215687,
"acc_norm_stderr": 0.029331162294251735
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.025955020841621115,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621115
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6681614349775785,
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"acc_norm": 0.6681614349775785,
"acc_norm_stderr": 0.031602951437766785
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm_stderr": 0.03641297081313729
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
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"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990945
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
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"acc_norm_stderr": 0.042365112580946315
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.7423312883435583,
"acc_norm_stderr": 0.03436150827846917
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
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"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
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"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179333
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.7138728323699421,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3843575418994413,
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"acc_norm_stderr": 0.0162690886639594
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
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"acc_norm": 0.7352941176470589,
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
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"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.02623696588115327
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7314814814814815,
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"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.024659685185967284
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4787234042553192,
"acc_stderr": 0.029800481645628693,
"acc_norm": 0.4787234042553192,
"acc_norm_stderr": 0.029800481645628693
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4471968709256845,
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"acc_norm": 0.4471968709256845,
"acc_norm_stderr": 0.012698825252435108
},
"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": 0.6552287581699346,
"acc_stderr": 0.01922832201869664,
"acc_norm": 0.6552287581699346,
"acc_norm_stderr": 0.01922832201869664
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.710204081632653,
"acc_stderr": 0.02904308868330433,
"acc_norm": 0.710204081632653,
"acc_norm_stderr": 0.02904308868330433
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.024845753212306046,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.024845753212306046
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.038786267710023595,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.038786267710023595
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.02954774168764004,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.02954774168764004
},
"harness|truthfulqa:mc|0": {
"mc1": 0.46266829865361075,
"mc1_stderr": 0.017454645150970588,
"mc2": 0.6276115895198878,
"mc2_stderr": 0.015378567971079934
},
"harness|winogrande|5": {
"acc": 0.7947908445146015,
"acc_stderr": 0.011350315707462057
},
"harness|gsm8k|5": {
"acc": 0.6004548900682335,
"acc_stderr": 0.01349166029881599
}
}
```
## 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] |
katarinagresova/Genomic_Benchmarks_demo_human_or_worm | ---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 15900000
num_examples: 75000
- name: test
num_bytes: 5300000
num_examples: 25000
download_size: 2380379
dataset_size: 21200000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "Genomic_Benchmarks_demo_human_or_worm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/beir_msmarco_test | ---
pretty_name: '`beir/msmarco/test`'
viewer: false
source_datasets: ['irds/beir_msmarco']
task_categories:
- text-retrieval
---
# Dataset Card for `beir/msmarco/test`
The `beir/msmarco/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/msmarco/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=43
- `qrels`: (relevance assessments); count=9,260
- For `docs`, use [`irds/beir_msmarco`](https://huggingface.co/datasets/irds/beir_msmarco)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/beir_msmarco_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/beir_msmarco_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Craswell2019TrecDl,
title={Overview of the TREC 2019 deep learning track},
author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos and Ellen Voorhees},
booktitle={TREC 2019},
year={2019}
}
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
@article{Thakur2021Beir,
title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
journal= "arXiv preprint arXiv:2104.08663",
month = "4",
year = "2021",
url = "https://arxiv.org/abs/2104.08663",
}
```
|
tddschn/tutorial | ---
configs:
- config_name: default
data_files:
- split: train
path: "train.csv"
- split: test
path: "test.csv"
- config_name: all
data_files: "*.csv"
language:
- en
tags:
- not-for-all-audiences
pretty_name: Tutorial Dataset
size_categories:
- n<1K
--- |
ashwinperti/yelpnew | ---
license: eupl-1.1
---
|
andrewatef/QAar | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5230.0
num_examples: 49
download_size: 4781
dataset_size: 5230.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_wnli_null_referential_pronouns | ---
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: 9770
num_examples: 55
- name: test
num_bytes: 24726
num_examples: 94
- name: train
num_bytes: 86736
num_examples: 493
download_size: 47619
dataset_size: 121232
---
# Dataset Card for "MULTI_VALUE_wnli_null_referential_pronouns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aops02/MetaMath-Vi | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 72172855
num_examples: 32972
download_size: 15771804
dataset_size: 72172855
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "MetaMath-Vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shirsh10mall/LLM_Instruct_Learning_Project_Preprocessed_Tokenized_Open_Orca_Dataset_Flan_T5 | ---
dataset_info:
features:
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: Inputs Token length
dtype: int64
- name: Response Token length
dtype: int64
splits:
- name: train
num_bytes: 1283943963.5926845
num_examples: 430318
- name: test
num_bytes: 226579926.12734038
num_examples: 75939
download_size: 588711752
dataset_size: 1510523889.7200248
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "temp_data_LLM_Project"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hearmeneigh/e621-rising-v3-curated | ---
dataset_info:
features:
- name: source_id
dtype: string
- name: source
dtype: string
- name: image
dtype: image
- name: tags
sequence: string
- name: url
dtype: string
- name: text
dtype: string
- name: selector
dtype: string
splits:
- name: train
num_bytes: 53726659168.0
num_examples: 279296
download_size: 53423627875
dataset_size: 53726659168.0
pretty_name: 'E621 Rising V3 Image Dataset'
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- furry
- anthro
- nsfw
- e621
- booru
- imagebooru
- imageboard
- gelbooru
- danbooru
- rule34
- not-for-all-audiences
---
<div style='background: #ffeef1; border: 1px solid #fd91a4; padding:1em; border-radius:3px; margin-bottom:2em;'>
<h3 style='margin:0'>NSFW</h3>
<p style='margin:0'>This dataset is not suitable for use by minors. The dataset contains X-rated/NFSW content.</p>
</div>
# E621 Rising V3: Curated Image Dataset
* **279,296** images (53GB) downloaded from `e621.net` (90% of samples), `gelbooru.com`, `danbooru.com`, and `rule34.xxx`
* **6,820** [tags](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-name.json)
* Used to train [E621 Rising v3](https://huggingface.co/hearmeneigh/e621-rising-v3) SDXL model
This dataset was created with [Dataset Rising](https://github.com/hearmeneigh/dataset-rising) toolchain and a [custom configuration](https://github.com/hearmeneigh/e621-rising-configs).
You can use these tools to train your own version!
## Image Processing
* Only `jpg` and `png` images were considered
* Image width and height have been clamped to `(0, 1024]px`; larger images have been resized to meet the limit
* Alpha channels have been removed
* All images have been converted to `jpg` format
* All images have been converted to TrueColor `RGB`
* All images have been verified to load with `Pillow`
* Metadata from E621 is [available here](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data)
## Tags
Comprehensive list of 6,820 tags and counts:
* [By name](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-name.json)
* [By count](https://huggingface.co/datasets/hearmeneigh/e621-rising-v3-preliminary-data/blob/main/tag-counts.by-count.json)
### Additional Tags
* `rating_explicit`
* `rating_questionable`
* `rating_safe`
* `rising_masterpiece`
* `rising_unpopular`
* `favorites_below_X` (25, 50, 100, 250, 500, 1000)
* `favorites_above_X` (250, 500, 1000, 2000, 3000, 4000)
* `score_below_X` (0, 25, 50, 100, 250, 500)
* `score_above_X` (100, 250, 500, 1000, 1500, 2000)
|
linhqyy/result_with_w2v2_originspknorm | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: id
dtype: string
- name: w2v2_baseline_transcription
dtype: string
- name: w2v2_baseline_norm
dtype: string
splits:
- name: train
num_bytes: 174371835.027
num_examples: 1299
download_size: 164200997
dataset_size: 174371835.027
---
# Dataset Card for "result_with_w2v2_originspknorm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yuvalkirstain/dreambooth_prior_reg_images | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 44656947.0
num_examples: 100
download_size: 44658302
dataset_size: 44656947.0
---
# Dataset Card for "dreambooth_prior_reg_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davidgasquez/spain_energy_demand | ---
dataset_info:
features:
- name: value
dtype: int64
- name: datetime
dtype: timestamp[us, tz=Etc/UTC]
splits:
- name: main
num_bytes: 10259824
num_examples: 641239
download_size: 8451864
dataset_size: 10259824
configs:
- config_name: default
data_files:
- split: main
path: data/main-*
---
|
autoevaluate/autoeval-staging-eval-project-xsum-69daf1dd-12935737 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. |
dolphinschao/fill5 | ---
license: apache-2.0
---
|
ProgComp/NeuripsHS | ---
license: mit
language:
- hi
- en
- bh
- as
- pa
task_categories:
- translation
---
Dataset comes In 3 parts:
- base data: CulturaX/webscrapes
- Instruct: AlpacaGPT4 hindi
- FT: multiple for tone and dialect |
liuyanchen1015/MULTI_VALUE_qqp_simple_past_for_present_perfect | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 274479
num_examples: 1494
- name: test
num_bytes: 2605532
num_examples: 13910
- name: train
num_bytes: 2463487
num_examples: 13233
download_size: 3246396
dataset_size: 5343498
---
# Dataset Card for "MULTI_VALUE_qqp_simple_past_for_present_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
causal-lm/hh-rlhf | ---
language: en
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 338350393
num_examples: 475599
- name: validation
num_bytes: 37949876
num_examples: 52845
download_size: 228121336
dataset_size: 376300269
---
# Dataset Card for "hh-rlhf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OK-ok1212/dataset | ---
license: mit
---
|
CVasNLPExperiments/CIFAR100_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 406640
num_examples: 1000
download_size: 131659
dataset_size: 406640
---
# Dataset Card for "CIFAR100_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lime8817/reg_images | ---
license: creativeml-openrail-m
---
|
samop/bloom | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2196528.0
num_examples: 268
- name: test
num_bytes: 245880.0
num_examples: 30
download_size: 1127825
dataset_size: 2442408.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "bloom"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/snips_chatgpt_5pct_v0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1069201
num_examples: 13084
download_size: 415667
dataset_size: 1069201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_50_1713170253 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 388928
num_examples: 901
download_size: 189696
dataset_size: 388928
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ranchlai/nips2023-dataset | ---
license: mit
---
|
open-llm-leaderboard/details_Mikivis__xuanxuan | ---
pretty_name: Evaluation run of Mikivis/xuanxuan
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Mikivis/xuanxuan](https://huggingface.co/Mikivis/xuanxuan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Mikivis__xuanxuan\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-16T21:42:00.993318](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__xuanxuan/blob/main/results_2023-09-16T21-42-00.993318.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\": 0.008389261744966443,\n\
\ \"em_stderr\": 0.000934054321686696,\n \"f1\": 0.05742869127516786,\n\
\ \"f1_stderr\": 0.0015884226243297857,\n \"acc\": 0.2521704814522494,\n\
\ \"acc_stderr\": 0.00702597803203845\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008389261744966443,\n \"em_stderr\": 0.000934054321686696,\n\
\ \"f1\": 0.05742869127516786,\n \"f1_stderr\": 0.0015884226243297857\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5043409629044988,\n\
\ \"acc_stderr\": 0.0140519560640769\n }\n}\n```"
repo_url: https://huggingface.co/Mikivis/xuanxuan
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|arc:challenge|25_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T21_42_00.993318
path:
- '**/details_harness|drop|3_2023-09-16T21-42-00.993318.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-16T21-42-00.993318.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T21_42_00.993318
path:
- '**/details_harness|gsm8k|5_2023-09-16T21-42-00.993318.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-16T21-42-00.993318.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hellaswag|10_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T13:14:51.241896.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T13:14:51.241896.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T13:14:51.241896.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_16T21_42_00.993318
path:
- '**/details_harness|winogrande|5_2023-09-16T21-42-00.993318.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-16T21-42-00.993318.parquet'
- config_name: results
data_files:
- split: 2023_09_01T13_14_51.241896
path:
- results_2023-09-01T13:14:51.241896.parquet
- split: 2023_09_16T21_42_00.993318
path:
- results_2023-09-16T21-42-00.993318.parquet
- split: latest
path:
- results_2023-09-16T21-42-00.993318.parquet
---
# Dataset Card for Evaluation run of Mikivis/xuanxuan
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Mikivis/xuanxuan
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Mikivis/xuanxuan](https://huggingface.co/Mikivis/xuanxuan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Mikivis__xuanxuan",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T21:42:00.993318](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__xuanxuan/blob/main/results_2023-09-16T21-42-00.993318.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": 0.008389261744966443,
"em_stderr": 0.000934054321686696,
"f1": 0.05742869127516786,
"f1_stderr": 0.0015884226243297857,
"acc": 0.2521704814522494,
"acc_stderr": 0.00702597803203845
},
"harness|drop|3": {
"em": 0.008389261744966443,
"em_stderr": 0.000934054321686696,
"f1": 0.05742869127516786,
"f1_stderr": 0.0015884226243297857
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5043409629044988,
"acc_stderr": 0.0140519560640769
}
}
```
### 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] |
sayan1101/identity_finetune_data_2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 387168
num_examples: 1181
- name: test
num_bytes: 66396
num_examples: 209
download_size: 221210
dataset_size: 453564
---
# Dataset Card for "identity_finetune_data_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pszemraj/scientific_lay_summarisation-elife-norm | ---
license: mit
task_categories:
- summarization
- text2text-generation
language:
- en
size_categories:
- 10K<n<100K
source_datasets: tomasg25/scientific_lay_summarisation
---
# scientific_lay_summarisation - elife - normalized
This is the "_elife_" split. For more words, refer to the [PLOS split README](https://huggingface.co/datasets/pszemraj/scientific_lay_summarisation-plos-norm)
## Contents
load with datasets:
```python
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("pszemraj/scientific_lay_summarisation-elife-norm")
dataset
```
Output:
```python
DatasetDict({
train: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 4346
})
test: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
validation: Dataset({
features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
num_rows: 241
})
})
```
## Lengths
Train set:
|
jacobbieker/aeronet | ---
license: mit
---
|
xinip/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
dtype: 'null'
- name: comments
sequence: string
- 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: 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: 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: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 777968
num_examples: 100
download_size: 293534
dataset_size: 777968
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
thewalkerdenton/Canny | ---
license: apache-2.0
---
|
TheDKBR/thedk | ---
license: openrail
---
|
jdabello/products | ---
license: apache-2.0
---
|
katarinagresova/Genomic_Benchmarks_dummy_mouse_enhancers_ensembl | ---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2273646
num_examples: 968
- name: test
num_bytes: 608062
num_examples: 242
download_size: 294310
dataset_size: 2881708
---
# Dataset Card for "Genomic_Benchmarks_dummy_mouse_enhancers_ensembl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mHossain/final_train_v4_test_780000 | ---
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: 6683220.9
num_examples: 18000
- name: test
num_bytes: 742580.1
num_examples: 2000
download_size: 3207945
dataset_size: 7425801.0
---
# Dataset Card for "final_train_v4_test_780000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-0.1 | ---
pretty_name: Evaluation run of Mihaiii/Pallas-0.5-LASER-0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Mihaiii/Pallas-0.5-LASER-0.1](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.1)\
\ 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_Mihaiii__Pallas-0.5-LASER-0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-05T01:49:24.518442](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-0.1/blob/main/results_2024-01-05T01-49-24.518442.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\": 0.7442434586660535,\n\
\ \"acc_stderr\": 0.02895658706740122,\n \"acc_norm\": 0.7490694764588209,\n\
\ \"acc_norm_stderr\": 0.02950295988554605,\n \"mc1\": 0.4149326805385557,\n\
\ \"mc1_stderr\": 0.017248314465805978,\n \"mc2\": 0.567845170456361,\n\
\ \"mc2_stderr\": 0.015750522408858988\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n\
\ \"acc_norm\": 0.6467576791808873,\n \"acc_norm_stderr\": 0.013967822714840056\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6425014937263493,\n\
\ \"acc_stderr\": 0.004782838352222523,\n \"acc_norm\": 0.8348934475204143,\n\
\ \"acc_norm_stderr\": 0.003705179029287334\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930394,\n\
\ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930394\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7924528301886793,\n \"acc_stderr\": 0.024959918028911267,\n\
\ \"acc_norm\": 0.7924528301886793,\n \"acc_norm_stderr\": 0.024959918028911267\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n\
\ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \
\ \"acc_norm_stderr\": 0.02765610492929436\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\"\
: 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\
\ \"acc_stderr\": 0.033917503223216586,\n \"acc_norm\": 0.7283236994219653,\n\
\ \"acc_norm_stderr\": 0.033917503223216586\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7702127659574468,\n \"acc_stderr\": 0.02750175294441242,\n\
\ \"acc_norm\": 0.7702127659574468,\n \"acc_norm_stderr\": 0.02750175294441242\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n\
\ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n\
\ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7241379310344828,\n \"acc_stderr\": 0.03724563619774632,\n\
\ \"acc_norm\": 0.7241379310344828,\n \"acc_norm_stderr\": 0.03724563619774632\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.6693121693121693,\n \"acc_stderr\": 0.024229965298425096,\n \"\
acc_norm\": 0.6693121693121693,\n \"acc_norm_stderr\": 0.024229965298425096\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.017066403719657255,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.017066403719657255\n \
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6650246305418719,\n \"acc_stderr\": 0.033208527423483104,\n \"\
acc_norm\": 0.6650246305418719,\n \"acc_norm_stderr\": 0.033208527423483104\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\"\
: 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.028887872395487946,\n\
\ \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.028887872395487946\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9141414141414141,\n \"acc_stderr\": 0.01996022556317289,\n \"\
acc_norm\": 0.9141414141414141,\n \"acc_norm_stderr\": 0.01996022556317289\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\
\ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7974358974358975,\n \"acc_stderr\": 0.02037766097037139,\n \
\ \"acc_norm\": 0.7974358974358975,\n \"acc_norm_stderr\": 0.02037766097037139\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.42962962962962964,\n \"acc_stderr\": 0.030182099804387262,\n \
\ \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.030182099804387262\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8277310924369747,\n \"acc_stderr\": 0.024528664971305424,\n\
\ \"acc_norm\": 0.8277310924369747,\n \"acc_norm_stderr\": 0.024528664971305424\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"\
acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9137614678899083,\n \"acc_stderr\": 0.012035597300116245,\n \"\
acc_norm\": 0.9137614678899083,\n \"acc_norm_stderr\": 0.012035597300116245\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293647,\n \"\
acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293647\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073315,\n \"\
acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073315\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065508,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065508\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7937219730941704,\n\
\ \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n\
\ \"acc_norm_stderr\": 0.02715715047956382\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"\
acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.035207039905179635,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.035207039905179635\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n\
\ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\
\ \"acc_stderr\": 0.01700436856813235,\n \"acc_norm\": 0.9273504273504274,\n\
\ \"acc_norm_stderr\": 0.01700436856813235\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.9029374201787995,\n\
\ \"acc_stderr\": 0.010586474712018292,\n \"acc_norm\": 0.9029374201787995,\n\
\ \"acc_norm_stderr\": 0.010586474712018292\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.021029269752423224,\n\
\ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.021029269752423224\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6670391061452514,\n\
\ \"acc_stderr\": 0.015761716178397563,\n \"acc_norm\": 0.6670391061452514,\n\
\ \"acc_norm_stderr\": 0.015761716178397563\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.0227337894054476,\n\
\ \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.0227337894054476\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7813504823151125,\n\
\ \"acc_stderr\": 0.02347558141786111,\n \"acc_norm\": 0.7813504823151125,\n\
\ \"acc_norm_stderr\": 0.02347558141786111\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\
\ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5921985815602837,\n \"acc_stderr\": 0.029316011776343562,\n \
\ \"acc_norm\": 0.5921985815602837,\n \"acc_norm_stderr\": 0.029316011776343562\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5840938722294654,\n\
\ \"acc_stderr\": 0.012588323850313594,\n \"acc_norm\": 0.5840938722294654,\n\
\ \"acc_norm_stderr\": 0.012588323850313594\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7977941176470589,\n \"acc_stderr\": 0.024398192986654924,\n\
\ \"acc_norm\": 0.7977941176470589,\n \"acc_norm_stderr\": 0.024398192986654924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.795751633986928,\n \"acc_stderr\": 0.016309755848361526,\n \
\ \"acc_norm\": 0.795751633986928,\n \"acc_norm_stderr\": 0.016309755848361526\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n\
\ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\
\ \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n\
\ \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.027265992434429103,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.027265992434429103\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4149326805385557,\n\
\ \"mc1_stderr\": 0.017248314465805978,\n \"mc2\": 0.567845170456361,\n\
\ \"mc2_stderr\": 0.015750522408858988\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8129439621152328,\n \"acc_stderr\": 0.010959716435242912\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6019711902956786,\n \
\ \"acc_stderr\": 0.013483026939074823\n }\n}\n```"
repo_url: https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|arc:challenge|25_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|gsm8k|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hellaswag|10_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T01-49-24.518442.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T01-49-24.518442.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- '**/details_harness|winogrande|5_2024-01-05T01-49-24.518442.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-05T01-49-24.518442.parquet'
- config_name: results
data_files:
- split: 2024_01_05T01_49_24.518442
path:
- results_2024-01-05T01-49-24.518442.parquet
- split: latest
path:
- results_2024-01-05T01-49-24.518442.parquet
---
# Dataset Card for Evaluation run of Mihaiii/Pallas-0.5-LASER-0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Mihaiii/Pallas-0.5-LASER-0.1](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.1) 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_Mihaiii__Pallas-0.5-LASER-0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-05T01:49:24.518442](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Pallas-0.5-LASER-0.1/blob/main/results_2024-01-05T01-49-24.518442.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": 0.7442434586660535,
"acc_stderr": 0.02895658706740122,
"acc_norm": 0.7490694764588209,
"acc_norm_stderr": 0.02950295988554605,
"mc1": 0.4149326805385557,
"mc1_stderr": 0.017248314465805978,
"mc2": 0.567845170456361,
"mc2_stderr": 0.015750522408858988
},
"harness|arc:challenge|25": {
"acc": 0.6279863481228669,
"acc_stderr": 0.014124597881844461,
"acc_norm": 0.6467576791808873,
"acc_norm_stderr": 0.013967822714840056
},
"harness|hellaswag|10": {
"acc": 0.6425014937263493,
"acc_stderr": 0.004782838352222523,
"acc_norm": 0.8348934475204143,
"acc_norm_stderr": 0.003705179029287334
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8552631578947368,
"acc_stderr": 0.028631951845930394,
"acc_norm": 0.8552631578947368,
"acc_norm_stderr": 0.028631951845930394
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7924528301886793,
"acc_stderr": 0.024959918028911267,
"acc_norm": 0.7924528301886793,
"acc_norm_stderr": 0.024959918028911267
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"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": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7283236994219653,
"acc_stderr": 0.033917503223216586,
"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.033917503223216586
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.04971358884367406,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367406
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7702127659574468,
"acc_stderr": 0.02750175294441242,
"acc_norm": 0.7702127659574468,
"acc_norm_stderr": 0.02750175294441242
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.6052631578947368,
"acc_stderr": 0.045981880578165414,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.045981880578165414
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7241379310344828,
"acc_stderr": 0.03724563619774632,
"acc_norm": 0.7241379310344828,
"acc_norm_stderr": 0.03724563619774632
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6693121693121693,
"acc_stderr": 0.024229965298425096,
"acc_norm": 0.6693121693121693,
"acc_norm_stderr": 0.024229965298425096
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04444444444444449,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04444444444444449
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9,
"acc_stderr": 0.017066403719657255,
"acc_norm": 0.9,
"acc_norm_stderr": 0.017066403719657255
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6650246305418719,
"acc_stderr": 0.033208527423483104,
"acc_norm": 0.6650246305418719,
"acc_norm_stderr": 0.033208527423483104
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.81,
"acc_stderr": 0.03942772444036625,
"acc_norm": 0.81,
"acc_norm_stderr": 0.03942772444036625
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8363636363636363,
"acc_stderr": 0.028887872395487946,
"acc_norm": 0.8363636363636363,
"acc_norm_stderr": 0.028887872395487946
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9141414141414141,
"acc_stderr": 0.01996022556317289,
"acc_norm": 0.9141414141414141,
"acc_norm_stderr": 0.01996022556317289
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9740932642487047,
"acc_stderr": 0.01146452335695318,
"acc_norm": 0.9740932642487047,
"acc_norm_stderr": 0.01146452335695318
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7974358974358975,
"acc_stderr": 0.02037766097037139,
"acc_norm": 0.7974358974358975,
"acc_norm_stderr": 0.02037766097037139
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.42962962962962964,
"acc_stderr": 0.030182099804387262,
"acc_norm": 0.42962962962962964,
"acc_norm_stderr": 0.030182099804387262
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8277310924369747,
"acc_stderr": 0.024528664971305424,
"acc_norm": 0.8277310924369747,
"acc_norm_stderr": 0.024528664971305424
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.47019867549668876,
"acc_stderr": 0.040752249922169775,
"acc_norm": 0.47019867549668876,
"acc_norm_stderr": 0.040752249922169775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9137614678899083,
"acc_stderr": 0.012035597300116245,
"acc_norm": 0.9137614678899083,
"acc_norm_stderr": 0.012035597300116245
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6481481481481481,
"acc_stderr": 0.03256850570293647,
"acc_norm": 0.6481481481481481,
"acc_norm_stderr": 0.03256850570293647
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9313725490196079,
"acc_stderr": 0.017744453647073315,
"acc_norm": 0.9313725490196079,
"acc_norm_stderr": 0.017744453647073315
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8987341772151899,
"acc_stderr": 0.019637720526065508,
"acc_norm": 0.8987341772151899,
"acc_norm_stderr": 0.019637720526065508
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7937219730941704,
"acc_stderr": 0.02715715047956382,
"acc_norm": 0.7937219730941704,
"acc_norm_stderr": 0.02715715047956382
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8473282442748091,
"acc_stderr": 0.031545216720054725,
"acc_norm": 0.8473282442748091,
"acc_norm_stderr": 0.031545216720054725
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.9008264462809917,
"acc_stderr": 0.02728524631275896,
"acc_norm": 0.9008264462809917,
"acc_norm_stderr": 0.02728524631275896
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8425925925925926,
"acc_stderr": 0.035207039905179635,
"acc_norm": 0.8425925925925926,
"acc_norm_stderr": 0.035207039905179635
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8711656441717791,
"acc_stderr": 0.026321383198783674,
"acc_norm": 0.8711656441717791,
"acc_norm_stderr": 0.026321383198783674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.5089285714285714,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.03586594738573974,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.03586594738573974
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9273504273504274,
"acc_stderr": 0.01700436856813235,
"acc_norm": 0.9273504273504274,
"acc_norm_stderr": 0.01700436856813235
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.9029374201787995,
"acc_stderr": 0.010586474712018292,
"acc_norm": 0.9029374201787995,
"acc_norm_stderr": 0.010586474712018292
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8121387283236994,
"acc_stderr": 0.021029269752423224,
"acc_norm": 0.8121387283236994,
"acc_norm_stderr": 0.021029269752423224
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6670391061452514,
"acc_stderr": 0.015761716178397563,
"acc_norm": 0.6670391061452514,
"acc_norm_stderr": 0.015761716178397563
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.0227337894054476,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.0227337894054476
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7813504823151125,
"acc_stderr": 0.02347558141786111,
"acc_norm": 0.7813504823151125,
"acc_norm_stderr": 0.02347558141786111
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8703703703703703,
"acc_stderr": 0.018689725721062072,
"acc_norm": 0.8703703703703703,
"acc_norm_stderr": 0.018689725721062072
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5921985815602837,
"acc_stderr": 0.029316011776343562,
"acc_norm": 0.5921985815602837,
"acc_norm_stderr": 0.029316011776343562
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5840938722294654,
"acc_stderr": 0.012588323850313594,
"acc_norm": 0.5840938722294654,
"acc_norm_stderr": 0.012588323850313594
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7977941176470589,
"acc_stderr": 0.024398192986654924,
"acc_norm": 0.7977941176470589,
"acc_norm_stderr": 0.024398192986654924
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.795751633986928,
"acc_stderr": 0.016309755848361526,
"acc_norm": 0.795751633986928,
"acc_norm_stderr": 0.016309755848361526
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940589,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8326530612244898,
"acc_stderr": 0.02389714476891452,
"acc_norm": 0.8326530612244898,
"acc_norm_stderr": 0.02389714476891452
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8905472636815921,
"acc_stderr": 0.022076326101824664,
"acc_norm": 0.8905472636815921,
"acc_norm_stderr": 0.022076326101824664
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.027265992434429103,
"acc_norm": 0.92,
"acc_norm_stderr": 0.027265992434429103
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015577,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015577
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4149326805385557,
"mc1_stderr": 0.017248314465805978,
"mc2": 0.567845170456361,
"mc2_stderr": 0.015750522408858988
},
"harness|winogrande|5": {
"acc": 0.8129439621152328,
"acc_stderr": 0.010959716435242912
},
"harness|gsm8k|5": {
"acc": 0.6019711902956786,
"acc_stderr": 0.013483026939074823
}
}
```
## 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] |
Sree1994/babylm_100M | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 62663655
num_examples: 255000
- name: test
num_bytes: 7636573
num_examples: 35000
- name: valid
num_bytes: 7636573
num_examples: 35000
download_size: 0
dataset_size: 77936801
---
# Dataset Card for "babylm_100M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
storresbusquets/asr-arg-spanish | ---
license: cc-by-sa-4.0
---
|
cahya/instructions-ur | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1506793.1752371443
num_examples: 1802
- name: test
num_bytes: 84454.00149775337
num_examples: 101
- name: validation
num_bytes: 83617.82326510234
num_examples: 100
download_size: 791422
dataset_size: 1674865.0
---
# Dataset Card for "instructions-ur"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ryan-Pupia/CS482-TaxiDataSetTrain | ---
dataset_info:
features:
- name: key
dtype: string
- name: fare_amount
dtype: float64
- name: pickup_datetime
dtype: string
- name: pickup_longitude
dtype: float64
- name: pickup_latitude
dtype: float64
- name: dropoff_longitude
dtype: float64
- name: dropoff_latitude
dtype: float64
- name: passenger_count
dtype: int64
splits:
- name: train
num_bytes: 5912642536
num_examples: 55423856
download_size: 3775003042
dataset_size: 5912642536
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HachiML/humaneval-ja-v0.6 | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: prompt_ja
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
splits:
- name: test
num_bytes: 274703
num_examples: 164
download_size: 125629
dataset_size: 274703
license: mit
task_categories:
- text2text-generation
language:
- ja
tags:
- code
- code-generation
size_categories:
- n<1K
pretty_name: HumanEval Japanese
source_datasets:
- openai_humaneval
---
# Dataset Card for "humaneval-ja"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shivani-bhoi2002/ProjectDataset | ---
license: unknown
---
|
TheGreatP/DaniloGentili | ---
license: openrail
---
|
open-llm-leaderboard/details_rwitz__dec10 | ---
pretty_name: Evaluation run of rwitz/dec10
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rwitz/dec10](https://huggingface.co/rwitz/dec10) 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 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 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_rwitz__dec10\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T03:10:59.161265](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__dec10/blob/main/results_2023-12-11T03-10-59.161265.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\": 0.6540294607487833,\n\
\ \"acc_stderr\": 0.032048882469360766,\n \"acc_norm\": 0.6541030274313245,\n\
\ \"acc_norm_stderr\": 0.03270870495285761,\n \"mc1\": 0.4504283965728274,\n\
\ \"mc1_stderr\": 0.017417264371967646,\n \"mc2\": 0.6041998017095335,\n\
\ \"mc2_stderr\": 0.015386323767333891\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6638225255972696,\n \"acc_stderr\": 0.013804855026205765,\n\
\ \"acc_norm\": 0.6911262798634812,\n \"acc_norm_stderr\": 0.013501770929344003\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6823341963752241,\n\
\ \"acc_stderr\": 0.004646172373101,\n \"acc_norm\": 0.8645688109938259,\n\
\ \"acc_norm_stderr\": 0.0034148422365171\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6127659574468085,\n \"acc_stderr\": 0.03184389265339526,\n\
\ \"acc_norm\": 0.6127659574468085,\n \"acc_norm_stderr\": 0.03184389265339526\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\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\"\
: 0.4444444444444444,\n \"acc_stderr\": 0.02559185776138219,\n \"\
acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.02559185776138219\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181015,\n \"\
acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181015\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229872,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229872\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131147,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131147\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8605504587155963,\n \"acc_stderr\": 0.014852421490033053,\n \"\
acc_norm\": 0.8605504587155963,\n \"acc_norm_stderr\": 0.014852421490033053\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.02531049537694486,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.02531049537694486\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\
\ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\
\ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39888268156424583,\n\
\ \"acc_stderr\": 0.016376966142610076,\n \"acc_norm\": 0.39888268156424583,\n\
\ \"acc_norm_stderr\": 0.016376966142610076\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\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\": 0.4745762711864407,\n \"acc_stderr\": 0.012753716929101006,\n\
\ \"acc_norm\": 0.4745762711864407,\n \"acc_norm_stderr\": 0.012753716929101006\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146293,\n \"\
acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146293\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.01890101532209309,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.01890101532209309\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\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4504283965728274,\n\
\ \"mc1_stderr\": 0.017417264371967646,\n \"mc2\": 0.6041998017095335,\n\
\ \"mc2_stderr\": 0.015386323767333891\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8074191002367798,\n \"acc_stderr\": 0.011082538847491904\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7058377558756633,\n \
\ \"acc_stderr\": 0.012551285331470152\n }\n}\n```"
repo_url: https://huggingface.co/rwitz/dec10
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|arc:challenge|25_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|arc:challenge|25_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|gsm8k|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|gsm8k|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hellaswag|10_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hellaswag|10_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-08-28.006278.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-10-59.161265.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T03-10-59.161265.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- '**/details_harness|winogrande|5_2023-12-11T03-08-28.006278.parquet'
- split: 2023_12_11T03_10_59.161265
path:
- '**/details_harness|winogrande|5_2023-12-11T03-10-59.161265.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T03-10-59.161265.parquet'
- config_name: results
data_files:
- split: 2023_12_11T03_08_28.006278
path:
- results_2023-12-11T03-08-28.006278.parquet
- split: 2023_12_11T03_10_59.161265
path:
- results_2023-12-11T03-10-59.161265.parquet
- split: latest
path:
- results_2023-12-11T03-10-59.161265.parquet
---
# Dataset Card for Evaluation run of rwitz/dec10
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rwitz/dec10
- **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 [rwitz/dec10](https://huggingface.co/rwitz/dec10) 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 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 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_rwitz__dec10",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T03:10:59.161265](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__dec10/blob/main/results_2023-12-11T03-10-59.161265.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": 0.6540294607487833,
"acc_stderr": 0.032048882469360766,
"acc_norm": 0.6541030274313245,
"acc_norm_stderr": 0.03270870495285761,
"mc1": 0.4504283965728274,
"mc1_stderr": 0.017417264371967646,
"mc2": 0.6041998017095335,
"mc2_stderr": 0.015386323767333891
},
"harness|arc:challenge|25": {
"acc": 0.6638225255972696,
"acc_stderr": 0.013804855026205765,
"acc_norm": 0.6911262798634812,
"acc_norm_stderr": 0.013501770929344003
},
"harness|hellaswag|10": {
"acc": 0.6823341963752241,
"acc_stderr": 0.004646172373101,
"acc_norm": 0.8645688109938259,
"acc_norm_stderr": 0.0034148422365171
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.02783491252754407,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.02783491252754407
},
"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": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
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}
```
### 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] |
roa7n/patched_test_f_SPOUT_ps_50__v2023d | ---
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 1947829
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download_size: 187258
dataset_size: 1947829
---
# Dataset Card for "patched_test_f_SPOUT_ps_50__v2023d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jackielii/spider-partial-exp | ---
configs:
- config_name: default
data_files:
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path: data/train-*
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path: data/test-*
dataset_info:
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download_size: 8986824
dataset_size: 23161096.0
---
# Dataset Card for "spider-partial-exp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danielhanchen/chatlogs | ---
license: apache-2.0
---
|
freshpearYoon/vr_val_free_1 | ---
dataset_info:
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splits:
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num_examples: 10000
download_size: 1543357949
dataset_size: 8562702473
configs:
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data_files:
- split: train
path: data/train-*
---
|
AustinMcMike/Steve_Jobs_ChatML2 | ---
license: apache-2.0
---
|
Oztobuzz/Kosmos_news | ---
configs:
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data_files:
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task_categories:
- image-to-text
language:
- en
- vi
---
# 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. -->
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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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JoshVictor/TEL-Medalpaca-Jo | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1684398
num_examples: 1500
download_size: 826035
dataset_size: 1684398
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_freecs__Zero-7B-test-2 | ---
pretty_name: Evaluation run of freecs/Zero-7B-test-2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [freecs/Zero-7B-test-2](https://huggingface.co/freecs/Zero-7B-test-2) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_freecs__Zero-7B-test-2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-20T18:46:57.239901](https://huggingface.co/datasets/open-llm-leaderboard/details_freecs__Zero-7B-test-2/blob/main/results_2024-01-20T18-46-57.239901.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\": 0.6310493818362459,\n\
\ \"acc_stderr\": 0.032481623842244296,\n \"acc_norm\": 0.6340213840269038,\n\
\ \"acc_norm_stderr\": 0.03313260403421946,\n \"mc1\": 0.42717258261933905,\n\
\ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.5995330460127621,\n\
\ \"mc2_stderr\": 0.015385793036833406\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6143344709897611,\n \"acc_stderr\": 0.014224250973257186,\n\
\ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6450906193985262,\n\
\ \"acc_stderr\": 0.0047750796365670966,\n \"acc_norm\": 0.8477394941246763,\n\
\ \"acc_norm_stderr\": 0.003585389636472374\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419035,\n\
\ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419035\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778398,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778398\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7290322580645161,\n\
\ \"acc_stderr\": 0.025284416114900156,\n \"acc_norm\": 0.7290322580645161,\n\
\ \"acc_norm_stderr\": 0.025284416114900156\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n\
\ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.031584153240477114,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.031584153240477114\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\
\ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977945,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977945\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612907,\n \"\
acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612907\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8284313725490197,\n \"acc_stderr\": 0.02646056956124063,\n \"\
acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.02646056956124063\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\
\ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\
\ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368982,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368982\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\
\ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34972067039106147,\n\
\ \"acc_stderr\": 0.01594930879023364,\n \"acc_norm\": 0.34972067039106147,\n\
\ \"acc_norm_stderr\": 0.01594930879023364\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.025457756696667885,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.025457756696667885\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.026160584450140446,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.026160584450140446\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4589308996088657,\n\
\ \"acc_stderr\": 0.012727084826799798,\n \"acc_norm\": 0.4589308996088657,\n\
\ \"acc_norm_stderr\": 0.012727084826799798\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983576,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983576\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\
\ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n\
\ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.036845294917747066,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.036845294917747066\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42717258261933905,\n\
\ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.5995330460127621,\n\
\ \"mc2_stderr\": 0.015385793036833406\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625849\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5360121304018196,\n \
\ \"acc_stderr\": 0.013736715929950318\n }\n}\n```"
repo_url: https://huggingface.co/freecs/Zero-7B-test-2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|arc:challenge|25_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|gsm8k|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hellaswag|10_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T18-46-57.239901.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-20T18-46-57.239901.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- '**/details_harness|winogrande|5_2024-01-20T18-46-57.239901.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-20T18-46-57.239901.parquet'
- config_name: results
data_files:
- split: 2024_01_20T18_46_57.239901
path:
- results_2024-01-20T18-46-57.239901.parquet
- split: latest
path:
- results_2024-01-20T18-46-57.239901.parquet
---
# Dataset Card for Evaluation run of freecs/Zero-7B-test-2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [freecs/Zero-7B-test-2](https://huggingface.co/freecs/Zero-7B-test-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_freecs__Zero-7B-test-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-20T18:46:57.239901](https://huggingface.co/datasets/open-llm-leaderboard/details_freecs__Zero-7B-test-2/blob/main/results_2024-01-20T18-46-57.239901.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": 0.6310493818362459,
"acc_stderr": 0.032481623842244296,
"acc_norm": 0.6340213840269038,
"acc_norm_stderr": 0.03313260403421946,
"mc1": 0.42717258261933905,
"mc1_stderr": 0.017316834410963926,
"mc2": 0.5995330460127621,
"mc2_stderr": 0.015385793036833406
},
"harness|arc:challenge|25": {
"acc": 0.6143344709897611,
"acc_stderr": 0.014224250973257186,
"acc_norm": 0.6612627986348123,
"acc_norm_stderr": 0.013830568927974332
},
"harness|hellaswag|10": {
"acc": 0.6450906193985262,
"acc_stderr": 0.0047750796365670966,
"acc_norm": 0.8477394941246763,
"acc_norm_stderr": 0.003585389636472374
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438662,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438662
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5404255319148936,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.5404255319148936,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6137931034482759,
"acc_stderr": 0.04057324734419035,
"acc_norm": 0.6137931034482759,
"acc_norm_stderr": 0.04057324734419035
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778398,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778398
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04444444444444449,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04444444444444449
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7290322580645161,
"acc_stderr": 0.025284416114900156,
"acc_norm": 0.7290322580645161,
"acc_norm_stderr": 0.025284416114900156
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.49261083743842365,
"acc_stderr": 0.03517603540361008,
"acc_norm": 0.49261083743842365,
"acc_norm_stderr": 0.03517603540361008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.031584153240477114,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.031584153240477114
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.02985751567338642,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.02985751567338642
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8808290155440415,
"acc_stderr": 0.023381935348121437,
"acc_norm": 0.8808290155440415,
"acc_norm_stderr": 0.023381935348121437
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6384615384615384,
"acc_stderr": 0.024359581465396993,
"acc_norm": 0.6384615384615384,
"acc_norm_stderr": 0.024359581465396993
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524575,
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}
```
## Dataset Details
### Dataset Description
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CyberHarem/koyanskaya_of_light_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of koyanskaya_of_light/光のコヤンスカヤ/光之高扬斯卡娅 (Fate/Grand Order)
This is the dataset of koyanskaya_of_light/光のコヤンスカヤ/光之高扬斯卡娅 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `pink_hair, long_hair, animal_ears, breasts, yellow_eyes, animal_ear_fluff, large_breasts, sidelocks, hair_between_eyes, fox_ears, fox_tail, glasses, tail, fox_girl, bow, hair_bow, ponytail, pink_bow`, 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 | 814.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_light_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 699.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_light_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1276 | 1.35 GiB | [Download](https://huggingface.co/datasets/CyberHarem/koyanskaya_of_light_fgo/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/koyanskaya_of_light_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, solo, choker, cleavage, off_shoulder, collarbone, smile, thighs, long_sleeves, wide_sleeves, black_headwear, very_long_hair, top_hat, white_gloves, open_mouth, thighhighs, black_skirt, holding, kimono, red_coat, whip |
| 1 | 16 |  |  |  |  |  | 1girl, black_bodysuit, center_opening, choker, cleavage, hip_vent, looking_at_viewer, smile, solo, blush, thighs, collarbone, open_mouth |
| 2 | 12 |  |  |  |  |  | 1boy, 1girl, black_bodysuit, blush, hetero, penis, nipples, thighs, vaginal, center_opening, hip_vent, mosaic_censoring, open_mouth, pussy, solo_focus, smile, spread_legs, choker, looking_at_viewer, navel, collarbone, clothed_sex |
| 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, black_gloves, china_dress, looking_at_viewer, solo, underboob, center_opening, sleeveless_dress, smile, tassel, thighs, double_bun, side_slit, sitting, blush, jingle_bell, open_mouth, white-framed_eyewear |
| 4 | 8 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, black_gloves, china_dress, double_bun, holding_fan, looking_at_viewer, smile, solo, underboob, center_opening, tassel, jingle_bell, open_mouth, fang, folded_fan, sleeveless_dress |
| 5 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, black_gloves, long_sleeves, twintails, fellatio, looking_at_viewer, mosaic_censoring, nipples, rabbit_ears, erection, pov, white_shirt, :>=, male_pubic_hair, black_bowtie, breasts_out, collared_shirt, cum, dress_shirt, open_clothes |
| 6 | 68 |  |  |  |  |  | black_bow, 1girl, rabbit_ears, smile, looking_at_viewer, twintails, white_shirt, long_sleeves, solo, collared_shirt, dress_shirt, underbust, black_gloves, corset, blush, white_pantyhose, coattails, thighs, cloak, leotard, open_mouth, playboy_bunny |
| 7 | 5 |  |  |  |  |  | bare_shoulders, blue_sky, casual_one-piece_swimsuit, cleavage, 2girls, black_one-piece_swimsuit, blush, looking_at_viewer, thighs, choker, covered_navel, day, grey-framed_eyewear, highleg_swimsuit, smile, bikini, closed_mouth, collarbone, open_mouth, solo_focus, white_hair |
| 8 | 5 |  |  |  |  |  | cleavage, hair_ribbon, 1girl, cat_paws, looking_at_viewer, neck_bell, paw_gloves, solo, bare_shoulders, blue_ribbon, detached_sleeves, jingle_bell, red_ribbon, blue_kimono, collarbone, fangs, grey_background, open_mouth, red_kimono, simple_background, smile |
| 9 | 37 |  |  |  |  |  | looking_at_viewer, very_long_hair, 1girl, double_bun, hat, long_sleeves, white_headwear, rabbit_ears, smile, white_dress, detached_collar, pink_gloves, white_coat, cleavage, double-breasted, wide_sleeves, open_coat, solo, short_dress, blush, thighs, white_thighhighs, garter_straps, thigh_boots, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | looking_at_viewer | solo | choker | cleavage | off_shoulder | collarbone | smile | thighs | long_sleeves | wide_sleeves | black_headwear | very_long_hair | top_hat | white_gloves | open_mouth | thighhighs | black_skirt | holding | kimono | red_coat | whip | black_bodysuit | center_opening | hip_vent | blush | 1boy | hetero | penis | nipples | vaginal | mosaic_censoring | pussy | solo_focus | spread_legs | navel | clothed_sex | black_dress | black_gloves | china_dress | underboob | sleeveless_dress | tassel | double_bun | side_slit | sitting | jingle_bell | white-framed_eyewear | holding_fan | fang | folded_fan | twintails | fellatio | rabbit_ears | erection | pov | white_shirt | :>= | male_pubic_hair | black_bowtie | breasts_out | collared_shirt | cum | dress_shirt | open_clothes | black_bow | underbust | corset | white_pantyhose | coattails | cloak | leotard | playboy_bunny | blue_sky | casual_one-piece_swimsuit | 2girls | black_one-piece_swimsuit | covered_navel | day | grey-framed_eyewear | highleg_swimsuit | bikini | closed_mouth | white_hair | hair_ribbon | cat_paws | neck_bell | paw_gloves | blue_ribbon | detached_sleeves | red_ribbon | blue_kimono | fangs | grey_background | red_kimono | simple_background | hat | white_headwear | white_dress | detached_collar | pink_gloves | white_coat | double-breasted | open_coat | short_dress | white_thighhighs | garter_straps | thigh_boots |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------------|:-------|:---------|:-----------|:---------------|:-------------|:--------|:---------|:---------------|:---------------|:-----------------|:-----------------|:----------|:---------------|:-------------|:-------------|:--------------|:----------|:---------|:-----------|:-------|:-----------------|:-----------------|:-----------|:--------|:-------|:---------|:--------|:----------|:----------|:-------------------|:--------|:-------------|:--------------|:--------|:--------------|:--------------|:---------------|:--------------|:------------|:-------------------|:---------|:-------------|:------------|:----------|:--------------|:-----------------------|:--------------|:-------|:-------------|:------------|:-----------|:--------------|:-----------|:------|:--------------|:------|:------------------|:---------------|:--------------|:-----------------|:------|:--------------|:---------------|:------------|:------------|:---------|:------------------|:------------|:--------|:----------|:----------------|:-----------|:----------------------------|:---------|:---------------------------|:----------------|:------|:----------------------|:-------------------|:---------|:---------------|:-------------|:--------------|:-----------|:------------|:-------------|:--------------|:-------------------|:-------------|:--------------|:--------|:------------------|:-------------|:--------------------|:------|:-----------------|:--------------|:------------------|:--------------|:-------------|:------------------|:------------|:--------------|:-------------------|:----------------|:--------------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 16 |  |  |  |  |  | X | | X | X | X | X | | X | X | X | | | | | | | X | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | X | | X | | | X | X | X | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | X | | | | | X | X | | | | | | | X | | | | | | | | X | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | | | | | X | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 68 |  |  |  |  |  | X | | X | X | | | | | X | X | X | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | X | | | X | | | | | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | | X | X | | X | X | | X | X | X | | | | | | | X | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | X | X | X | | X | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 9 | 37 |  |  |  |  |  | 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 |
|
lmg-anon/VNTL-v2.5-1.6k-dpo-pairs | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 23750414
num_examples: 8988
download_size: 7587165
dataset_size: 23750414
task_categories:
- translation
language:
- en
- ja
tags:
- dpo
---
# Dataset Card for "VNTL-v2.5-1.6k-dpo-pairs"
This is a very experimental DPO dataset for VNTL, I have no idea if DPO will work well to improve translation, but I guess it's worth a shot!
This dataset was generated using the model [vntl-7b-v0.3.1](https://huggingface.co/lmg-anon/vntl-7b-v0.3.1-hf) using prompts from the dataset [VNTL-v2.5-1k](https://huggingface.co/datasets/lmg-anon/VNTL-v2.5-1k). All rejected sequences were generated using temperature **0.7**, and they were chosen using a cosine similarity threshold.
Things to consider afterwards:
- **Distilation**: This dataset wasn't filtered in anyway, so there may be pairs that are actually ties or where the chosen sequence is bad.
- https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
- **Avoid human data**: According to the paper, DPO performs better with sequences sampled directly from the model. Therefore, the dataset could be enhanced by trying to extract the chosen sequences from the model itself.
- https://arxiv.org/html/2305.18290v2#S4.p5.15.1
- **CPO**: CPO may be a better fit than DPO, it is supposedly more forgiving for accuracy, which is better for translation tasks since the translation being correct is better than it being 100% accurate to the chosen sequence.
- https://github.com/fe1ixxu/ALMA |
stephmnt/fpa | ---
license: mit
language:
- fr
--- |
AdapterOcean/med_alpaca_standardized_cluster_7_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 12387279
num_examples: 7527
download_size: 6662023
dataset_size: 12387279
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_7_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Netotomasia/laridolimaomofado | ---
license: openrail
---
|
9wimu9/translated_eli5_dataset_sin_v2-gold-answer-removed-from-contexts | ---
dataset_info:
features:
- name: question
dtype: string
- name: gold_answer
dtype: string
- name: contexts
sequence: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 436001746.5264764
num_examples: 44836
- name: test
num_bytes: 48446799.473523624
num_examples: 4982
- name: validation
num_bytes: 48446799.473523624
num_examples: 4982
download_size: 217824691
dataset_size: 532895345.4735236
---
# Dataset Card for "translated_eli5_dataset_sin_v2-gold-answer-removed-from-contexts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_allknowingroger__NexusMistral2-7B-slerp | ---
pretty_name: Evaluation run of allknowingroger/NexusMistral2-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allknowingroger/NexusMistral2-7B-slerp](https://huggingface.co/allknowingroger/NexusMistral2-7B-slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_allknowingroger__NexusMistral2-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-11T04:56:46.272916](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__NexusMistral2-7B-slerp/blob/main/results_2024-04-11T04-56-46.272916.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\": 0.6309937700400754,\n\
\ \"acc_stderr\": 0.03262184221410638,\n \"acc_norm\": 0.6339471478648573,\n\
\ \"acc_norm_stderr\": 0.03327552483787294,\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.608174338272664,\n\
\ \"mc2_stderr\": 0.015426399036215073\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938215,\n\
\ \"acc_norm\": 0.6629692832764505,\n \"acc_norm_stderr\": 0.013813476652902276\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6475801633140809,\n\
\ \"acc_stderr\": 0.004767475366689765,\n \"acc_norm\": 0.8474407488548098,\n\
\ \"acc_norm_stderr\": 0.003588272874852478\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n\
\ \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\
acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6806451612903226,\n\
\ \"acc_stderr\": 0.02652270967466777,\n \"acc_norm\": 0.6806451612903226,\n\
\ \"acc_norm_stderr\": 0.02652270967466777\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.0351452856217501,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.0351452856217501\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721164,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721164\n \
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110936,\n\
\ \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110936\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131147,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131147\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612896,\n \"\
acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612896\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"\
acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.02704462171947409,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.02704462171947409\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7932489451476793,\n \"acc_stderr\": 0.02636165166838909,\n \
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.02636165166838909\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159464,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917669,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917669\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\
\ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\
\ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.013964393769899133,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.013964393769899133\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3575418994413408,\n\
\ \"acc_stderr\": 0.016029394474894886,\n \"acc_norm\": 0.3575418994413408,\n\
\ \"acc_norm_stderr\": 0.016029394474894886\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729474,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729474\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799215,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799215\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \
\ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46153846153846156,\n\
\ \"acc_stderr\": 0.01273239828619044,\n \"acc_norm\": 0.46153846153846156,\n\
\ \"acc_norm_stderr\": 0.01273239828619044\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6965174129353234,\n\
\ \"acc_stderr\": 0.03251006816458618,\n \"acc_norm\": 0.6965174129353234,\n\
\ \"acc_norm_stderr\": 0.03251006816458618\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366255,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366255\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.608174338272664,\n\
\ \"mc2_stderr\": 0.015426399036215073\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7955801104972375,\n \"acc_stderr\": 0.011334090612597209\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5466262319939348,\n \
\ \"acc_stderr\": 0.013712471049515448\n }\n}\n```"
repo_url: https://huggingface.co/allknowingroger/NexusMistral2-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|arc:challenge|25_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|gsm8k|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hellaswag|10_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-56-46.272916.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T04-56-46.272916.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- '**/details_harness|winogrande|5_2024-04-11T04-56-46.272916.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-11T04-56-46.272916.parquet'
- config_name: results
data_files:
- split: 2024_04_11T04_56_46.272916
path:
- results_2024-04-11T04-56-46.272916.parquet
- split: latest
path:
- results_2024-04-11T04-56-46.272916.parquet
---
# Dataset Card for Evaluation run of allknowingroger/NexusMistral2-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [allknowingroger/NexusMistral2-7B-slerp](https://huggingface.co/allknowingroger/NexusMistral2-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_allknowingroger__NexusMistral2-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-11T04:56:46.272916](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__NexusMistral2-7B-slerp/blob/main/results_2024-04-11T04-56-46.272916.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": 0.6309937700400754,
"acc_stderr": 0.03262184221410638,
"acc_norm": 0.6339471478648573,
"acc_norm_stderr": 0.03327552483787294,
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.608174338272664,
"mc2_stderr": 0.015426399036215073
},
"harness|arc:challenge|25": {
"acc": 0.6083617747440273,
"acc_stderr": 0.014264122124938215,
"acc_norm": 0.6629692832764505,
"acc_norm_stderr": 0.013813476652902276
},
"harness|hellaswag|10": {
"acc": 0.6475801633140809,
"acc_stderr": 0.004767475366689765,
"acc_norm": 0.8474407488548098,
"acc_norm_stderr": 0.003588272874852478
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.037150621549989056,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.037150621549989056
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.036563436533531585
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5446808510638298,
"acc_stderr": 0.03255525359340355,
"acc_norm": 0.5446808510638298,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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[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
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[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]
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**BibTeX:**
[More Information Needed]
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[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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## Dataset Card Contact
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liuyanchen1015/MULTI_VALUE_stsb_double_superlative | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 8302
num_examples: 33
- name: test
num_bytes: 3913
num_examples: 19
- name: train
num_bytes: 16548
num_examples: 76
download_size: 28923
dataset_size: 28763
---
# Dataset Card for "MULTI_VALUE_stsb_double_superlative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713101349 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 79699
num_examples: 216
download_size: 47818
dataset_size: 79699
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
theteroles/asci | ---
license: mit
---
|
rjac/DepressionDetection-prompted | ---
dataset_info:
features:
- name: clean_text
dtype: string
- name: is_depression
dtype: int64
- name: instances
sequence: string
splits:
- name: train
num_bytes: 4631512
num_examples: 5411
- name: test
num_bytes: 1930456
num_examples: 2320
download_size: 3543125
dataset_size: 6561968
---
# Dataset Card for "DepressionDetection-prompted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wanghaofan/pokemon-wiki-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: name_en
dtype: string
- name: name_zh
dtype: string
- name: text_en
dtype: string
- name: text_zh
dtype: string
splits:
- name: train
num_bytes: 117645424.0
num_examples: 898
download_size: 117512478
dataset_size: 117645424.0
---
# Dataset Card for Pokémon wiki captions
This project is inspired by [pokmon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions), where the captions are all generated by pre-trained BLIP without any manual effort.
However, the quality and accuracy of their captions are not satisfactory enough, which leaves it known whether better captions lead to better results. This motivates our dataset.
# Example
> General attribute, looks like a little monkey, body color is composed of purple and beige, the end of the tail is like a hand
> Poisonous attributes, it looks like a huge purple cobra, with black stripes on its body, small head, and triangular eyes
# Properties
All 898 images are from [The Complete Pokemon Images Data Set](https://www.kaggle.com/datasets/arenagrenade/the-complete-pokemon-images-data-set?resource=download) in Kaggle with size 475x475. Each image is accompanied with corresponding
pokemon name and its detailed description from [Pokemon Wiki](https://wiki.52poke.com/wiki/%E4%B8%BB%E9%A1%B5), English and Chinese captions are provided. Human efforts are also involved to revise.
# How to use
```
from datasets import load_dataset
dataset = load_dataset("wanghaofan/pokemon-wiki-captions")
```
The dataset is formatted as below. For each row the dataset contains `image`, `name_en`, `name_zh`, `text_en` and `text_zh` keys. `image` is a varying size PIL jpeg, `name` is the name of pokemon, and `text` is the accompanying text caption. Only a train split is provided.
```
DatasetDict({
train: Dataset({
features: ['image', 'name_en', 'name_zh', 'text_en', 'text_zh'],
num_rows: 898
})
})
```
# Citation
If you use this dataset in your work, please cite it as:
```
@misc{wanghaofan2022pokemon,
author = {Haofan Wang},
title = {Pokemon wiki captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/wanghaofan/pokemon-wiki-captions/}}
}
```
|
AdapterOcean/med_alpaca_standardized_cluster_10_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 17346696
num_examples: 8485
download_size: 9346628
dataset_size: 17346696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_10_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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