datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Muthuchancoach/TG_QnA | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 39973
num_examples: 158
download_size: 10001
dataset_size: 39973
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TG_QnA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
income/cqadupstack-unix-top-20-gen-queries | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
---
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
ThWu/mmlu_train_test_split | ---
dataset_info:
features:
- name: input
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: target
dtype: string
- name: topic
dtype: string
- name: question_id
dtype: int64
splits:
- name: train
num_bytes: 6984551
num_examples: 14042
- name: test
num_bytes: 765372
num_examples: 1531
download_size: 4269240
dataset_size: 7749923
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Bingsu/some_corpus | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 11307919646
num_examples: 12932421
download_size: 1089008611
dataset_size: 11307919646
---
# Dataset Card for "some_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ai-aerospace/ams_data_train_generic_v0.1_100 | ---
license: apache-2.0
base-model: TheBloke/Llama-2-7B-Chat-GGUF
---
Question and answer pairs for the first 100 entries of aerospace mechanism symposia 5000 word chunk entries. Full file of entries is here: https://github.com/dsmueller3760/aerospace_chatbot/blob/llm_training/data/AMS/ams_data_answers.jsonl
See this repository for details: https://github.com/dsmueller3760/aerospace_chatbot/tree/main
Prompts generated using TheBloke/Llama-2-7B-Chat-GGUF |
rescer/twitter_dataset_1713222816 | ---
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: 202139
num_examples: 638
download_size: 115925
dataset_size: 202139
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
delphi-suite/v0-next-logprobs-llama2-400k | ---
dataset_info:
features:
- name: logprobs
sequence: float64
splits:
- name: validation
num_bytes: 45818277
num_examples: 10982
download_size: 37682254
dataset_size: 45818277
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
Disfluency/disfluency-es-16k | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 21315396.0
num_examples: 270
- name: test
num_bytes: 1731088.0
num_examples: 30
download_size: 21061943
dataset_size: 23046484.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "disfluency-es-16k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vishnusr/code_searchnet_reduced_val | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: 'Unnamed: 0.1'
dtype: int64
- name: 'Unnamed: 0'
dtype: int64
- name: code
dtype: string
- name: docstring
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 1078734
num_examples: 500
download_size: 483209
dataset_size: 1078734
---
# Dataset Card for "code_searchnet_reduced_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GIZ/vulnerability_training_data_full | ---
task_categories:
- text-classification
language:
- en
tags:
- climate
pretty_name: Vulnerability training data
--- |
delayedkarma/langchain-issues | ---
license: apache-2.0
---
### Dataset Card for LangChain Issues
#### Dataset Summary
LangChain Issues is a dataset consisting of LangChain issues and pull requests associated with the LangChain repository (https://github.com/langchain-ai/langchain).
It is intended for educational purposes and can be used for semantic search or multilabel text classification.
The contents of each LangChain issue are in English and concern the domain of datasets for NLP, computer vision, and beyond. |
12Manman12/translate-en-to-bclvrc | ---
language:
- en
tags:
- code
--- |
nielsr/datacomp_small_english_captions_with_weird_characters | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: uid
dtype: string
- name: url
dtype: string
- name: text
dtype: string
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: clip_b32_similarity_score
dtype: float32
- name: clip_l14_similarity_score
dtype: float32
- name: face_bboxes
sequence:
sequence: float64
- name: sha256
dtype: string
- name: detected_language
dtype: string
splits:
- name: train
num_bytes: 96674791.78677922
num_examples: 301183
download_size: 82472583
dataset_size: 96674791.78677922
---
# Dataset Card for "datacomp_small_english_captions_with_weird_characters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2 | ---
pretty_name: Evaluation run of BarryFutureman/WestLakeX-7B-EvoMerge-Variant2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BarryFutureman/WestLakeX-7B-EvoMerge-Variant2](https://huggingface.co/BarryFutureman/WestLakeX-7B-EvoMerge-Variant2)\
\ 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_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T03:18:15.694379](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2/blob/main/results_2024-02-02T03-18-15.694379.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\": 0.6538297853321007,\n\
\ \"acc_stderr\": 0.0320522890373237,\n \"acc_norm\": 0.6530566018124656,\n\
\ \"acc_norm_stderr\": 0.03272874681048371,\n \"mc1\": 0.5618115055079559,\n\
\ \"mc1_stderr\": 0.01736923616440441,\n \"mc2\": 0.7034639754228852,\n\
\ \"mc2_stderr\": 0.014889031021791599\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7081911262798635,\n \"acc_stderr\": 0.013284525292403511,\n\
\ \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7144991037641903,\n\
\ \"acc_stderr\": 0.0045072961962278075,\n \"acc_norm\": 0.8851822346146186,\n\
\ \"acc_norm_stderr\": 0.003181503506054323\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.0373852067611967,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.0373852067611967\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\": 0.027834912527544064,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531003,\n \"\
acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531003\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\
acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"\
acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\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\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097112,\n \
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097112\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.02925290592725197,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.02925290592725197\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\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\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\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\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\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\": 0.4223463687150838,\n\
\ \"acc_stderr\": 0.01651959427529712,\n \"acc_norm\": 0.4223463687150838,\n\
\ \"acc_norm_stderr\": 0.01651959427529712\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02609016250427905,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02609016250427905\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.02447722285613511,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.02447722285613511\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4667535853976532,\n\
\ \"acc_stderr\": 0.012741974333897227,\n \"acc_norm\": 0.4667535853976532,\n\
\ \"acc_norm_stderr\": 0.012741974333897227\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\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\": 0.028535560337128448,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\
\ \"acc_stderr\": 0.02740385941078685,\n \"acc_norm\": 0.8159203980099502,\n\
\ \"acc_norm_stderr\": 0.02740385941078685\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5618115055079559,\n\
\ \"mc1_stderr\": 0.01736923616440441,\n \"mc2\": 0.7034639754228852,\n\
\ \"mc2_stderr\": 0.014889031021791599\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8579321231254933,\n \"acc_stderr\": 0.009812000391679369\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6830932524639879,\n \
\ \"acc_stderr\": 0.012815868296721353\n }\n}\n```"
repo_url: https://huggingface.co/BarryFutureman/WestLakeX-7B-EvoMerge-Variant2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|arc:challenge|25_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|gsm8k|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hellaswag|10_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T03-18-15.694379.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T03-18-15.694379.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- '**/details_harness|winogrande|5_2024-02-02T03-18-15.694379.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T03-18-15.694379.parquet'
- config_name: results
data_files:
- split: 2024_02_02T03_18_15.694379
path:
- results_2024-02-02T03-18-15.694379.parquet
- split: latest
path:
- results_2024-02-02T03-18-15.694379.parquet
---
# Dataset Card for Evaluation run of BarryFutureman/WestLakeX-7B-EvoMerge-Variant2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BarryFutureman/WestLakeX-7B-EvoMerge-Variant2](https://huggingface.co/BarryFutureman/WestLakeX-7B-EvoMerge-Variant2) 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_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T03:18:15.694379](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__WestLakeX-7B-EvoMerge-Variant2/blob/main/results_2024-02-02T03-18-15.694379.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": 0.6538297853321007,
"acc_stderr": 0.0320522890373237,
"acc_norm": 0.6530566018124656,
"acc_norm_stderr": 0.03272874681048371,
"mc1": 0.5618115055079559,
"mc1_stderr": 0.01736923616440441,
"mc2": 0.7034639754228852,
"mc2_stderr": 0.014889031021791599
},
"harness|arc:challenge|25": {
"acc": 0.7081911262798635,
"acc_stderr": 0.013284525292403511,
"acc_norm": 0.7252559726962458,
"acc_norm_stderr": 0.013044617212771227
},
"harness|hellaswag|10": {
"acc": 0.7144991037641903,
"acc_stderr": 0.0045072961962278075,
"acc_norm": 0.8851822346146186,
"acc_norm_stderr": 0.003181503506054323
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.0373852067611967,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.0373852067611967
},
"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": 0.027834912527544064,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544064
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
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"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.43915343915343913,
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"acc_norm_stderr": 0.025559920550531003
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
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"acc_norm_stderr": 0.023540799358723295
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm_stderr": 0.035179450386910616
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
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"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
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"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.02385479568097112
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.02925290592725197
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.030388353551886786,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.030388353551886786
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8422018348623853,
"acc_stderr": 0.01563002297009244,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.01563002297009244
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.02595502084162113,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
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"acc": 0.8015267175572519,
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"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
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"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
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"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
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"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.02023714900899093,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.02023714900899093
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
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"acc_norm": 0.74,
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
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"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069356
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4223463687150838,
"acc_stderr": 0.01651959427529712,
"acc_norm": 0.4223463687150838,
"acc_norm_stderr": 0.01651959427529712
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.02609016250427905,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.02609016250427905
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.02558306248998481,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.02558306248998481
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.02447722285613511,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.02447722285613511
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
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"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4667535853976532,
"acc_stderr": 0.012741974333897227,
"acc_norm": 0.4667535853976532,
"acc_norm_stderr": 0.012741974333897227
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6617647058823529,
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"acc_norm": 0.6617647058823529,
"acc_norm_stderr": 0.028739328513983572
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
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"acc_norm_stderr": 0.018950886770806315
},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128448,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128448
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8159203980099502,
"acc_stderr": 0.02740385941078685,
"acc_norm": 0.8159203980099502,
"acc_norm_stderr": 0.02740385941078685
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5618115055079559,
"mc1_stderr": 0.01736923616440441,
"mc2": 0.7034639754228852,
"mc2_stderr": 0.014889031021791599
},
"harness|winogrande|5": {
"acc": 0.8579321231254933,
"acc_stderr": 0.009812000391679369
},
"harness|gsm8k|5": {
"acc": 0.6830932524639879,
"acc_stderr": 0.012815868296721353
}
}
```
## 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] |
aanchalsatyan/Embedding | ---
license: mit
---
|
Shakebird/white_house_speech | ---
license: mit
language:
- en
--- |
CarlosFersoft/GPBusiness | ---
license: mit
task_categories:
- question-answering
language:
- es
tags:
- finance
pretty_name: GPBusiness
size_categories:
- n<1K
--- |
skaty5678/temp-collator-7711 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 16184544
num_examples: 7711
download_size: 4456541
dataset_size: 16184544
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d7f07bf2 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1330
dataset_size: 182
---
# Dataset Card for "d7f07bf2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AkashMnd/prismadgen | ---
license: mit
---
|
open-llm-leaderboard/details_garage-bAInd__Stable-Platypus2-13B | ---
pretty_name: Evaluation run of garage-bAInd/Stable-Platypus2-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [garage-bAInd/Stable-Platypus2-13B](https://huggingface.co/garage-bAInd/Stable-Platypus2-13B)\
\ 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_garage-bAInd__Stable-Platypus2-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T23:47:31.962394](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Stable-Platypus2-13B/blob/main/results_2023-09-17T23-47-31.962394.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\": 0.37531459731543626,\n\
\ \"em_stderr\": 0.004958702554959804,\n \"f1\": 0.45221476510067204,\n\
\ \"f1_stderr\": 0.004729347386559949,\n \"acc\": 0.39347033490847444,\n\
\ \"acc_stderr\": 0.00776582600946219\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.37531459731543626,\n \"em_stderr\": 0.004958702554959804,\n\
\ \"f1\": 0.45221476510067204,\n \"f1_stderr\": 0.004729347386559949\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01819560272934041,\n \
\ \"acc_stderr\": 0.003681611894073872\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.011850040124850508\n\
\ }\n}\n```"
repo_url: https://huggingface.co/garage-bAInd/Stable-Platypus2-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|arc:challenge|25_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T23_47_31.962394
path:
- '**/details_harness|drop|3_2023-09-17T23-47-31.962394.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T23-47-31.962394.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T23_47_31.962394
path:
- '**/details_harness|gsm8k|5_2023-09-17T23-47-31.962394.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T23-47-31.962394.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hellaswag|10_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T15:52:34.927040.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T15:52:34.927040.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T15:52:34.927040.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T23_47_31.962394
path:
- '**/details_harness|winogrande|5_2023-09-17T23-47-31.962394.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T23-47-31.962394.parquet'
- config_name: results
data_files:
- split: 2023_08_09T15_52_34.927040
path:
- results_2023-08-09T15:52:34.927040.parquet
- split: 2023_09_17T23_47_31.962394
path:
- results_2023-09-17T23-47-31.962394.parquet
- split: latest
path:
- results_2023-09-17T23-47-31.962394.parquet
---
# Dataset Card for Evaluation run of garage-bAInd/Stable-Platypus2-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/garage-bAInd/Stable-Platypus2-13B
- **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 [garage-bAInd/Stable-Platypus2-13B](https://huggingface.co/garage-bAInd/Stable-Platypus2-13B) 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_garage-bAInd__Stable-Platypus2-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T23:47:31.962394](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Stable-Platypus2-13B/blob/main/results_2023-09-17T23-47-31.962394.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": 0.37531459731543626,
"em_stderr": 0.004958702554959804,
"f1": 0.45221476510067204,
"f1_stderr": 0.004729347386559949,
"acc": 0.39347033490847444,
"acc_stderr": 0.00776582600946219
},
"harness|drop|3": {
"em": 0.37531459731543626,
"em_stderr": 0.004958702554959804,
"f1": 0.45221476510067204,
"f1_stderr": 0.004729347386559949
},
"harness|gsm8k|5": {
"acc": 0.01819560272934041,
"acc_stderr": 0.003681611894073872
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.011850040124850508
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_chargoddard__internlm2-base-20b-llama | ---
pretty_name: Evaluation run of chargoddard/internlm2-base-20b-llama
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/internlm2-base-20b-llama](https://huggingface.co/chargoddard/internlm2-base-20b-llama)\
\ 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_chargoddard__internlm2-base-20b-llama\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-21T08:12:11.575065](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__internlm2-base-20b-llama/blob/main/results_2024-01-21T08-12-11.575065.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\": 0.6376672606100185,\n\
\ \"acc_stderr\": 0.03233501598179968,\n \"acc_norm\": 0.6426334526719998,\n\
\ \"acc_norm_stderr\": 0.0329801483756123,\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.43966281100559496,\n\
\ \"mc2_stderr\": 0.014256122898440773\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5878839590443686,\n \"acc_stderr\": 0.014383915302225403,\n\
\ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.014104578366491888\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6158135829516033,\n\
\ \"acc_stderr\": 0.004854082479916909,\n \"acc_norm\": 0.8210515833499303,\n\
\ \"acc_norm_stderr\": 0.0038252574352092344\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\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\": 0.7039473684210527,\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\": 0.65,\n\
\ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \
\ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n\
\ \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036843,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036843\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6042553191489362,\n \"acc_stderr\": 0.031967586978353627,\n\
\ \"acc_norm\": 0.6042553191489362,\n \"acc_norm_stderr\": 0.031967586978353627\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305528,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305528\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377562\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\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\
\ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-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\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026552207828215282,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026552207828215282\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015184,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015184\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110943,\n\
\ \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110943\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977924,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977924\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092437,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092437\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\
acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.02786594228663933,\n \"acc_norm\"\
: 0.803921568627451,\n \"acc_norm_stderr\": 0.02786594228663933\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \"\
acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724561,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724561\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\
acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094633\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\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128136,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128136\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\
\ \"acc_stderr\": 0.0144191239809319,\n \"acc_norm\": 0.7956577266922095,\n\
\ \"acc_norm_stderr\": 0.0144191239809319\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6907514450867052,\n \"acc_stderr\": 0.02488314057007176,\n\
\ \"acc_norm\": 0.6907514450867052,\n \"acc_norm_stderr\": 0.02488314057007176\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38994413407821227,\n\
\ \"acc_stderr\": 0.016312376629213067,\n \"acc_norm\": 0.38994413407821227,\n\
\ \"acc_norm_stderr\": 0.016312376629213067\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188947,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188947\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.41843971631205673,\n \"acc_stderr\": 0.029427994039419998,\n \
\ \"acc_norm\": 0.41843971631205673,\n \"acc_norm_stderr\": 0.029427994039419998\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4765319426336376,\n\
\ \"acc_stderr\": 0.012756161942523365,\n \"acc_norm\": 0.4765319426336376,\n\
\ \"acc_norm_stderr\": 0.012756161942523365\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389844,\n\
\ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389844\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0190709855896875,\n \
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0190709855896875\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291313,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291313\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2913096695226438,\n\
\ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.43966281100559496,\n\
\ \"mc2_stderr\": 0.014256122898440773\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44806671721000757,\n \
\ \"acc_stderr\": 0.013697992668274518\n }\n}\n```"
repo_url: https://huggingface.co/chargoddard/internlm2-base-20b-llama
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_21T08_12_11.575065
path:
- '**/details_harness|arc:challenge|25_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|gsm8k|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hellaswag|10_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T08-12-11.575065.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-21T08-12-11.575065.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- '**/details_harness|winogrande|5_2024-01-21T08-12-11.575065.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-21T08-12-11.575065.parquet'
- config_name: results
data_files:
- split: 2024_01_21T08_12_11.575065
path:
- results_2024-01-21T08-12-11.575065.parquet
- split: latest
path:
- results_2024-01-21T08-12-11.575065.parquet
---
# Dataset Card for Evaluation run of chargoddard/internlm2-base-20b-llama
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [chargoddard/internlm2-base-20b-llama](https://huggingface.co/chargoddard/internlm2-base-20b-llama) 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_chargoddard__internlm2-base-20b-llama",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-21T08:12:11.575065](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__internlm2-base-20b-llama/blob/main/results_2024-01-21T08-12-11.575065.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": 0.6376672606100185,
"acc_stderr": 0.03233501598179968,
"acc_norm": 0.6426334526719998,
"acc_norm_stderr": 0.0329801483756123,
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.43966281100559496,
"mc2_stderr": 0.014256122898440773
},
"harness|arc:challenge|25": {
"acc": 0.5878839590443686,
"acc_stderr": 0.014383915302225403,
"acc_norm": 0.6305460750853242,
"acc_norm_stderr": 0.014104578366491888
},
"harness|hellaswag|10": {
"acc": 0.6158135829516033,
"acc_stderr": 0.004854082479916909,
"acc_norm": 0.8210515833499303,
"acc_norm_stderr": 0.0038252574352092344
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"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": 0.7039473684210527,
"acc_stderr": 0.037150621549989056,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.037150621549989056
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006717,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006717
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036843,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036843
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6042553191489362,
"acc_stderr": 0.031967586978353627,
"acc_norm": 0.6042553191489362,
"acc_norm_stderr": 0.031967586978353627
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.02535574126305528,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.02535574126305528
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.04390259265377562,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.04390259265377562
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.023415293433568525,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.023415293433568525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.541871921182266,
"acc_stderr": 0.03505630140785741,
"acc_norm": 0.541871921182266,
"acc_norm_stderr": 0.03505630140785741
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8,
"acc_stderr": 0.031234752377721164,
"acc_norm": 0.8,
"acc_norm_stderr": 0.031234752377721164
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026552207828215282,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026552207828215282
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8601036269430051,
"acc_stderr": 0.025033870583015184,
"acc_norm": 0.8601036269430051,
"acc_norm_stderr": 0.025033870583015184
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6282051282051282,
"acc_stderr": 0.024503472557110943,
"acc_norm": 0.6282051282051282,
"acc_norm_stderr": 0.024503472557110943
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253252,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253252
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977924,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977924
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8422018348623853,
"acc_stderr": 0.015630022970092437,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.015630022970092437
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.03388857118502325,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.03388857118502325
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.02786594228663933,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.02786594228663933
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477515,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6946564885496184,
"acc_stderr": 0.04039314978724561,
"acc_norm": 0.6946564885496184,
"acc_norm_stderr": 0.04039314978724561
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7520661157024794,
"acc_stderr": 0.039418975265163025,
"acc_norm": 0.7520661157024794,
"acc_norm_stderr": 0.039418975265163025
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.04236511258094633,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.04236511258094633
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.033519538795212696,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.033519538795212696
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.02158649400128136,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.02158649400128136
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7956577266922095,
"acc_stderr": 0.0144191239809319,
"acc_norm": 0.7956577266922095,
"acc_norm_stderr": 0.0144191239809319
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6907514450867052,
"acc_stderr": 0.02488314057007176,
"acc_norm": 0.6907514450867052,
"acc_norm_stderr": 0.02488314057007176
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.38994413407821227,
"acc_stderr": 0.016312376629213067,
"acc_norm": 0.38994413407821227,
"acc_norm_stderr": 0.016312376629213067
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7091503267973857,
"acc_stderr": 0.02600480036395213,
"acc_norm": 0.7091503267973857,
"acc_norm_stderr": 0.02600480036395213
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188947,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188947
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.024288533637726095,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.024288533637726095
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.41843971631205673,
"acc_stderr": 0.029427994039419998,
"acc_norm": 0.41843971631205673,
"acc_norm_stderr": 0.029427994039419998
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4765319426336376,
"acc_stderr": 0.012756161942523365,
"acc_norm": 0.4765319426336376,
"acc_norm_stderr": 0.012756161942523365
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6691176470588235,
"acc_stderr": 0.02858270975389844,
"acc_norm": 0.6691176470588235,
"acc_norm_stderr": 0.02858270975389844
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.0190709855896875,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.0190709855896875
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.028795185574291313,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.028795185574291313
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.02553843336857833,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.02553843336857833
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2913096695226438,
"mc1_stderr": 0.01590598704818483,
"mc2": 0.43966281100559496,
"mc2_stderr": 0.014256122898440773
},
"harness|winogrande|5": {
"acc": 0.7821625887924231,
"acc_stderr": 0.011601066079939324
},
"harness|gsm8k|5": {
"acc": 0.44806671721000757,
"acc_stderr": 0.013697992668274518
}
}
```
## 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] |
dkk2564996/sevkkkkkk | ---
license: openrail
---
|
fiveflow/cot_ranking | ---
dataset_info:
features:
- name: question
dtype: string
- name: response_j
dtype: string
- name: response_k
dtype: string
splits:
- name: train
num_bytes: 64266082
num_examples: 67830
- name: test
num_bytes: 3323500
num_examples: 3570
download_size: 408618
dataset_size: 67589582
---
# Dataset Card for "cot_ranking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jpbello/common_language_preprocessed | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: sentence
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: label
dtype:
class_label:
names:
'0': Arabic
'1': Basque
'2': Breton
'3': Catalan
'4': Chinese_China
'5': Chinese_Hongkong
'6': Chinese_Taiwan
'7': Chuvash
'8': Czech
'9': Dhivehi
'10': Dutch
'11': English
'12': Esperanto
'13': Estonian
'14': French
'15': Frisian
'16': Georgian
'17': German
'18': Greek
'19': Hakha_Chin
'20': Indonesian
'21': Interlingua
'22': Italian
'23': Japanese
'24': Kabyle
'25': Kinyarwanda
'26': Kyrgyz
'27': Latvian
'28': Maltese
'29': Mangolian
'30': Persian
'31': Polish
'32': Portuguese
'33': Romanian
'34': Romansh_Sursilvan
'35': Russian
'36': Sakha
'37': Slovenian
'38': Spanish
'39': Swedish
'40': Tamil
'41': Tatar
'42': Turkish
'43': Ukranian
'44': Welsh
- name: input_values
sequence: float32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 13848986619
num_examples: 22194
- name: validation
num_bytes: 3461442109
num_examples: 5888
- name: test
num_bytes: 3473659131
num_examples: 5963
download_size: 8143061729
dataset_size: 20784087859
---
# Dataset Card for "common_language_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
c01dsnap/top-1m | ---
license: cc-by-nc-sa-4.0
---
# Top 1 Million Domains
A collection of free and directly downloadable top 1M domain lists, a suitable alternative to the now-retired Alexa.com service.
These alternative services offer a solution for obtaining top domain data in the absence of Alexa.com.
Please feel free to send a pull request for any other free and downloadable top 1M domain list.
## Lists
### Alexa
- Download page: https://alexa.com/ (End of Service at middle of 2022)
- Download link: https://s3.amazonaws.com/alexa-static/top-1m.csv.zip
### Cisco Umbrella
- Download page: https://s3-us-west-1.amazonaws.com/umbrella-static/index.html
- Download link: https://s3-us-west-1.amazonaws.com/umbrella-static/top-1m.csv.zip
### Majestic
- Download page: https://majestic.com/reports/majestic-million
- Download link: https://downloads.majestic.com/majestic_million.csv
### BuiltWith
- Download page: https://builtwith.com/top-1m
- Download link: https://builtwith.com/dl/builtwith-top1m.zip
### Statvoo
- Download page: https://statvoo.com/top/ranked
- Download link: https://statvoo.com/dl/top-1million-sites.csv.zip
### DomCop
- Download page: https://www.domcop.com/top-10-million-websites
- Download link: https://www.domcop.com/files/top/top10milliondomains.csv.zip
### Tranco
- Download page: https://tranco-list.eu/
- Download link: https://tranco-list.eu/top-1m.csv.zip
### Cloudflare
- Download page: https://radar.cloudflare.com/domains
- Download link: https://radar.cloudflare.com/charts/LargerTopDomainsTable/attachment?id=525&top=1000000
|
lucianosb/cetacean-ptbr | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 2314477136
num_examples: 1409162
download_size: 1413883118
dataset_size: 2314477136
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
language:
- pt
---
This dataset is a merge of [Open-Orca](https://huggingface.co/datasets/cnmoro/GPT4-500k-Augmented-PTBR-Clean) and [Dolphin](https://huggingface.co/datasets/JJhooww/dolphin_ptbr_alpaca_format) translated to portuguese. |
open-llm-leaderboard/details_giraffe176__WestLake_Noromaid_OpenHermes_neural-chat | ---
pretty_name: Evaluation run of giraffe176/WestLake_Noromaid_OpenHermes_neural-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [giraffe176/WestLake_Noromaid_OpenHermes_neural-chat](https://huggingface.co/giraffe176/WestLake_Noromaid_OpenHermes_neural-chat)\
\ 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_giraffe176__WestLake_Noromaid_OpenHermes_neural-chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-02T16:42:40.853852](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__WestLake_Noromaid_OpenHermes_neural-chat/blob/main/results_2024-03-02T16-42-40.853852.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\": 0.6502553416511081,\n\
\ \"acc_stderr\": 0.03213553415502459,\n \"acc_norm\": 0.6513459760802845,\n\
\ \"acc_norm_stderr\": 0.03278721251513175,\n \"mc1\": 0.390452876376989,\n\
\ \"mc1_stderr\": 0.017078230743431448,\n \"mc2\": 0.5546796539463894,\n\
\ \"mc2_stderr\": 0.015237857217982279\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6382252559726962,\n \"acc_stderr\": 0.014041957945038076,\n\
\ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.013678810399518827\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6741684923322048,\n\
\ \"acc_stderr\": 0.0046772682828393995,\n \"acc_norm\": 0.8612826130252937,\n\
\ \"acc_norm_stderr\": 0.003449449618650543\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\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\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n\
\ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778408,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778408\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\
\ \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n\
\ \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\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\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\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\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342856,\n\
\ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342856\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\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n\
\ \"acc_stderr\": 0.028125972265654373,\n \"acc_norm\": 0.7990196078431373,\n\
\ \"acc_norm_stderr\": 0.028125972265654373\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389094,\n\
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389094\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\": 0.7786259541984732,\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\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\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\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\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\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597528\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\": 0.8365261813537676,\n\
\ \"acc_stderr\": 0.01322392861674162,\n \"acc_norm\": 0.8365261813537676,\n\
\ \"acc_norm_stderr\": 0.01322392861674162\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\
\ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33631284916201115,\n\
\ \"acc_stderr\": 0.01580100372914589,\n \"acc_norm\": 0.33631284916201115,\n\
\ \"acc_norm_stderr\": 0.01580100372914589\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\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\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\
\ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\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\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\
\ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\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\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.02411267824090081,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.02411267824090081\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.390452876376989,\n\
\ \"mc1_stderr\": 0.017078230743431448,\n \"mc2\": 0.5546796539463894,\n\
\ \"mc2_stderr\": 0.015237857217982279\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8042620363062352,\n \"acc_stderr\": 0.011151145042218327\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6565579984836998,\n \
\ \"acc_stderr\": 0.01307993381180031\n }\n}\n```"
repo_url: https://huggingface.co/giraffe176/WestLake_Noromaid_OpenHermes_neural-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|arc:challenge|25_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|gsm8k|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hellaswag|10_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T16-42-40.853852.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T16-42-40.853852.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- '**/details_harness|winogrande|5_2024-03-02T16-42-40.853852.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-02T16-42-40.853852.parquet'
- config_name: results
data_files:
- split: 2024_03_02T16_42_40.853852
path:
- results_2024-03-02T16-42-40.853852.parquet
- split: latest
path:
- results_2024-03-02T16-42-40.853852.parquet
---
# Dataset Card for Evaluation run of giraffe176/WestLake_Noromaid_OpenHermes_neural-chat
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [giraffe176/WestLake_Noromaid_OpenHermes_neural-chat](https://huggingface.co/giraffe176/WestLake_Noromaid_OpenHermes_neural-chat) 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_giraffe176__WestLake_Noromaid_OpenHermes_neural-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-02T16:42:40.853852](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__WestLake_Noromaid_OpenHermes_neural-chat/blob/main/results_2024-03-02T16-42-40.853852.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": 0.6502553416511081,
"acc_stderr": 0.03213553415502459,
"acc_norm": 0.6513459760802845,
"acc_norm_stderr": 0.03278721251513175,
"mc1": 0.390452876376989,
"mc1_stderr": 0.017078230743431448,
"mc2": 0.5546796539463894,
"mc2_stderr": 0.015237857217982279
},
"harness|arc:challenge|25": {
"acc": 0.6382252559726962,
"acc_stderr": 0.014041957945038076,
"acc_norm": 0.6757679180887372,
"acc_norm_stderr": 0.013678810399518827
},
"harness|hellaswag|10": {
"acc": 0.6741684923322048,
"acc_stderr": 0.0046772682828393995,
"acc_norm": 0.8612826130252937,
"acc_norm_stderr": 0.003449449618650543
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438665,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438665
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"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": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.03208115750788684,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.03208115750788684
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778408,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778408
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4523809523809524,
"acc_stderr": 0.044518079590553275,
"acc_norm": 0.4523809523809524,
"acc_norm_stderr": 0.044518079590553275
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7774193548387097,
"acc_stderr": 0.023664216671642518,
"acc_norm": 0.7774193548387097,
"acc_norm_stderr": 0.023664216671642518
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479049,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479049
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.022935144053919443,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.022935144053919443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.023854795680971128,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.023854795680971128
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.362962962962963,
"acc_stderr": 0.02931820364520686,
"acc_norm": 0.362962962962963,
"acc_norm_stderr": 0.02931820364520686
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7016806722689075,
"acc_stderr": 0.029719142876342856,
"acc_norm": 0.7016806722689075,
"acc_norm_stderr": 0.029719142876342856
},
"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": 0.8440366972477065,
"acc_stderr": 0.01555580271359017,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654373,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654373
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7932489451476793,
"acc_stderr": 0.026361651668389094,
"acc_norm": 0.7932489451476793,
"acc_norm_stderr": 0.026361651668389094
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.03641297081313729,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.03641297081313729
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597528,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597528
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8365261813537676,
"acc_stderr": 0.01322392861674162,
"acc_norm": 0.8365261813537676,
"acc_norm_stderr": 0.01322392861674162
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.023445826276545543,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.023445826276545543
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.33631284916201115,
"acc_stderr": 0.01580100372914589,
"acc_norm": 0.33631284916201115,
"acc_norm_stderr": 0.01580100372914589
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.025160998214292456,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292456
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.025755865922632945,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7530864197530864,
"acc_stderr": 0.02399350170904211,
"acc_norm": 0.7530864197530864,
"acc_norm_stderr": 0.02399350170904211
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4716312056737589,
"acc_stderr": 0.029779450957303062,
"acc_norm": 0.4716312056737589,
"acc_norm_stderr": 0.029779450957303062
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46153846153846156,
"acc_stderr": 0.01273239828619044,
"acc_norm": 0.46153846153846156,
"acc_norm_stderr": 0.01273239828619044
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6911764705882353,
"acc_stderr": 0.02806499816704009,
"acc_norm": 0.6911764705882353,
"acc_norm_stderr": 0.02806499816704009
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.018926082916083383,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.018926082916083383
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
"acc_stderr": 0.02783302387139968,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.02783302387139968
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8656716417910447,
"acc_stderr": 0.02411267824090081,
"acc_norm": 0.8656716417910447,
"acc_norm_stderr": 0.02411267824090081
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.02796678585916089,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.02796678585916089
},
"harness|truthfulqa:mc|0": {
"mc1": 0.390452876376989,
"mc1_stderr": 0.017078230743431448,
"mc2": 0.5546796539463894,
"mc2_stderr": 0.015237857217982279
},
"harness|winogrande|5": {
"acc": 0.8042620363062352,
"acc_stderr": 0.011151145042218327
},
"harness|gsm8k|5": {
"acc": 0.6565579984836998,
"acc_stderr": 0.01307993381180031
}
}
```
## 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] |
huolongguo10/MultiChat | ---
license: openrail
task_categories:
- conversational
language:
- zh
tags:
- code
--- |
liuyanchen1015/MULTI_VALUE_mrpc_null_relcl | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 83150
num_examples: 293
- name: train
num_bytes: 189202
num_examples: 660
- name: validation
num_bytes: 21484
num_examples: 75
download_size: 203711
dataset_size: 293836
---
# Dataset Card for "MULTI_VALUE_mrpc_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
datahrvoje/twitter_dataset_1713006552 | ---
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: 20501
num_examples: 45
download_size: 11125
dataset_size: 20501
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
EgilKarlsen/Thunderbird_GPT2_Baseline | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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download_size: 0
dataset_size: 154102307.1875
---
# Dataset Card for "Thunderbird_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Amit/ddpm-butterflies-128 | ---
license: unknown
---
|
dim/yandex_q_200k | ---
dataset_info:
features:
- name: description
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 291927288.0830295
num_examples: 200000
download_size: 155069887
dataset_size: 291927288.0830295
---
# Dataset Card for "yandex_q_200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ggul-tiger/negobot_price_weak_datas | ---
dataset_info:
features:
- name: price
dtype: int64
- name: events
list:
- name: message
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 59481
num_examples: 300
download_size: 15988
dataset_size: 59481
---
# Dataset Card for "negobot_price_weak_datas"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kamyar-zeinalipour/turkish_train_v2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 57527348
num_examples: 187395
download_size: 9804859
dataset_size: 57527348
---
# Dataset Card for "turkish_train_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Romildon/boa | ---
license: openrail
---
|
menyo20k_mt | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- en
- yo
license:
- cc-by-nc-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: menyo-20k
pretty_name: MENYO-20k
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- yo
config_name: menyo20k_mt
splits:
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num_bytes: 870011
num_examples: 3397
- name: test
num_bytes: 1905432
num_examples: 6633
download_size: 5206234
dataset_size: 5326788
---
# Dataset Card for MENYO-20k
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/uds-lsv/menyo-20k_MT/
- **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Languages are English and Yoruba.
## Dataset Structure
### Data Instances
An instance example:
```
{'translation':
{'en': 'Unit 1: What is Creative Commons?',
'yo': 'Ìdá 1: Kín ni Creative Commons?'
}
}
```
### Data Fields
- `translation`:
- `en`: English sentence.
- `yo`: Yoruba sentence.
### Data Splits
Training, validation and test splits are available.
## 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
The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use.
The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE
### Citation Information
If you use this dataset, please cite this paper:
```
@inproceedings{adelani-etal-2021-effect,
title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation",
author = "Adelani, David and
Ruiter, Dana and
Alabi, Jesujoba and
Adebonojo, Damilola and
Ayeni, Adesina and
Adeyemi, Mofe and
Awokoya, Ayodele Esther and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)",
month = aug,
year = "2021",
address = "Virtual",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2021.mtsummit-research.6",
pages = "61--75",
abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.",
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
|
BigAction/mind2web_clean | ---
dataset_info:
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splits:
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num_examples: 199
download_size: 17218698
dataset_size: 105415456
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nandini82/sciq-qa | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
dtype: string
- name: lecture
dtype: string
splits:
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dataset_size: 5708965.803186403
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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path: data/validation-*
- split: test
path: data/test-*
---
|
ranimeree/mixed_data | ---
dataset_info:
features:
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dtype: image
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dtype: image
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num_examples: 348
download_size: 1432353431
dataset_size: 2020959138.73
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
AdapterOcean/Open_Platypus_standardized_cluster_6_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
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dtype: float64
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dtype: int64
splits:
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download_size: 1112562
dataset_size: 2339198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_6_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Yah216/APCD-Poem_Rawiy_detection | ---
language:
- ar
task_categories:
- text-classification
---
# AutoTrain Dataset for project: Poem_Rawiy_detection
## Dataset Descritpion
We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the Qafiyah columns were kept:
```
@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
author = {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
Moustafa A.},
title = {Learning Meters of Arabic and English Poems With Recurrent Neural Networks: a Step
Forward for Language Understanding and Synthesis},
journal = {arXiv preprint arXiv:1905.05700},
year = 2019,
url = {https://github.com/hci-lab/LearningMetersPoems}
}
```
### Languages
The BCP-47 code for the dataset's language is ar.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u0643\u0644\u0651\u064c \u064a\u064e\u0632\u0648\u0644\u064f \u0633\u064e\u0631\u064a\u0639\u0627\u064b \u0644\u0627 \u062b\u064e\u0628\u0627\u062a\u064e \u0644\u0647\u064f \u0641\u0643\u064f\u0646 \u0644\u0650\u0648\u064e\u0642\u062a\u0643\u064e \u064a\u0627 \u0645\u0650\u0633\u0643\u064a\u0646\u064f \u0645\u064f\u063a\u062a\u064e\u0646\u0650\u0645\u0627",
"target": 27
},
{
"text": "\u0648\u0642\u062f \u0623\u0628\u0631\u0632\u064e \u0627\u0644\u0631\u0651\u064f\u0645\u0651\u064e\u0627\u0646\u064f \u0644\u0644\u0637\u0631\u0641\u0650 \u063a\u064f\u0635\u0652\u0646\u064e\u0647\u064f \u0646\u0647\u0648\u062f\u0627\u064b \u062a\u064f\u0635\u0627\u0646\u064f \u0627\u0644\u0644\u0645\u0633\u064e \u0639\u0646 \u0643\u0641\u0651\u0650 \u0623\u062d\u0645\u0642\u0650",
"target": 23
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(num_classes=35, names=['\u0621', '\u0624', '\u0627', '\u0628', '\u062a', '\u062b', '\u062c', '\u062d', '\u062e', '\u062f', '\u0630', '\u0631', '\u0632', '\u0633', '\u0634', '\u0635', '\u0636', '\u0637', '\u0637\u0646', '\u0638', '\u0639', '\u063a', '\u0641', '\u0642', '\u0643', '\u0644', '\u0644\u0627', '\u0645', '\u0646', '\u0647', '\u0647\u0640', '\u0647\u0646', '\u0648', '\u0649', '\u064a'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1347718 |
| valid | 336950 |
|
nahidhasannascenia/Bangla-Instruct-Llama_alpaca-llama2-chat | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2632731
num_examples: 1185
download_size: 520404
dataset_size: 2632731
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BadreddineHug/3s_librispeech_subset | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 1832833
num_examples: 6
download_size: 518341
dataset_size: 1832833
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sefaria/links | ---
license: gpl-3.0
---
|
ttaront/filtered_clx | ---
configs:
- config_name: en
data_files: "en/*.parquet"
- config_name: ja
data_files: "ja/*.parquet"
pretty_name: filtered_clx
language:
- en
- ja
--- |
Tngarg/hindi_train | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: text
dtype: string
- name: sentiment
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 318038
num_examples: 2212
download_size: 202043
dataset_size: 318038
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hindi_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aoome123/haha | ---
dataset_info:
config_name: aoome123/use
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 5462003720
num_examples: 5687
- name: test
num_bytes: 682871112
num_examples: 711
- name: valid
num_bytes: 682869512
num_examples: 711
download_size: 902052782
dataset_size: 6827744344
configs:
- config_name: aoome123/use
data_files:
- split: train
path: aoome123/use/train-*
- split: test
path: aoome123/use/test-*
- split: valid
path: aoome123/use/valid-*
---
# Dataset Card for "important"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
test-org-usm3d/test-gated | ---
extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects."
extra_gated_fields:
Company: text
Country: country
Specific date: date_picker
I want to use this dataset for:
type: select
options:
- Research
- Education
- label: Other
value: other
I agree to use this dataset for non-commercial use ONLY: checkbox
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_description: "Our team may take 2-3 days to process your request"
extra_gated_button_content: "Acknowledge license"
--- |
PeterLawrence/processed_demo | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 26986
num_examples: 34
download_size: 5785
dataset_size: 26986
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vietgpt-archive/legal_document_vi | ---
dataset_info:
features:
- name: subject
dtype: string
- name: meta
struct:
- name: effective_date
dtype: string
- name: issuing_agency
dtype: string
- name: promulgation_date
dtype: string
- name: sign_number
dtype: string
- name: signer
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 7634485791
num_examples: 424187
download_size: 2642253186
dataset_size: 7634485791
---
# Dataset Card for "legal_document_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alvarobartt/stack-exchange-paired-mini | ---
dataset_info:
features:
- name: qid
dtype: int64
- name: question
dtype: string
- name: date
dtype: string
- name: metadata
sequence: string
- name: response_j
dtype: string
- name: response_k
dtype: string
splits:
- name: train
num_bytes: 335534
num_examples: 100
download_size: 105377
dataset_size: 335534
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
- question-answering
language:
- en
size_categories:
- n<1K
---
# StackExchange Paired Mini (100 samples)
This is a subset of the `StackExchange Paired` [lvwerra/stack-exchange-paired](https://hf.co/lvwerra/stack-exchange-paired) dataset.
## Disclaimer
For licensing or any other related detail, please refer to the original dataset linked above. |
Coco3384/name_of_your_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': test
'1': train
splits:
- name: train
num_bytes: 1260861060.079
num_examples: 10479
- name: validation
num_bytes: 5199.0
num_examples: 1
- name: test
num_bytes: 262109400.0
num_examples: 2800
download_size: 1195043893
dataset_size: 1522975659.079
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
TheFinAI/flare-causal20-sc | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 6914334
num_examples: 8628
download_size: 2501268
dataset_size: 6914334
---
# Dataset Card for "flare-causal20-sc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mbeaty2/data | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 2929210.0
num_examples: 127
download_size: 1529434
dataset_size: 2929210.0
---
# Dataset Card for "data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/0415e725 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1335
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0415e725"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Akshita15/cubelelo_data | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
dtype: 'null'
- name: prediction_agent
dtype: 'null'
- name: annotation
dtype: string
- name: annotation_agent
dtype: string
- name: vectors
struct:
- name: paraphrase-multilingual-mpnet-base-v2
sequence: float64
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 12103055
num_examples: 1922
download_size: 9829799
dataset_size: 12103055
---
# Dataset Card for "cubelelo_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NYUMets/nyumets_brats | ---
license: other
extra_gated_heading: "NYU Langone Health NYUMets Dataset Sharing Agreement"
extra_gated_prompt: "By registering for downloads from the NYUMets Dataset, I agree to this Dataset Sharing Agreement, as well as to the terms of use as posted and updated periodically at: http://nyulangone.org/policies-disclaimers/disclaimer.\nThe NYUMets Dataset is considered proprietary to and owned by New York University and NYU Langone Health (together “NYU”). Other than the rights granted herein, NYU retains all rights, title, and interest in the NYUMets Dataset.\nSubject to the provisions of this Agreement, NYU shall give to me access to and the right to download the NYUMets Dataset, and NYU hereby grants to me a non-exclusive, royalty-free license to use the NYUMets Dataset for internal research or educational purposes only and only as permitted by this Agreement. This Agreement conveys no other rights of any sort with respect to the NYUMets Dataset or the intellectual property rights embodied therein.\nI will receive AWS permissions to access the NYUMets Dataset without charge for internal research or educational purposes only. The link will permit me to download and access a verbatim copy of the NYUMets Dataset solely for such use. I will NOT SHARE THE DOWNLOADED DATA or AWS Account Access Credentials to the NYUMets Dataset with others. If another user within my organization or elsewhere wishes to obtain a copy of and use the NYUMets Dataset, they must register as an individual user and comply with all the terms of this Agreement."
extra_gated_fields:
Name: text
Email: text
Organization: text
Phone Number: text
By checking this box, you are certifying that you have read and understood the NYU Langone Health NYUMets Dataset Sharing Agreement: checkbox
---
|
boapps/kmdb_entities | ---
dataset_info:
features:
- name: id
dtype: int64
- name: people
sequence: string
- name: institutions
sequence: string
- name: places
sequence: string
- name: text
dtype: string
- name: ent_lemmas
sequence:
sequence: string
- name: ent_tokens
list:
- name: text
dtype: string
- name: lemma
dtype: string
- name: tokens
list:
- name: i
dtype: int64
- name: text
dtype: string
- name: lemma
dtype: string
- name: ent_type
dtype: string
- name: iob
dtype: string
- name: words
sequence: string
splits:
- name: train
num_bytes: 670240594
num_examples: 46914
download_size: 262010648
dataset_size: 670240594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
one-sec-cv12/chunk_149 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 17601084144.375
num_examples: 183253
download_size: 15549262193
dataset_size: 17601084144.375
---
# Dataset Card for "chunk_149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DataStudio/OCR_document_bluir_part_07 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3085454343.625
num_examples: 175835
download_size: 3081898367
dataset_size: 3085454343.625
---
# Dataset Card for "OCR_document_bluir_part_07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
graphs-datasets/MD17-salicylic_acid | ---
licence: unknown
task_categories:
- graph-ml
---
# Dataset Card for salicylic_acid
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `salicylic_acid` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`salicylic_acid` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 220231 |
| average #nodes | 16.0 |
| average #edges | 208.2681717461586 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
ademax/ocr_scan_vi_01 | ---
language: vi
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 410862389.5689411
num_examples: 11003
- name: test
num_bytes: 37340942.4310589
num_examples: 1000
download_size: 447854730
dataset_size: 448203332.0
---
# Dataset Card for "quan_ocr_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/VALUE_qqp_dey_it | ---
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: 331535
num_examples: 1870
- name: test
num_bytes: 3266804
num_examples: 18707
- name: train
num_bytes: 2881908
num_examples: 16269
download_size: 4036549
dataset_size: 6480247
---
# Dataset Card for "VALUE_qqp_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-futin__feed-top_vi-71f14a-2175469967 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: facebook/opt-1.3b
metrics: []
dataset_name: futin/feed
dataset_config: top_vi
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-1.3b
* Dataset: futin/feed
* Config: top_vi
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
SIA86/LFQAKnowledgeBase | ---
license: openrail
task_categories:
- question-answering
- text2text-generation
language:
- ru
pretty_name: LFQA_KB
size_categories:
- n<1K
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: heading1
dtype: string
- name: heading2
dtype: string
- name: heading3
dtype: string
- name: heading4
dtype: string
- name: heading5
dtype: string
- name: text
dtype: string
--- |
mrjang/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245921
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_rufjdk5480__gov-qna-ko-merged | ---
pretty_name: Evaluation run of rufjdk5480/gov-qna-ko-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rufjdk5480/gov-qna-ko-merged](https://huggingface.co/rufjdk5480/gov-qna-ko-merged)\
\ 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_rufjdk5480__gov-qna-ko-merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-05T04:31:12.088602](https://huggingface.co/datasets/open-llm-leaderboard/details_rufjdk5480__gov-qna-ko-merged/blob/main/results_2024-01-05T04-31-12.088602.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\": 0.6966669201907558,\n\
\ \"acc_stderr\": 0.03014874135400622,\n \"acc_norm\": 0.7075970828086718,\n\
\ \"acc_norm_stderr\": 0.03073409651050876,\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.48607139277849154,\n\
\ \"mc2_stderr\": 0.01710096370379909\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3506825938566553,\n \"acc_stderr\": 0.013944635930726089,\n\
\ \"acc_norm\": 0.39505119453924914,\n \"acc_norm_stderr\": 0.014285898292938165\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.33917546305516827,\n\
\ \"acc_stderr\": 0.004724619193427588,\n \"acc_norm\": 0.39055964947221666,\n\
\ \"acc_norm_stderr\": 0.004868787333436588\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\
\ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\
\ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8355263157894737,\n \"acc_stderr\": 0.03016753346863271,\n\
\ \"acc_norm\": 0.8355263157894737,\n \"acc_norm_stderr\": 0.03016753346863271\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n\
\ \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.71,\n \
\ \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7811320754716982,\n \"acc_stderr\": 0.0254478638251086,\n\
\ \"acc_norm\": 0.7811320754716982,\n \"acc_norm_stderr\": 0.0254478638251086\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8680555555555556,\n\
\ \"acc_stderr\": 0.02830096838204443,\n \"acc_norm\": 0.8680555555555556,\n\
\ \"acc_norm_stderr\": 0.02830096838204443\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\
: 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\
\ \"acc_stderr\": 0.034961014811911786,\n \"acc_norm\": 0.6994219653179191,\n\
\ \"acc_norm_stderr\": 0.034961014811911786\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n\
\ \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n\
\ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745636,\n\
\ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745636\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6403508771929824,\n\
\ \"acc_stderr\": 0.04514496132873633,\n \"acc_norm\": 0.6403508771929824,\n\
\ \"acc_norm_stderr\": 0.04514496132873633\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6896551724137931,\n \"acc_stderr\": 0.038552896163789485,\n\
\ \"acc_norm\": 0.6896551724137931,\n \"acc_norm_stderr\": 0.038552896163789485\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\
acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.8451612903225807,\n\
\ \"acc_stderr\": 0.020579287326583227,\n \"acc_norm\": 0.8451612903225807,\n\
\ \"acc_norm_stderr\": 0.020579287326583227\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n\
\ \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\
\ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8737373737373737,\n \"acc_stderr\": 0.023664359402880236,\n \"\
acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.023664359402880236\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n\
\ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7076923076923077,\n \"acc_stderr\": 0.023060438380857726,\n\
\ \"acc_norm\": 0.7076923076923077,\n \"acc_norm_stderr\": 0.023060438380857726\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7899159663865546,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.7899159663865546,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\
acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6296296296296297,\n \"acc_stderr\": 0.03293377139415191,\n \"\
acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.03293377139415191\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8627450980392157,\n \"acc_stderr\": 0.024152225962801588,\n \"\
acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.024152225962801588\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8818565400843882,\n \"acc_stderr\": 0.021011052659878453,\n \
\ \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.021011052659878453\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7713004484304933,\n\
\ \"acc_stderr\": 0.028188240046929203,\n \"acc_norm\": 0.7713004484304933,\n\
\ \"acc_norm_stderr\": 0.028188240046929203\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476074,\n\
\ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476074\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"\
acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5982142857142857,\n\
\ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\
\ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761012,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761012\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\
\ \"acc_stderr\": 0.017893784904018533,\n \"acc_norm\": 0.9188034188034188,\n\
\ \"acc_norm_stderr\": 0.017893784904018533\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \
\ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8735632183908046,\n\
\ \"acc_stderr\": 0.011884488905895555,\n \"acc_norm\": 0.8735632183908046,\n\
\ \"acc_norm_stderr\": 0.011884488905895555\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8034682080924855,\n \"acc_stderr\": 0.021393961404363847,\n\
\ \"acc_norm\": 0.8034682080924855,\n \"acc_norm_stderr\": 0.021393961404363847\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3564245810055866,\n\
\ \"acc_stderr\": 0.016018239710513398,\n \"acc_norm\": 0.3564245810055866,\n\
\ \"acc_norm_stderr\": 0.016018239710513398\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.826797385620915,\n \"acc_stderr\": 0.021668400256514276,\n\
\ \"acc_norm\": 0.826797385620915,\n \"acc_norm_stderr\": 0.021668400256514276\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.797427652733119,\n\
\ \"acc_stderr\": 0.022827317491059686,\n \"acc_norm\": 0.797427652733119,\n\
\ \"acc_norm_stderr\": 0.022827317491059686\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999850994,\n\
\ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999850994\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5460992907801419,\n \"acc_stderr\": 0.02970045324729147,\n \
\ \"acc_norm\": 0.5460992907801419,\n \"acc_norm_stderr\": 0.02970045324729147\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.546284224250326,\n\
\ \"acc_stderr\": 0.012715404841277748,\n \"acc_norm\": 0.546284224250326,\n\
\ \"acc_norm_stderr\": 0.012715404841277748\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\": 0.7859477124183006,\n \"acc_stderr\": 0.016593429662329035,\n \
\ \"acc_norm\": 0.7859477124183006,\n \"acc_norm_stderr\": 0.016593429662329035\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8040816326530612,\n \"acc_stderr\": 0.025409301953225678,\n\
\ \"acc_norm\": 0.8040816326530612,\n \"acc_norm_stderr\": 0.025409301953225678\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\
\ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\
\ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.48607139277849154,\n\
\ \"mc2_stderr\": 0.01710096370379909\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5674822415153907,\n \"acc_stderr\": 0.013923911578623823\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2767247915087187,\n \
\ \"acc_stderr\": 0.012323047397959787\n }\n}\n```"
repo_url: https://huggingface.co/rufjdk5480/gov-qna-ko-merged
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_05T04_31_12.088602
path:
- '**/details_harness|arc:challenge|25_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|gsm8k|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hellaswag|10_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T04-31-12.088602.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T04-31-12.088602.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- '**/details_harness|winogrande|5_2024-01-05T04-31-12.088602.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-05T04-31-12.088602.parquet'
- config_name: results
data_files:
- split: 2024_01_05T04_31_12.088602
path:
- results_2024-01-05T04-31-12.088602.parquet
- split: latest
path:
- results_2024-01-05T04-31-12.088602.parquet
---
# Dataset Card for Evaluation run of rufjdk5480/gov-qna-ko-merged
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rufjdk5480/gov-qna-ko-merged](https://huggingface.co/rufjdk5480/gov-qna-ko-merged) 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_rufjdk5480__gov-qna-ko-merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-05T04:31:12.088602](https://huggingface.co/datasets/open-llm-leaderboard/details_rufjdk5480__gov-qna-ko-merged/blob/main/results_2024-01-05T04-31-12.088602.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": 0.6966669201907558,
"acc_stderr": 0.03014874135400622,
"acc_norm": 0.7075970828086718,
"acc_norm_stderr": 0.03073409651050876,
"mc1": 0.2178702570379437,
"mc1_stderr": 0.014450846714123892,
"mc2": 0.48607139277849154,
"mc2_stderr": 0.01710096370379909
},
"harness|arc:challenge|25": {
"acc": 0.3506825938566553,
"acc_stderr": 0.013944635930726089,
"acc_norm": 0.39505119453924914,
"acc_norm_stderr": 0.014285898292938165
},
"harness|hellaswag|10": {
"acc": 0.33917546305516827,
"acc_stderr": 0.004724619193427588,
"acc_norm": 0.39055964947221666,
"acc_norm_stderr": 0.004868787333436588
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6888888888888889,
"acc_stderr": 0.039992628766177214,
"acc_norm": 0.6888888888888889,
"acc_norm_stderr": 0.039992628766177214
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8355263157894737,
"acc_stderr": 0.03016753346863271,
"acc_norm": 0.8355263157894737,
"acc_norm_stderr": 0.03016753346863271
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.71,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.71,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7811320754716982,
"acc_stderr": 0.0254478638251086,
"acc_norm": 0.7811320754716982,
"acc_norm_stderr": 0.0254478638251086
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8680555555555556,
"acc_stderr": 0.02830096838204443,
"acc_norm": 0.8680555555555556,
"acc_norm_stderr": 0.02830096838204443
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.034961014811911786,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.034961014811911786
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5,
"acc_stderr": 0.04975185951049946,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04975185951049946
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6680851063829787,
"acc_stderr": 0.030783736757745636,
"acc_norm": 0.6680851063829787,
"acc_norm_stderr": 0.030783736757745636
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.6403508771929824,
"acc_stderr": 0.04514496132873633,
"acc_norm": 0.6403508771929824,
"acc_norm_stderr": 0.04514496132873633
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6896551724137931,
"acc_stderr": 0.038552896163789485,
"acc_norm": 0.6896551724137931,
"acc_norm_stderr": 0.038552896163789485
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.47883597883597884,
"acc_stderr": 0.025728230952130726,
"acc_norm": 0.47883597883597884,
"acc_norm_stderr": 0.025728230952130726
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5238095238095238,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.5238095238095238,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8451612903225807,
"acc_stderr": 0.020579287326583227,
"acc_norm": 0.8451612903225807,
"acc_norm_stderr": 0.020579287326583227
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6354679802955665,
"acc_stderr": 0.0338640574606209,
"acc_norm": 0.6354679802955665,
"acc_norm_stderr": 0.0338640574606209
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8121212121212121,
"acc_stderr": 0.03050193405942914,
"acc_norm": 0.8121212121212121,
"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8737373737373737,
"acc_stderr": 0.023664359402880236,
"acc_norm": 0.8737373737373737,
"acc_norm_stderr": 0.023664359402880236
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9378238341968912,
"acc_stderr": 0.017426974154240524,
"acc_norm": 0.9378238341968912,
"acc_norm_stderr": 0.017426974154240524
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7076923076923077,
"acc_stderr": 0.023060438380857726,
"acc_norm": 0.7076923076923077,
"acc_norm_stderr": 0.023060438380857726
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.02944316932303154,
"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7899159663865546,
"acc_stderr": 0.026461398717471874,
"acc_norm": 0.7899159663865546,
"acc_norm_stderr": 0.026461398717471874
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4966887417218543,
"acc_stderr": 0.04082393379449654,
"acc_norm": 0.4966887417218543,
"acc_norm_stderr": 0.04082393379449654
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8844036697247707,
"acc_stderr": 0.01370874953417264,
"acc_norm": 0.8844036697247707,
"acc_norm_stderr": 0.01370874953417264
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.03293377139415191,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.03293377139415191
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8627450980392157,
"acc_stderr": 0.024152225962801588,
"acc_norm": 0.8627450980392157,
"acc_norm_stderr": 0.024152225962801588
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8818565400843882,
"acc_stderr": 0.021011052659878453,
"acc_norm": 0.8818565400843882,
"acc_norm_stderr": 0.021011052659878453
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7713004484304933,
"acc_stderr": 0.028188240046929203,
"acc_norm": 0.7713004484304933,
"acc_norm_stderr": 0.028188240046929203
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8244274809160306,
"acc_stderr": 0.03336820338476074,
"acc_norm": 0.8244274809160306,
"acc_norm_stderr": 0.03336820338476074
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8512396694214877,
"acc_stderr": 0.03248470083807194,
"acc_norm": 0.8512396694214877,
"acc_norm_stderr": 0.03248470083807194
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.03602814176392645,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.03602814176392645
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5982142857142857,
"acc_stderr": 0.04653333146973647,
"acc_norm": 0.5982142857142857,
"acc_norm_stderr": 0.04653333146973647
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
"acc_stderr": 0.03393295729761012,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.03393295729761012
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9188034188034188,
"acc_stderr": 0.017893784904018533,
"acc_norm": 0.9188034188034188,
"acc_norm_stderr": 0.017893784904018533
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.78,
"acc_stderr": 0.041633319989322626,
"acc_norm": 0.78,
"acc_norm_stderr": 0.041633319989322626
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8735632183908046,
"acc_stderr": 0.011884488905895555,
"acc_norm": 0.8735632183908046,
"acc_norm_stderr": 0.011884488905895555
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8034682080924855,
"acc_stderr": 0.021393961404363847,
"acc_norm": 0.8034682080924855,
"acc_norm_stderr": 0.021393961404363847
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3564245810055866,
"acc_stderr": 0.016018239710513398,
"acc_norm": 0.3564245810055866,
"acc_norm_stderr": 0.016018239710513398
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.826797385620915,
"acc_stderr": 0.021668400256514276,
"acc_norm": 0.826797385620915,
"acc_norm_stderr": 0.021668400256514276
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.797427652733119,
"acc_stderr": 0.022827317491059686,
"acc_norm": 0.797427652733119,
"acc_norm_stderr": 0.022827317491059686
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.845679012345679,
"acc_stderr": 0.020100830999850994,
"acc_norm": 0.845679012345679,
"acc_norm_stderr": 0.020100830999850994
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5460992907801419,
"acc_stderr": 0.02970045324729147,
"acc_norm": 0.5460992907801419,
"acc_norm_stderr": 0.02970045324729147
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.546284224250326,
"acc_stderr": 0.012715404841277748,
"acc_norm": 0.546284224250326,
"acc_norm_stderr": 0.012715404841277748
},
"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": 0.7859477124183006,
"acc_stderr": 0.016593429662329035,
"acc_norm": 0.7859477124183006,
"acc_norm_stderr": 0.016593429662329035
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.04309118709946458,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.04309118709946458
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8040816326530612,
"acc_stderr": 0.025409301953225678,
"acc_norm": 0.8040816326530612,
"acc_norm_stderr": 0.025409301953225678
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8855721393034826,
"acc_stderr": 0.022509345325101706,
"acc_norm": 0.8855721393034826,
"acc_norm_stderr": 0.022509345325101706
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.91,
"acc_stderr": 0.028762349126466125,
"acc_norm": 0.91,
"acc_norm_stderr": 0.028762349126466125
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8830409356725146,
"acc_stderr": 0.024648068961366152,
"acc_norm": 0.8830409356725146,
"acc_norm_stderr": 0.024648068961366152
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2178702570379437,
"mc1_stderr": 0.014450846714123892,
"mc2": 0.48607139277849154,
"mc2_stderr": 0.01710096370379909
},
"harness|winogrande|5": {
"acc": 0.5674822415153907,
"acc_stderr": 0.013923911578623823
},
"harness|gsm8k|5": {
"acc": 0.2767247915087187,
"acc_stderr": 0.012323047397959787
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
IndonesiaAI/sft-dataset | ---
dataset_info:
features:
- name: qid
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 7696896445.5759535
num_examples: 3798835
- name: test
num_bytes: 855211166.424046
num_examples: 422093
download_size: 4782631024
dataset_size: 8552107612.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-4000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 999403
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ahmed167/floor-plans-dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1790707.0
num_examples: 31
download_size: 1747568
dataset_size: 1790707.0
---
# Dataset Card for "floor-plans-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NLPC-UOM/Travel-Dataset-5000 | ---
language:
- en
license:
- mit
---
This question base consits of 5000 travel domain based questions which are being annotated under a taxonomy related to the travel domain. The taxonomy is a hierarchical taxonomy with two levels of 7 coarse classes and 63 fine classes. 5000TravelQuestionsDataset.xlsx file consists of the annotated question base and the taxonomy. For the question base only use 5000TravelQuestionsDataset.csv file.
If you use this data set in your reserch work, cite it as Kahaduwa, H., Pathirana, D., Arachchi, P.L., Dias, V., Ranathunga, S. and Kohomban, U., 2017, May. Question Answering system for the travel domain. In Engineering Research Conference (MERCon), 2017 Moratuwa (pp. 449-454). IEEE.
If you need more clarifications please contact through following email addresses.
Pathum - pathum.12@cse.mrt.ac.lk Dilshan -pathirana.12@cse.mrt.ac.lk Hasangi - hasangik.12@cse.mrt.ac.lk Vishma - vishma.12@cse.mrt.ac.lk |
ZurabDz/bart_tokenized_data_bpe_byte_level | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 704227420
num_examples: 2708567
download_size: 437699986
dataset_size: 704227420
---
# Dataset Card for "bart_tokenized_data_bpe_byte_level"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
csebuetnlp/illusionVQA-Soft-Localization | ---
language:
- en
size_categories:
- n<1K
task_categories:
- image-to-text
- visual-question-answering
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
- name: category
dtype: string
- name: reasoning
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 50339
num_examples: 4
- name: test
num_bytes: 24579079
num_examples: 1000
download_size: 24495650
dataset_size: 24629418
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: cc-by-nc-sa-4.0
---
# IllusionVQA: Optical Illusion Dataset
[Project Page](https://illusionvqa.github.io/) |
[Paper](https://arxiv.org/abs/2403.15952) |
[Github](https://github.com/csebuetnlp/IllusionVQA/)
## TL;DR
IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively.
## Usage
```python
from datasets import load_dataset
import base64
from openai import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
def encode_image(pil_image):
temp_name = "temp.jpg"
pil_image.save(temp_name)
with open(temp_name, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def construct_mcq(options, correct_option):
correct_option_letter = None
i = "a"
mcq = ""
for option in options:
if option == correct_option:
correct_option_letter = i
mcq += f"{i}. {option}\n"
i = chr(ord(i) + 1)
mcq = mcq[:-1]
return mcq, correct_option_letter
def add_row(content, data, i, with_answer=False):
mcq, correct_option_letter = construct_mcq(data["options"], data["answer"])
content.append({ "type": "text",
"text": "Image " + str(i) + ": " + data["question"] + "\n" + mcq })
content.append({ "type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{encode_image(data['image'])}",
"detail": "low"}})
if with_answer:
content.append({"type": "text", "text": "Answer {}: ".format(i) + correct_option_letter})
else:
content.append({"type": "text", "text": "Answer {}: ".format(i), })
return content
dataset = load_dataset("csebuetnlp/illusionVQA-Comprehension")
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
content = [{
"type": "text",
"text": "You'll be given an image, an instruction and some choices. You have to select the correct one. Do not explain your reasoning. Answer with the option's letter from the given choices directly. Here are a few examples:",
}]
### Add a few examples
for i, data in enumerate(dataset["train"], 1):
content = add_row(content, data, i, with_answer=True)
content.append({"type": "text", "text": "Now you try it!",})
next_idx = i + 1
### Add the test data
test_data = dataset["test"][0]
content_t = add_row(content.copy(), test_data, next_idx, with_answer=False)
### Get the answer from GPT-4
response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[{"role": "user","content": content_t,}],
max_tokens=5,
)
gpt4_answer = response.choices[0].message.content
print(gpt4_answer)
```
## License
This dataset is made available for non-commercial research purposes only under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). The dataset may not be used for training models. The dataset contains images collected from the internet. While permission has been obtained from some of the images' creators, permission has not yet been received from all creators. If you believe any image in this dataset is used without proper permission and you are the copyright holder, please email <a href="mailto:sameen2080@gmail.com">Haz Sameen Shahgir</a> to request the removal of the image from the dataset.
The dataset creator makes no representations or warranties regarding the copyright status of the images in the dataset. The dataset creator shall not be held liable for any unauthorized use of copyrighted material that may be contained in the dataset.
You agree to the terms and conditions specified in this license by downloading or using this dataset. If you do not agree with these terms, do not download or use the dataset.
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>
### Citation
```
@article{shahgir2024illusionvqa,
title={IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models},
author={Haz Sameen Shahgir and Khondker Salman Sayeed and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yue Dong and Rifat Shahriyar},
year={2024},
url={https://arxiv.org/abs/2403.15952},
}
``` |
nicolof88/spider_train_prompts | ---
license: apache-2.0
---
|
qgallouedec/prj_gia_dataset_metaworld_handle_pull_side_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the handle-pull-side-v2 environment, sample for the policy handle-pull-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_pull_side_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_pull_side_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
open-llm-leaderboard/details_TFLai__Luban-Platypus2-13B-QLora-0.80-epoch | ---
pretty_name: Evaluation run of TFLai/Luban-Platypus2-13B-QLora-0.80-epoch
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/Luban-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Luban-Platypus2-13B-QLora-0.80-epoch)\
\ 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_TFLai__Luban-Platypus2-13B-QLora-0.80-epoch\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T15:34:59.483409](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Luban-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T15-34-59.483409.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\": 0.022546140939597316,\n\
\ \"em_stderr\": 0.0015202810875087171,\n \"f1\": 0.12950398489932863,\n\
\ \"f1_stderr\": 0.002336366110485991,\n \"acc\": 0.3814234073910959,\n\
\ \"acc_stderr\": 0.0073618459091066004\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.022546140939597316,\n \"em_stderr\": 0.0015202810875087171,\n\
\ \"f1\": 0.12950398489932863,\n \"f1_stderr\": 0.002336366110485991\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \
\ \"acc_stderr\": 0.002615326510775673\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7537490134175217,\n \"acc_stderr\": 0.012108365307437528\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TFLai/Luban-Platypus2-13B-QLora-0.80-epoch
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|arc:challenge|25_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T15_34_59.483409
path:
- '**/details_harness|drop|3_2023-10-22T15-34-59.483409.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T15-34-59.483409.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T15_34_59.483409
path:
- '**/details_harness|gsm8k|5_2023-10-22T15-34-59.483409.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T15-34-59.483409.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hellaswag|10_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T01:02:30.667173.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T01:02:30.667173.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T01:02:30.667173.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T15_34_59.483409
path:
- '**/details_harness|winogrande|5_2023-10-22T15-34-59.483409.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T15-34-59.483409.parquet'
- config_name: results
data_files:
- split: 2023_08_30T01_02_30.667173
path:
- results_2023-08-30T01:02:30.667173.parquet
- split: 2023_10_22T15_34_59.483409
path:
- results_2023-10-22T15-34-59.483409.parquet
- split: latest
path:
- results_2023-10-22T15-34-59.483409.parquet
---
# Dataset Card for Evaluation run of TFLai/Luban-Platypus2-13B-QLora-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Luban-Platypus2-13B-QLora-0.80-epoch
- **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 [TFLai/Luban-Platypus2-13B-QLora-0.80-epoch](https://huggingface.co/TFLai/Luban-Platypus2-13B-QLora-0.80-epoch) 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_TFLai__Luban-Platypus2-13B-QLora-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T15:34:59.483409](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Luban-Platypus2-13B-QLora-0.80-epoch/blob/main/results_2023-10-22T15-34-59.483409.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": 0.022546140939597316,
"em_stderr": 0.0015202810875087171,
"f1": 0.12950398489932863,
"f1_stderr": 0.002336366110485991,
"acc": 0.3814234073910959,
"acc_stderr": 0.0073618459091066004
},
"harness|drop|3": {
"em": 0.022546140939597316,
"em_stderr": 0.0015202810875087171,
"f1": 0.12950398489932863,
"f1_stderr": 0.002336366110485991
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775673
},
"harness|winogrande|5": {
"acc": 0.7537490134175217,
"acc_stderr": 0.012108365307437528
}
}
```
### 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] |
Seanxh/twitter_dataset_1713086345 | ---
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: 23637
num_examples: 53
download_size: 13242
dataset_size: 23637
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZHLiu627/ultrafeedback_binarized_with_response_full_part0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: reference_response
dtype: string
splits:
- name: train_prefs
num_bytes: 165761185
num_examples: 20000
download_size: 92065089
dataset_size: 165761185
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
# Dataset Card for "ultrafeedback_binarized_with_response_full_part0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DONG19/CoT_instruction_20k | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 6132650
num_examples: 20022
download_size: 0
dataset_size: 6132650
---
# Dataset Card for "CoT_instruction_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Delcos__Velara | ---
pretty_name: Evaluation run of Delcos/Velara
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Delcos/Velara](https://huggingface.co/Delcos/Velara) 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_Delcos__Velara\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-08T00:16:45.141900](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__Velara/blob/main/results_2023-12-08T00-16-45.141900.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\": 0.5941805681088884,\n\
\ \"acc_stderr\": 0.03328213036591988,\n \"acc_norm\": 0.5983564094269671,\n\
\ \"acc_norm_stderr\": 0.03395331581770101,\n \"mc1\": 0.2778457772337821,\n\
\ \"mc1_stderr\": 0.015680929364024637,\n \"mc2\": 0.44699355725588724,\n\
\ \"mc2_stderr\": 0.015255919110214552\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5708191126279863,\n \"acc_stderr\": 0.014464085894870653,\n\
\ \"acc_norm\": 0.5895904436860068,\n \"acc_norm_stderr\": 0.014374922192642664\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6477793268273252,\n\
\ \"acc_stderr\": 0.004766860907171539,\n \"acc_norm\": 0.8283210515833499,\n\
\ \"acc_norm_stderr\": 0.00376330474609875\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.03899073687357334,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.03899073687357334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n\
\ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.5549132947976878,\n\
\ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.032650194750335815,\n\
\ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.032650194750335815\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4365079365079365,\n \"acc_stderr\": 0.025542846817400513,\n \"\
acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.025542846817400513\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.6806451612903226,\n\
\ \"acc_stderr\": 0.026522709674667768,\n \"acc_norm\": 0.6806451612903226,\n\
\ \"acc_norm_stderr\": 0.026522709674667768\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n\
\ \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306433,\n\
\ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306433\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5769230769230769,\n \"acc_stderr\": 0.02504919787604234,\n \
\ \"acc_norm\": 0.5769230769230769,\n \"acc_norm_stderr\": 0.02504919787604234\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3,\n \"acc_stderr\": 0.027940457136228402,\n \"acc_norm\"\
: 0.3,\n \"acc_norm_stderr\": 0.027940457136228402\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\
: {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"\
acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.763302752293578,\n \"acc_stderr\": 0.0182240781172991,\n \"acc_norm\"\
: 0.763302752293578,\n \"acc_norm_stderr\": 0.0182240781172991\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.03376922151252336,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.03376922151252336\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\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808517,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808517\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\
\ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\
\ \"acc_stderr\": 0.025140935950335445,\n \"acc_norm\": 0.8205128205128205,\n\
\ \"acc_norm_stderr\": 0.025140935950335445\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7803320561941252,\n\
\ \"acc_stderr\": 0.014805384478371151,\n \"acc_norm\": 0.7803320561941252,\n\
\ \"acc_norm_stderr\": 0.014805384478371151\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6560693641618497,\n \"acc_stderr\": 0.025574123786546665,\n\
\ \"acc_norm\": 0.6560693641618497,\n \"acc_norm_stderr\": 0.025574123786546665\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.0142426300705749,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.0142426300705749\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6601307189542484,\n \"acc_stderr\": 0.027121956071388856,\n\
\ \"acc_norm\": 0.6601307189542484,\n \"acc_norm_stderr\": 0.027121956071388856\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.662379421221865,\n\
\ \"acc_stderr\": 0.02685882587948853,\n \"acc_norm\": 0.662379421221865,\n\
\ \"acc_norm_stderr\": 0.02685882587948853\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.02604176620271716,\n\
\ \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.02604176620271716\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829707,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829707\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4511082138200782,\n\
\ \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n\
\ \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5919117647058824,\n \"acc_stderr\": 0.029855261393483924,\n\
\ \"acc_norm\": 0.5919117647058824,\n \"acc_norm_stderr\": 0.029855261393483924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6209150326797386,\n \"acc_stderr\": 0.01962744474841223,\n \
\ \"acc_norm\": 0.6209150326797386,\n \"acc_norm_stderr\": 0.01962744474841223\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6857142857142857,\n \"acc_stderr\": 0.029719329422417475,\n\
\ \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.029719329422417475\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7910447761194029,\n\
\ \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n\
\ \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2778457772337821,\n\
\ \"mc1_stderr\": 0.015680929364024637,\n \"mc2\": 0.44699355725588724,\n\
\ \"mc2_stderr\": 0.015255919110214552\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637563\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.40333586050037906,\n \
\ \"acc_stderr\": 0.013512654781814687\n }\n}\n```"
repo_url: https://huggingface.co/Delcos/Velara
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_08T00_16_45.141900
path:
- '**/details_harness|arc:challenge|25_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|gsm8k|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hellaswag|10_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-16-45.141900.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-08T00-16-45.141900.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- '**/details_harness|winogrande|5_2023-12-08T00-16-45.141900.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-08T00-16-45.141900.parquet'
- config_name: results
data_files:
- split: 2023_12_08T00_16_45.141900
path:
- results_2023-12-08T00-16-45.141900.parquet
- split: latest
path:
- results_2023-12-08T00-16-45.141900.parquet
---
# Dataset Card for Evaluation run of Delcos/Velara
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Delcos/Velara
- **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 [Delcos/Velara](https://huggingface.co/Delcos/Velara) 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_Delcos__Velara",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-08T00:16:45.141900](https://huggingface.co/datasets/open-llm-leaderboard/details_Delcos__Velara/blob/main/results_2023-12-08T00-16-45.141900.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": 0.5941805681088884,
"acc_stderr": 0.03328213036591988,
"acc_norm": 0.5983564094269671,
"acc_norm_stderr": 0.03395331581770101,
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024637,
"mc2": 0.44699355725588724,
"mc2_stderr": 0.015255919110214552
},
"harness|arc:challenge|25": {
"acc": 0.5708191126279863,
"acc_stderr": 0.014464085894870653,
"acc_norm": 0.5895904436860068,
"acc_norm_stderr": 0.014374922192642664
},
"harness|hellaswag|10": {
"acc": 0.6477793268273252,
"acc_stderr": 0.004766860907171539,
"acc_norm": 0.8283210515833499,
"acc_norm_stderr": 0.00376330474609875
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.030052580579557845,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.030052580579557845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
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"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.03899073687357334
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
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"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm": 0.5549132947976878,
"acc_norm_stderr": 0.03789401760283647
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.71,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5234042553191489,
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"acc_norm": 0.5234042553191489,
"acc_norm_stderr": 0.032650194750335815
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.04657047260594964,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.04657047260594964
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4689655172413793,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.4689655172413793,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4365079365079365,
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"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.025542846817400513
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.6806451612903226,
"acc_norm_stderr": 0.026522709674667768
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4630541871921182,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.4630541871921182,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.69,
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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": 0.8549222797927462,
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"acc_norm_stderr": 0.025416343096306433
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.5769230769230769,
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"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3,
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"acc_norm": 0.3,
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6176470588235294,
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"acc_norm": 0.6176470588235294,
"acc_norm_stderr": 0.031566630992154156
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2847682119205298,
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"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.03684881521389024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.763302752293578,
"acc_stderr": 0.0182240781172991,
"acc_norm": 0.763302752293578,
"acc_norm_stderr": 0.0182240781172991
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.03376922151252336,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.03376922151252336
},
"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": 0.7679324894514767,
"acc_stderr": 0.027479744550808517,
"acc_norm": 0.7679324894514767,
"acc_norm_stderr": 0.027479744550808517
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.648854961832061,
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"acc_norm": 0.648854961832061,
"acc_norm_stderr": 0.04186445163013751
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.04236511258094632,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.04236511258094632
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690878,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690878
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8205128205128205,
"acc_stderr": 0.025140935950335445,
"acc_norm": 0.8205128205128205,
"acc_norm_stderr": 0.025140935950335445
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7803320561941252,
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"acc_norm": 0.7803320561941252,
"acc_norm_stderr": 0.014805384478371151
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6560693641618497,
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"acc_norm": 0.6560693641618497,
"acc_norm_stderr": 0.025574123786546665
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23798882681564246,
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"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.0142426300705749
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.6601307189542484,
"acc_norm_stderr": 0.027121956071388856
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.662379421221865,
"acc_norm_stderr": 0.02685882587948853
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.02604176620271716
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"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm_stderr": 0.012709037347346233
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.029855261393483924
},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.6857142857142857,
"acc_norm_stderr": 0.029719329422417475
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.7910447761194029,
"acc_norm_stderr": 0.028748298931728655
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
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"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835816
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7426900584795322,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.7426900584795322,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024637,
"mc2": 0.44699355725588724,
"mc2_stderr": 0.015255919110214552
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637563
},
"harness|gsm8k|5": {
"acc": 0.40333586050037906,
"acc_stderr": 0.013512654781814687
}
}
```
### 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] |
DeepFoldProtein/foldseek_over70_ss_proteome_processed_1024_ankh_sst8_70_test | ---
dataset_info:
features:
- name: uniprotAccession
dtype: string
- name: chain_id
dtype: string
- name: seq
dtype: string
- name: sst3
dtype: string
- name: sst8
dtype: string
- name: len
dtype: int64
- name: confidenceScore
sequence: float64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
- name: label
sequence: int64
- name: loss_mask
sequence: int64
splits:
- name: train
num_bytes: 1915136
num_examples: 99
download_size: 128542
dataset_size: 1915136
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-from-one-sec-cv12/chunk_252 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 896545004
num_examples: 174697
download_size: 915076502
dataset_size: 896545004
---
# Dataset Card for "chunk_252"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Abijith/car-damage-segmentation-small | ---
license: apache-2.0
---
# Car damage segmentation sample dataset |
Nerfgun3/brush_style | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Brush Style Embedding / Textual Inversion
## Usage
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
To use it in a prompt: ```"art by brush_style"```
If it is to strong just add [] around it.
Trained until 10000 steps
I added a 7.5k steps trained ver in the files aswell. If you want to use that version, remove the ```"-7500"``` from the file name and replace the 10k steps ver in your folder
Have fun :)
## Example Pictures
<table>
<tr>
<td><img src=https://i.imgur.com/Mp2F6GR.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/a2Cmqb4.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/YwSafu4.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/fCFSIs5.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/S8v6sXG.png width=100% height=100%/></td>
</tr>
</table>
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
abdoelsayed/Open-ArabicaQA | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
license: mit
task_categories:
- question-answering
language:
- ar
pretty_name: abdoelsayed/Open-ArabicaQA
size_categories:
- 10K<n<100K
---
# ArabicaQA
ArabicaQA: Comprehensive Dataset for Arabic Question Answering
This repository contains dataset for paper *ArabicaQA: Comprehensive Dataset for Arabic Question Answering*. Below, we provide details regarding the materials available in this repository:
ArabicaQA is a robust dataset designed to support and advance the development of Arabic Question Answering (QA) systems. This dataset encompasses a wide range of question types, including both Machine Reading Comprehension (MRC) and Open-Domain questions, catering to various aspects of QA research and application. The dataset is structured to facilitate training, validation, and testing of Arabic QA models.
For more detail https://github.com/DataScienceUIBK/ArabicaQA/tree/main
## Dataset
Within this folder, you will find the training, validation, and test sets of the ArabicaQA dataset. Refer to the table below for the dataset statistics:
| | Training | Validation | Test |
| -------------------|----------|------------|--------|
| MRC (with answers) | 62,186 | 13,483 | 13,426 |
| MRC (unanswerable) | 2,596 | 561 | 544 |
| Open-Domain | 62,057 | 13,475 | 13,414 |
| Open-Domain | 58,528 | 12,541 | 12,541 |
## Citation
If you find these codes or data useful, please consider citing our paper as:
```
@misc{abdallah2024arabicaqa,
title={ArabicaQA: A Comprehensive Dataset for Arabic Question Answering},
author={Abdelrahman Abdallah and Mahmoud Kasem and Mahmoud Abdalla and Mohamed Mahmoud and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
year={2024},
eprint={2403.17848},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Santosh-Gupta/EncephalitisParagraphEmbeddings | ---
license: mit
dataset_info:
features:
- name: paragraph_embeddings
sequence: float32
splits:
- name: train
num_bytes: 1578707784
num_examples: 513234
download_size: 1897946417
dataset_size: 1578707784
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
otavinshow/karlvoz | ---
license: openrail
---
|
jlbaker361/addition_decimal | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 2145709.8
num_examples: 29376
- name: test
num_bytes: 238412.2
num_examples: 3264
download_size: 884683
dataset_size: 2384122.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "addition_decimal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SaffalPoosh/DM-vton-test | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: person_images
dtype: image
- name: cloth_images
dtype: image
- name: cloth_edge_images
dtype: image
splits:
- name: test
num_bytes: 6878828.0
num_examples: 416
download_size: 6469560
dataset_size: 6878828.0
---
# Dataset Card for "DM-vton"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thomasgauthier/observation_or_evaluation | ---
task_categories:
- text-classification
tags:
- Synthetic
- Nonviolent communication
- Empathetic understanding
language:
- en
multilinguality:
- monolingual
language_creators:
- book
- tv_script
annotations_creators:
- machine-generated
size_categories:
- 1K<n<10K
license:
- apache-2.0
---
# Dataset Card for "Observation or evaluation"
## Dataset Description
- **Homepage:** [Blog post](https://thomasgauthier.dev/devlog/nvc)
- **Repository:** [Github](https://github.com/thomasgauthier/observation_or_evaluation)
### Dataset Summary
This dataset contains statements classified into observations and evaluations categories, based on the principles of Nonviolent Communication (NVC) teached by Marshall Rosenberg. It includes a synthetic dataset generated and augmented through various language models to classify statements reflecting either pure observations (noticing) or evaluations (judgments), aimed at understanding and practicing effective empathetic communication. The dataset is constructed to evaluate generalist large language models abilities at distinguishing between observational and evaluative sentences as defined in NVC, serving as a benchmark for sentiment analysis and subjective interpretation accuracy.
### Supported Tasks and Leaderboards #
- `text-classification`: This task involves classifying sentences into one of the two categories : statements containing observations and statements containing evaluations. This discerning ability can help in understanding and improving empathetic communication skills. There is no active leaderboard for this task but this dataset can be used as one.
### Languages
The dataset is entirely in English.
## Dataset Structure
### Data Instances
A typical instance in this dataset might look like:
```json
{
"statement": "John was angry with me yesterday for no reason.",
"reasoning": "The statement suggests that John felt a certain emotion (anger) directed towards the speaker and adds the qualifier 'for no reason'. This is considered an evaluation.",
"classification": "Evaluation",
"pure_observation_alternative": "John told me he was angry."
}
```
### Data Fields
- `statement`: The original sentence provided in the dataset.
- `reasoning`: The rationale behind classifying the statement as an observation, evaluation, or mixed.
- `classification`: The classification of the statement - `Observation`, `Evaluation`, or `Mixed`.
- `pure_observation_alternative`?: An optional alternative version of the statement that represents a pure observation without evaluation.
### Data Splits #
The dataset is incorporated into a `test` split, intended not for training purposes, but rather as a benchmark to evaluate generalist models.
## Additional files
This repo also includes
- [`observation_or_evaluation.ipynb`](observation_or_evaluation.ipynb): The complete code for generating, filtering and refining the dataset
- [`generations.csv`](generations.csv): A CSV file with all the prompts and generations (with generation parameters) sent and received from Together.ai
- [`results.sqlite`](results.sqlite): The sqlite file where everything was saved (see notebook)
## Dataset Creation
### Curation Rationale #
The dataset was created to provide a metric to gauge language models abilities at Nonviolent Communication (NVC), specifically the differentiation between observations and evaluations, which is a core concept in NVC.
### Source Data
All samples in this dataset were generated by large language models. The bulk of the data was inspired by an exercise in Marshall Rosenberg's book *Nonviolent Communication: A Language of Life*. It was further augmented with TV script (*Seinfeld*) seed data to ensure varied and relatable statements.
### Annotations
Annotations were generated by language model outputs, with subsequent manual and automated review and adjustment to ensure quality and adherence to NVC principles.
## Considerations for Using the Data
### Social Impact of Dataset #
This dataset aims to contribute positively to the development of AI systems capable of understanding and practicing principles of empathetic and nonviolent communication, potentially reducing misunderstandings and conflicts in human interactions.
### Discussion of Biases #
Given the synthetic nature of part of the dataset, there most certainly are biases in the language models' training data that could affect the classifications.
### Other Known Limitations #
The synthetic generation of data points may not capture the full complexity and nuance of human emotional expression and interpretation. Furthermore, the classifications contained in the dataset have not been reviewed by NVC practitioners and could fail to properly reflect NVC principles.
## Additional Information
### Dataset Curators
The dataset generation pipeline was developped by Thomas Gauthier-Caron
### Licensing Information
The dataset is distributed under a Apache 2.0 license.
### Citation Information
```
@misc{observation_or_evaluation_dataset_2024,
author = {Gauthier-Caron, Thomas},
title = {Observation or evaluation dataset},
year = {2024},
howpublished = {\url{https://thomasgauthier.dev/devlog/nvc}}
}
```
### Contributions
Special thanks to Marshall B. Rosenberg for the foundational work on Nonviolent Communication.
Additional thanks to Mistral, Hugging Face, Together.AI and Nous Research for the AI models and inference services that enabled this work. |
ChanceFocus/m2sum | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 10278
num_examples: 1
- name: test
num_bytes: 4679014
num_examples: 200
download_size: 0
dataset_size: 4689292
---
# Dataset Card for "m2sum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/ringo_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ringo/鈴瑚/링고 (Touhou)
This is the dataset of ringo/鈴瑚/링고 (Touhou), containing 500 images and their tags.
The core tags of this character are `animal_ears, rabbit_ears, blonde_hair, short_hair, hat, floppy_ears, red_eyes, flat_cap, brown_headwear, breasts, cabbie_hat`, 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 | 437.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ringo_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 296.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ringo_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1096 | 601.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ringo_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 406.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ringo_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1096 | 782.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ringo_touhou/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/ringo_touhou',
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 | 23 |  |  |  |  |  | 1girl, dango, orange_shirt, solo, midriff, short_sleeves, shorts, skewer, eating, looking_at_viewer, navel, barefoot, smile |
| 1 | 8 |  |  |  |  |  | 1girl, dango, holding_food, orange_shirt, short_sleeves, solo, striped_shorts, yellow_shorts, closed_mouth, midriff, navel, simple_background, eating, white_background, bangs, vertical_stripes, :t, barefoot, blush_stickers, frills, full_body, medium_breasts, crop_top, one-hour_drawing_challenge, yellow_shirt |
| 2 | 10 |  |  |  |  |  | 1girl, orange_shirt, solo, upper_body, open_mouth, short_sleeves, looking_at_viewer, simple_background, bangs, smile, collarbone |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | dango | orange_shirt | solo | midriff | short_sleeves | shorts | skewer | eating | looking_at_viewer | navel | barefoot | smile | holding_food | striped_shorts | yellow_shorts | closed_mouth | simple_background | white_background | bangs | vertical_stripes | :t | blush_stickers | frills | full_body | medium_breasts | crop_top | one-hour_drawing_challenge | yellow_shirt | upper_body | open_mouth | collarbone |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:---------------|:-------|:----------|:----------------|:---------|:---------|:---------|:--------------------|:--------|:-----------|:--------|:---------------|:-----------------|:----------------|:---------------|:--------------------|:-------------------|:--------|:-------------------|:-----|:-----------------|:---------|:------------|:-----------------|:-----------|:-----------------------------|:---------------|:-------------|:-------------|:-------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 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 | | | |
| 2 | 10 |  |  |  |  |  | X | | X | X | | X | | | | X | | | X | | | | | X | | X | | | | | | | | | | X | X | X |
|
23nx7ng/cdial-bias | ---
license: cc-by-nc-nd-4.0
---
Data by Zhou Jingyan, et al. (2022) and copied from GitHub with permission. Original repo: https://github.com/para-zhou/CDial-Bias/. |
daydreamer-json/temporalHlsRawDataStorage | ---
viewer: false
---
|
coralexbadea/monitorul_trial_qa400 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 945166
num_examples: 3181
download_size: 441212
dataset_size: 945166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "monitorul_trial_qa400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
justinian336/salvadoran-news-ner | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 73983057.36422747
num_examples: 56025
download_size: 43634286
dataset_size: 73983057.36422747
---
# Dataset Card for "salvadoran-news-ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/nekone_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nekone/ネコネ/猫音 (Azur Lane)
This is the dataset of nekone/ネコネ/猫音 (Azur Lane), containing 46 images and their tags.
The core tags of this character are `animal_ears, red_eyes, brown_hair, tail, long_hair, black_hair, mole_under_eye, mole, ponytail, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 46 | 47.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nekone_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 46 | 31.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nekone_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 107 | 67.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nekone_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 46 | 43.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nekone_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 107 | 87.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nekone_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nekone_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, long_sleeves, simple_background, white_background, holding, dress, bangs, hair_ribbon, standing, twintails |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | blush | long_sleeves | simple_background | white_background | holding | dress | bangs | hair_ribbon | standing | twintails |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:---------------|:--------------------|:-------------------|:----------|:--------|:--------|:--------------|:-----------|:------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Vinnyyw/AnahiSA | ---
license: openrail
---
|
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