datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
income/hotpotqa-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. |
PJMixers/Math-1M | ---
language:
- en
tags:
- math
size_categories:
- 1M<n<10M
---
Created with [this script](https://gist.github.com/xzuyn/5807bbc2a305590f7b49b879dc0354ad), so I assume everything is 100% correct (with rounding). |
greathero/evenmorex11-newthreeclass-newercontrailsvalidationdataset | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 162552.0
num_examples: 9
download_size: 38146
dataset_size: 162552.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wadhwani-ai/pest-management-opendata | ---
license: apache-2.0
---
# Wadhwani AI Pest Management Open Data
This dataset is a Hugging Face adaptor to the official dataset [hosted
on
Github](https://github.com/wadhwani-ai/pest-management-opendata). Please
refer to that repository for detailed and up-to-date documentation.
## Usage
This dataset is large. It is strongly recommended users access it as a
stream:
```python
from datasets import load_dataset
dataset = load_dataset('wadhwani-ai/pest-management-opendata', streaming=True)
```
Bounding boxes are stored as geospatial types. Once loaded, they can be
read as follows:
```python
from shapely.wkb import loads
for (s, data) in dataset.items():
for d in data:
pests = d['pests']
iterable = map(pests.get, ('label', 'geometry'))
for (i, j) in zip(*iterable):
geom = loads(j)
print(i, geom.bounds)
```
The bounds of a geometry are what most object detection systems
require. See the [Shapely
documentation](https://shapely.readthedocs.io/en/stable/manual.html#object.bounds)
for more.
|
open-llm-leaderboard/details_yam-peleg__Experiment27-7B | ---
pretty_name: Evaluation run of yam-peleg/Experiment27-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yam-peleg__Experiment27-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T21:59:41.752903](https://huggingface.co/datasets/open-llm-leaderboard/details_yam-peleg__Experiment27-7B/blob/main/results_2024-03-01T21-59-41.752903.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\": 0.6508760252105659,\n\
\ \"acc_stderr\": 0.03215102952058525,\n \"acc_norm\": 0.6502532816256344,\n\
\ \"acc_norm_stderr\": 0.03282509388107025,\n \"mc1\": 0.6291309669522643,\n\
\ \"mc1_stderr\": 0.016909693580248835,\n \"mc2\": 0.7869929436345311,\n\
\ \"mc2_stderr\": 0.013499518741879673\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7192832764505119,\n \"acc_stderr\": 0.01313123812697558,\n\
\ \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.012889272949313368\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.715893248356901,\n\
\ \"acc_stderr\": 0.004500662294697923,\n \"acc_norm\": 0.891256721768572,\n\
\ \"acc_norm_stderr\": 0.0031068060075356316\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047423976,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047423976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\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\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.02341529343356853\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\": 0.7,\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\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603346,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603346\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\
\ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\
acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\
\ \"acc_stderr\": 0.013586619219903341,\n \"acc_norm\": 0.8250319284802043,\n\
\ \"acc_norm_stderr\": 0.013586619219903341\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4111731843575419,\n\
\ \"acc_stderr\": 0.016456498033977512,\n \"acc_norm\": 0.4111731843575419,\n\
\ \"acc_norm_stderr\": 0.016456498033977512\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.025917806117147158,\n\
\ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.025917806117147158\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712992,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712992\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4771838331160365,\n\
\ \"acc_stderr\": 0.012756933382823696,\n \"acc_norm\": 0.4771838331160365,\n\
\ \"acc_norm_stderr\": 0.012756933382823696\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\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\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6291309669522643,\n\
\ \"mc1_stderr\": 0.016909693580248835,\n \"mc2\": 0.7869929436345311,\n\
\ \"mc2_stderr\": 0.013499518741879673\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8492501973164956,\n \"acc_stderr\": 0.010056094631479674\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6808188021228203,\n \
\ \"acc_stderr\": 0.012840345676251648\n }\n}\n```"
repo_url: https://huggingface.co/yam-peleg/Experiment27-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|arc:challenge|25_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|gsm8k|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hellaswag|10_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T21-59-41.752903.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T21-59-41.752903.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- '**/details_harness|winogrande|5_2024-03-01T21-59-41.752903.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T21-59-41.752903.parquet'
- config_name: results
data_files:
- split: 2024_03_01T21_59_41.752903
path:
- results_2024-03-01T21-59-41.752903.parquet
- split: latest
path:
- results_2024-03-01T21-59-41.752903.parquet
---
# Dataset Card for Evaluation run of yam-peleg/Experiment27-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_yam-peleg__Experiment27-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T21:59:41.752903](https://huggingface.co/datasets/open-llm-leaderboard/details_yam-peleg__Experiment27-7B/blob/main/results_2024-03-01T21-59-41.752903.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": 0.6508760252105659,
"acc_stderr": 0.03215102952058525,
"acc_norm": 0.6502532816256344,
"acc_norm_stderr": 0.03282509388107025,
"mc1": 0.6291309669522643,
"mc1_stderr": 0.016909693580248835,
"mc2": 0.7869929436345311,
"mc2_stderr": 0.013499518741879673
},
"harness|arc:challenge|25": {
"acc": 0.7192832764505119,
"acc_stderr": 0.01313123812697558,
"acc_norm": 0.735494880546075,
"acc_norm_stderr": 0.012889272949313368
},
"harness|hellaswag|10": {
"acc": 0.715893248356901,
"acc_stderr": 0.004500662294697923,
"acc_norm": 0.891256721768572,
"acc_norm_stderr": 0.0031068060075356316
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.041539484047423976,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.041539484047423976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
ademax/dataset_line_connect | ---
dataset_info:
features:
- name: lineA
dtype: string
- name: lineB
dtype: string
- name: is_join
dtype: int64
splits:
- name: train
num_bytes: 1143553535
num_examples: 10001530
download_size: 412153174
dataset_size: 1143553535
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_line_connect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-76000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
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num_bytes: 13336000
num_examples: 1000
download_size: 1093539
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
code-philia/CoEdPilot-line-locator | ---
license: mit
---
|
reza-alipour/Yelp_Sentiment | ---
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: text
dtype: string
- name: IsPositive
dtype: int64
splits:
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num_bytes: 24204778
num_examples: 444101
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num_bytes: 3466415
num_examples: 63483
- name: test
num_bytes: 6861944
num_examples: 126670
download_size: 17440510
dataset_size: 34533137
---
# Dataset Card for "Yelp_Sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lhallee/prost_valid_test | ---
configs:
- config_name: default
data_files:
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
splits:
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num_bytes: 603744
num_examples: 1259
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num_bytes: 140994
num_examples: 474
download_size: 679584
dataset_size: 744738
---
# Dataset Card for "prost_valid_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Deltan2002/laptop_dataset | ---
license: odc-by
task_categories:
- text-classification
- feature-extraction
- question-answering
language:
- en
pretty_name: laptop_dataset
size_categories:
- n<1K
--- |
FanChen0116/bus_few4_16x_pvi | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-from_location
'2': B-from_location
'3': B-leaving_date
'4': I-leaving_date
'5': I-to_location
'6': B-to_location
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sequence: string
splits:
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num_bytes: 138287
num_examples: 560
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num_bytes: 6900
num_examples: 35
- name: test
num_bytes: 70618
num_examples: 377
download_size: 20540
dataset_size: 215805
---
# Dataset Card for "bus_few4_16x_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/VQAv2_test_2 | ---
dataset_info:
features:
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dtype: string
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sequence: string
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sequence: string
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sequence: string
splits:
- name: test
num_bytes: 14214582779.0
num_examples: 89559
download_size: 2707843217
dataset_size: 14214582779.0
---
# Dataset Card for "VQAv2_test_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Saturo1234567/gojo2 | ---
license: apache-2.0
---
|
mychen76/cord-ocr-text-v2 | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: ocr_kie
dtype: string
- name: ocr_text
dtype: string
- name: ocr_box
dtype: string
splits:
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num_bytes: 1796452
num_examples: 800
download_size: 887206
dataset_size: 1796452
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cord-ocr-text-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_01-ai__Yi-34B-200K | ---
pretty_name: Evaluation run of 01-ai/Yi-34B-200K
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/Yi-34B-200K) 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_01-ai__Yi-34B-200K\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-05T03:41:41.478096](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-200K/blob/main/results_2023-12-05T03-41-41.478096.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\": 0.7553618929104267,\n\
\ \"acc_stderr\": 0.02837585903729335,\n \"acc_norm\": 0.7603811984841083,\n\
\ \"acc_norm_stderr\": 0.028905075105130153,\n \"mc1\": 0.3818849449204406,\n\
\ \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5364445120598228,\n\
\ \"mc2_stderr\": 0.014804162952722544\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759091,\n\
\ \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063227\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6557458673571002,\n\
\ \"acc_stderr\": 0.004741534106470288,\n \"acc_norm\": 0.8558056164110734,\n\
\ \"acc_norm_stderr\": 0.0035056879433872927\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7185185185185186,\n\
\ \"acc_stderr\": 0.038850042458002526,\n \"acc_norm\": 0.7185185185185186,\n\
\ \"acc_norm_stderr\": 0.038850042458002526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8618421052631579,\n \"acc_stderr\": 0.028081042939576552,\n\
\ \"acc_norm\": 0.8618421052631579,\n \"acc_norm_stderr\": 0.028081042939576552\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.8226415094339623,\n \"acc_stderr\": 0.02350873921884694,\n\
\ \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.02350873921884694\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n\
\ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \
\ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7341040462427746,\n\
\ \"acc_stderr\": 0.033687629322594316,\n \"acc_norm\": 0.7341040462427746,\n\
\ \"acc_norm_stderr\": 0.033687629322594316\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\
\ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n\
\ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.02767845257821239,\n\
\ \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.02767845257821239\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\
\ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\
\ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7724137931034483,\n \"acc_stderr\": 0.03493950380131184,\n\
\ \"acc_norm\": 0.7724137931034483,\n \"acc_norm_stderr\": 0.03493950380131184\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.6322751322751323,\n \"acc_stderr\": 0.02483383982556242,\n \"\
acc_norm\": 0.6322751322751323,\n \"acc_norm_stderr\": 0.02483383982556242\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n\
\ \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n\
\ \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8935483870967742,\n\
\ \"acc_stderr\": 0.01754510295165663,\n \"acc_norm\": 0.8935483870967742,\n\
\ \"acc_norm_stderr\": 0.01754510295165663\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6748768472906403,\n \"acc_stderr\": 0.032957975663112704,\n\
\ \"acc_norm\": 0.6748768472906403,\n \"acc_norm_stderr\": 0.032957975663112704\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\"\
: 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781675,\n\
\ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781675\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9191919191919192,\n \"acc_stderr\": 0.019417681889724536,\n \"\
acc_norm\": 0.9191919191919192,\n \"acc_norm_stderr\": 0.019417681889724536\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.010281417011909039,\n\
\ \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.010281417011909039\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8051282051282052,\n \"acc_stderr\": 0.020083167595181393,\n\
\ \"acc_norm\": 0.8051282051282052,\n \"acc_norm_stderr\": 0.020083167595181393\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.4074074074074074,\n \"acc_stderr\": 0.029958249250082114,\n \
\ \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.029958249250082114\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.819327731092437,\n \"acc_stderr\": 0.02499196496660077,\n \
\ \"acc_norm\": 0.819327731092437,\n \"acc_norm_stderr\": 0.02499196496660077\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248436,\n \"\
acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248436\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769574,\n \"\
acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769574\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293648,\n \"\
acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293648\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9166666666666666,\n \"acc_stderr\": 0.019398452135813905,\n \"\
acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.019398452135813905\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \
\ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8071748878923767,\n\
\ \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.8071748878923767,\n\
\ \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804468,\n\
\ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804468\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.9090909090909091,\n \"acc_stderr\": 0.02624319405407388,\n \"\
acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.02624319405407388\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\
\ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\
\ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.02521232721050711,\n\
\ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.02521232721050711\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5535714285714286,\n\
\ \"acc_stderr\": 0.04718471485219587,\n \"acc_norm\": 0.5535714285714286,\n\
\ \"acc_norm_stderr\": 0.04718471485219587\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\
\ \"acc_stderr\": 0.016534627684311357,\n \"acc_norm\": 0.9316239316239316,\n\
\ \"acc_norm_stderr\": 0.016534627684311357\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9042145593869731,\n\
\ \"acc_stderr\": 0.01052403107905584,\n \"acc_norm\": 0.9042145593869731,\n\
\ \"acc_norm_stderr\": 0.01052403107905584\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575277,\n\
\ \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575277\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6558659217877095,\n\
\ \"acc_stderr\": 0.015889221313307094,\n \"acc_norm\": 0.6558659217877095,\n\
\ \"acc_norm_stderr\": 0.015889221313307094\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.869281045751634,\n \"acc_stderr\": 0.01930187362421527,\n\
\ \"acc_norm\": 0.869281045751634,\n \"acc_norm_stderr\": 0.01930187362421527\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.842443729903537,\n\
\ \"acc_stderr\": 0.020692237273583984,\n \"acc_norm\": 0.842443729903537,\n\
\ \"acc_norm_stderr\": 0.020692237273583984\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571853,\n\
\ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571853\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6099290780141844,\n \"acc_stderr\": 0.02909767559946393,\n \
\ \"acc_norm\": 0.6099290780141844,\n \"acc_norm_stderr\": 0.02909767559946393\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5990873533246415,\n\
\ \"acc_stderr\": 0.012516960350640814,\n \"acc_norm\": 0.5990873533246415,\n\
\ \"acc_norm_stderr\": 0.012516960350640814\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8198529411764706,\n \"acc_stderr\": 0.02334516361654484,\n\
\ \"acc_norm\": 0.8198529411764706,\n \"acc_norm_stderr\": 0.02334516361654484\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8186274509803921,\n \"acc_stderr\": 0.015588643495370463,\n \
\ \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.015588643495370463\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8163265306122449,\n \"acc_stderr\": 0.024789071332007646,\n\
\ \"acc_norm\": 0.8163265306122449,\n \"acc_norm_stderr\": 0.024789071332007646\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n\
\ \"acc_stderr\": 0.0201906705350279,\n \"acc_norm\": 0.9104477611940298,\n\
\ \"acc_norm_stderr\": 0.0201906705350279\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3818849449204406,\n\
\ \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5364445120598228,\n\
\ \"mc2_stderr\": 0.014804162952722544\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8255722178374112,\n \"acc_stderr\": 0.010665187902498438\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6163760424564063,\n \
\ \"acc_stderr\": 0.013394238584938161\n }\n}\n```"
repo_url: https://huggingface.co/01-ai/Yi-34B-200K
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_05T03_41_41.478096
path:
- '**/details_harness|arc:challenge|25_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|gsm8k|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hellaswag|10_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-41-41.478096.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T03-41-41.478096.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- '**/details_harness|winogrande|5_2023-12-05T03-41-41.478096.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-05T03-41-41.478096.parquet'
- config_name: results
data_files:
- split: 2023_12_05T03_41_41.478096
path:
- results_2023-12-05T03-41-41.478096.parquet
- split: latest
path:
- results_2023-12-05T03-41-41.478096.parquet
---
# Dataset Card for Evaluation run of 01-ai/Yi-34B-200K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/01-ai/Yi-34B-200K
- **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 [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/Yi-34B-200K) 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_01-ai__Yi-34B-200K",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T03:41:41.478096](https://huggingface.co/datasets/open-llm-leaderboard/details_01-ai__Yi-34B-200K/blob/main/results_2023-12-05T03-41-41.478096.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": 0.7553618929104267,
"acc_stderr": 0.02837585903729335,
"acc_norm": 0.7603811984841083,
"acc_norm_stderr": 0.028905075105130153,
"mc1": 0.3818849449204406,
"mc1_stderr": 0.017008101939163495,
"mc2": 0.5364445120598228,
"mc2_stderr": 0.014804162952722544
},
"harness|arc:challenge|25": {
"acc": 0.6262798634812287,
"acc_stderr": 0.014137708601759091,
"acc_norm": 0.6535836177474402,
"acc_norm_stderr": 0.013905011180063227
},
"harness|hellaswag|10": {
"acc": 0.6557458673571002,
"acc_stderr": 0.004741534106470288,
"acc_norm": 0.8558056164110734,
"acc_norm_stderr": 0.0035056879433872927
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7185185185185186,
"acc_stderr": 0.038850042458002526,
"acc_norm": 0.7185185185185186,
"acc_norm_stderr": 0.038850042458002526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8618421052631579,
"acc_stderr": 0.028081042939576552,
"acc_norm": 0.8618421052631579,
"acc_norm_stderr": 0.028081042939576552
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036843,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036843
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8226415094339623,
"acc_stderr": 0.02350873921884694,
"acc_norm": 0.8226415094339623,
"acc_norm_stderr": 0.02350873921884694
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7341040462427746,
"acc_stderr": 0.033687629322594316,
"acc_norm": 0.7341040462427746,
"acc_norm_stderr": 0.033687629322594316
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.49019607843137253,
"acc_stderr": 0.04974229460422817,
"acc_norm": 0.49019607843137253,
"acc_norm_stderr": 0.04974229460422817
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7659574468085106,
"acc_stderr": 0.02767845257821239,
"acc_norm": 0.7659574468085106,
"acc_norm_stderr": 0.02767845257821239
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5964912280701754,
"acc_stderr": 0.04615186962583707,
"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7724137931034483,
"acc_stderr": 0.03493950380131184,
"acc_norm": 0.7724137931034483,
"acc_norm_stderr": 0.03493950380131184
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6322751322751323,
"acc_stderr": 0.02483383982556242,
"acc_norm": 0.6322751322751323,
"acc_norm_stderr": 0.02483383982556242
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5793650793650794,
"acc_stderr": 0.04415438226743745,
"acc_norm": 0.5793650793650794,
"acc_norm_stderr": 0.04415438226743745
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8935483870967742,
"acc_stderr": 0.01754510295165663,
"acc_norm": 0.8935483870967742,
"acc_norm_stderr": 0.01754510295165663
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6748768472906403,
"acc_stderr": 0.032957975663112704,
"acc_norm": 0.6748768472906403,
"acc_norm_stderr": 0.032957975663112704
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8484848484848485,
"acc_stderr": 0.027998073798781675,
"acc_norm": 0.8484848484848485,
"acc_norm_stderr": 0.027998073798781675
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9191919191919192,
"acc_stderr": 0.019417681889724536,
"acc_norm": 0.9191919191919192,
"acc_norm_stderr": 0.019417681889724536
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9792746113989638,
"acc_stderr": 0.010281417011909039,
"acc_norm": 0.9792746113989638,
"acc_norm_stderr": 0.010281417011909039
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8051282051282052,
"acc_stderr": 0.020083167595181393,
"acc_norm": 0.8051282051282052,
"acc_norm_stderr": 0.020083167595181393
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.4074074074074074,
"acc_stderr": 0.029958249250082114,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.029958249250082114
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.819327731092437,
"acc_stderr": 0.02499196496660077,
"acc_norm": 0.819327731092437,
"acc_norm_stderr": 0.02499196496660077
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5099337748344371,
"acc_stderr": 0.04081677107248436,
"acc_norm": 0.5099337748344371,
"acc_norm_stderr": 0.04081677107248436
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9211009174311927,
"acc_stderr": 0.011558198113769574,
"acc_norm": 0.9211009174311927,
"acc_norm_stderr": 0.011558198113769574
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6481481481481481,
"acc_stderr": 0.03256850570293648,
"acc_norm": 0.6481481481481481,
"acc_norm_stderr": 0.03256850570293648
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9166666666666666,
"acc_stderr": 0.019398452135813905,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.019398452135813905
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9113924050632911,
"acc_stderr": 0.018498315206865384,
"acc_norm": 0.9113924050632911,
"acc_norm_stderr": 0.018498315206865384
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8071748878923767,
"acc_stderr": 0.026478240960489365,
"acc_norm": 0.8071748878923767,
"acc_norm_stderr": 0.026478240960489365
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8625954198473282,
"acc_stderr": 0.030194823996804468,
"acc_norm": 0.8625954198473282,
"acc_norm_stderr": 0.030194823996804468
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.9090909090909091,
"acc_stderr": 0.02624319405407388,
"acc_norm": 0.9090909090909091,
"acc_norm_stderr": 0.02624319405407388
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8981481481481481,
"acc_stderr": 0.029239272675632748,
"acc_norm": 0.8981481481481481,
"acc_norm_stderr": 0.029239272675632748
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8834355828220859,
"acc_stderr": 0.02521232721050711,
"acc_norm": 0.8834355828220859,
"acc_norm_stderr": 0.02521232721050711
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5535714285714286,
"acc_stderr": 0.04718471485219587,
"acc_norm": 0.5535714285714286,
"acc_norm_stderr": 0.04718471485219587
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
"acc_stderr": 0.03393295729761011,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.03393295729761011
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9316239316239316,
"acc_stderr": 0.016534627684311357,
"acc_norm": 0.9316239316239316,
"acc_norm_stderr": 0.016534627684311357
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.9042145593869731,
"acc_stderr": 0.01052403107905584,
"acc_norm": 0.9042145593869731,
"acc_norm_stderr": 0.01052403107905584
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8092485549132948,
"acc_stderr": 0.021152676966575277,
"acc_norm": 0.8092485549132948,
"acc_norm_stderr": 0.021152676966575277
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6558659217877095,
"acc_stderr": 0.015889221313307094,
"acc_norm": 0.6558659217877095,
"acc_norm_stderr": 0.015889221313307094
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.869281045751634,
"acc_stderr": 0.01930187362421527,
"acc_norm": 0.869281045751634,
"acc_norm_stderr": 0.01930187362421527
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.842443729903537,
"acc_stderr": 0.020692237273583984,
"acc_norm": 0.842443729903537,
"acc_norm_stderr": 0.020692237273583984
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8672839506172839,
"acc_stderr": 0.018877353839571853,
"acc_norm": 0.8672839506172839,
"acc_norm_stderr": 0.018877353839571853
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6099290780141844,
"acc_stderr": 0.02909767559946393,
"acc_norm": 0.6099290780141844,
"acc_norm_stderr": 0.02909767559946393
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5990873533246415,
"acc_stderr": 0.012516960350640814,
"acc_norm": 0.5990873533246415,
"acc_norm_stderr": 0.012516960350640814
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8198529411764706,
"acc_stderr": 0.02334516361654484,
"acc_norm": 0.8198529411764706,
"acc_norm_stderr": 0.02334516361654484
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.8186274509803921,
"acc_stderr": 0.015588643495370463,
"acc_norm": 0.8186274509803921,
"acc_norm_stderr": 0.015588643495370463
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8163265306122449,
"acc_stderr": 0.024789071332007646,
"acc_norm": 0.8163265306122449,
"acc_norm_stderr": 0.024789071332007646
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.9104477611940298,
"acc_stderr": 0.0201906705350279,
"acc_norm": 0.9104477611940298,
"acc_norm_stderr": 0.0201906705350279
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.0272659924344291,
"acc_norm": 0.92,
"acc_norm_stderr": 0.0272659924344291
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685516,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685516
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8830409356725146,
"acc_stderr": 0.024648068961366152,
"acc_norm": 0.8830409356725146,
"acc_norm_stderr": 0.024648068961366152
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3818849449204406,
"mc1_stderr": 0.017008101939163495,
"mc2": 0.5364445120598228,
"mc2_stderr": 0.014804162952722544
},
"harness|winogrande|5": {
"acc": 0.8255722178374112,
"acc_stderr": 0.010665187902498438
},
"harness|gsm8k|5": {
"acc": 0.6163760424564063,
"acc_stderr": 0.013394238584938161
}
}
```
### 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] |
distilled-from-one-sec-cv12/chunk_97 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1243011456
num_examples: 242208
download_size: 1269304753
dataset_size: 1243011456
---
# Dataset Card for "chunk_97"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jerome-white/arena-bt-stan | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: parameter
dtype: string
- name: sample
dtype: int64
- name: value
dtype: float64
- name: chain
dtype: int64
- name: element
dtype: string
splits:
- name: train
num_bytes: 133376000
num_examples: 2592000
download_size: 39697856
dataset_size: 133376000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AISE-TUDelft/ML4SE23_G4_Small_Clone_Bench | ---
task_categories:
- text-classification
language:
- en
tags:
- code clone detection
pretty_name: Small Clone Bench
size_categories:
- n<1K
--- |
yuvidhepe/us-accidents-updated | ---
license:
- cc-by-nc-sa-4.0
kaggle_id: sobhanmoosavi/us-accidents
dataset_info:
features:
- name: ID
dtype: string
- name: Source
dtype: string
- name: Severity
dtype: int64
- name: Start_Time
dtype: string
- name: End_Time
dtype: string
- name: Start_Lat
dtype: float64
- name: Start_Lng
dtype: float64
- name: End_Lat
dtype: float64
- name: End_Lng
dtype: float64
- name: Distance(mi)
dtype: float64
- name: Description
dtype: string
- name: Street
dtype: string
- name: City
dtype: string
- name: County
dtype: string
- name: State
dtype: string
- name: Zipcode
dtype: string
- name: Country
dtype: string
- name: Timezone
dtype: string
- name: Airport_Code
dtype: string
- name: Weather_Timestamp
dtype: string
- name: Temperature(F)
dtype: float64
- name: Wind_Chill(F)
dtype: float64
- name: Humidity(%)
dtype: float64
- name: Pressure(in)
dtype: float64
- name: Visibility(mi)
dtype: float64
- name: Wind_Direction
dtype: string
- name: Wind_Speed(mph)
dtype: float64
- name: Precipitation(in)
dtype: float64
- name: Weather_Condition
dtype: string
- name: Amenity
dtype: bool
- name: Bump
dtype: bool
- name: Crossing
dtype: bool
- name: Give_Way
dtype: bool
- name: Junction
dtype: bool
- name: No_Exit
dtype: bool
- name: Railway
dtype: bool
- name: Roundabout
dtype: bool
- name: Station
dtype: bool
- name: Stop
dtype: bool
- name: Traffic_Calming
dtype: bool
- name: Traffic_Signal
dtype: bool
- name: Turning_Loop
dtype: bool
- name: Sunrise_Sunset
dtype: string
- name: Civil_Twilight
dtype: string
- name: Nautical_Twilight
dtype: string
- name: Astronomical_Twilight
dtype: string
splits:
- name: train
num_bytes: 3147354997
num_examples: 7728394
download_size: 1088140045
dataset_size: 3147354997
---
# Dataset Card for US Accidents (2016 - 2023)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/sobhanmoosavi/us-accidents
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Description
This is a countrywide car accident dataset, which covers __49 states of the USA__. The accident data are collected from __February 2016 to Mar 2023__, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road-networks. Currently, there are about __7.7 million__ accident records in this dataset. Check [here](https://smoosavi.org/datasets/us_accidents) to learn more about this dataset.
### Acknowledgements
Please cite the following papers if you use this dataset:
- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “[A Countrywide Traffic Accident Dataset](https://arxiv.org/abs/1906.05409).”, 2019.
- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. ["Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights."](https://arxiv.org/abs/1909.09638) In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.
### Content
This dataset has been collected in real-time, using multiple Traffic APIs. Currently, it contains accident data that are collected from February 2016 to Dec 2021 for the Contiguous United States. Check [here](https://smoosavi.org/datasets/us_accidents) to learn more about this dataset.
### Inspiration
US-Accidents can be used for numerous applications such as real-time car accident prediction, studying car accidents hotspot locations, casualty analysis and extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful to study the impact of COVID-19 on traffic behavior and accidents.
### Usage Policy and Legal Disclaimer
This dataset is being distributed only for __Research__ purposes, under Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By clicking on download button(s) below, you are agreeing to use this data only for non-commercial, research, or academic applications. You may need to cite the above papers if you use this dataset.
### Inquiries or need help?
For any inquiries, contact me at moosavi.3@osu.edu
### 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
This dataset was shared by [@sobhanmoosavi](https://kaggle.com/sobhanmoosavi)
### Licensing Information
The license for this dataset is cc-by-nc-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
reversenimra/nimra | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_appvoid__palmer-002.5 | ---
pretty_name: Evaluation run of appvoid/palmer-002.5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [appvoid/palmer-002.5](https://huggingface.co/appvoid/palmer-002.5) 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_appvoid__palmer-002.5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-25T15:49:21.576501](https://huggingface.co/datasets/open-llm-leaderboard/details_appvoid__palmer-002.5/blob/main/results_2024-01-25T15-49-21.576501.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\": 0.26026405988403956,\n\
\ \"acc_stderr\": 0.030773563546552134,\n \"acc_norm\": 0.26041347392018827,\n\
\ \"acc_norm_stderr\": 0.03152706572860613,\n \"mc1\": 0.2631578947368421,\n\
\ \"mc1_stderr\": 0.01541524174023701,\n \"mc2\": 0.4022295069003637,\n\
\ \"mc2_stderr\": 0.01452265399729067\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3575085324232082,\n \"acc_stderr\": 0.014005494275916571,\n\
\ \"acc_norm\": 0.37542662116040953,\n \"acc_norm_stderr\": 0.014150631435111728\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4645488946425015,\n\
\ \"acc_stderr\": 0.004977223485342026,\n \"acc_norm\": 0.6184027086237801,\n\
\ \"acc_norm_stderr\": 0.00484785754695748\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536975,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536975\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n\
\ \"acc_stderr\": 0.037125378336148665,\n \"acc_norm\": 0.24444444444444444,\n\
\ \"acc_norm_stderr\": 0.037125378336148665\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210324984,\n\
\ \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210324984\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\
\ \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2679245283018868,\n \"acc_stderr\": 0.027257260322494845,\n\
\ \"acc_norm\": 0.2679245283018868,\n \"acc_norm_stderr\": 0.027257260322494845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.18497109826589594,\n\
\ \"acc_stderr\": 0.029605623981771214,\n \"acc_norm\": 0.18497109826589594,\n\
\ \"acc_norm_stderr\": 0.029605623981771214\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n\
\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n\
\ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2297872340425532,\n \"acc_stderr\": 0.027501752944412424,\n\
\ \"acc_norm\": 0.2297872340425532,\n \"acc_norm_stderr\": 0.027501752944412424\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.17543859649122806,\n\
\ \"acc_stderr\": 0.0357795481394837,\n \"acc_norm\": 0.17543859649122806,\n\
\ \"acc_norm_stderr\": 0.0357795481394837\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.23448275862068965,\n \"acc_stderr\": 0.035306258743465914,\n\
\ \"acc_norm\": 0.23448275862068965,\n \"acc_norm_stderr\": 0.035306258743465914\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\
acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\
\ \"acc_stderr\": 0.032684540130117436,\n \"acc_norm\": 0.15873015873015872,\n\
\ \"acc_norm_stderr\": 0.032684540130117436\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.25161290322580643,\n \"acc_stderr\": 0.02468597928623997,\n \"\
acc_norm\": 0.25161290322580643,\n \"acc_norm_stderr\": 0.02468597928623997\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.23645320197044334,\n \"acc_stderr\": 0.029896114291733552,\n \"\
acc_norm\": 0.23645320197044334,\n \"acc_norm_stderr\": 0.029896114291733552\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2727272727272727,\n \"acc_stderr\": 0.03477691162163659,\n\
\ \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03477691162163659\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.21212121212121213,\n \"acc_stderr\": 0.02912652283458682,\n \"\
acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.02912652283458682\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.24870466321243523,\n \"acc_stderr\": 0.031195840877700307,\n\
\ \"acc_norm\": 0.24870466321243523,\n \"acc_norm_stderr\": 0.031195840877700307\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2846153846153846,\n \"acc_stderr\": 0.022878322799706287,\n\
\ \"acc_norm\": 0.2846153846153846,\n \"acc_norm_stderr\": 0.022878322799706287\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176892,\n\
\ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176892\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2052980132450331,\n \"acc_stderr\": 0.03297986648473836,\n \"\
acc_norm\": 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473836\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.24403669724770644,\n \"acc_stderr\": 0.01841528635141641,\n \"\
acc_norm\": 0.24403669724770644,\n \"acc_norm_stderr\": 0.01841528635141641\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3611111111111111,\n \"acc_stderr\": 0.032757734861009996,\n \"\
acc_norm\": 0.3611111111111111,\n \"acc_norm_stderr\": 0.032757734861009996\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.27450980392156865,\n \"acc_stderr\": 0.031321798030832904,\n \"\
acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.031321798030832904\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035303,\n \
\ \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035303\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.36771300448430494,\n\
\ \"acc_stderr\": 0.03236198350928276,\n \"acc_norm\": 0.36771300448430494,\n\
\ \"acc_norm_stderr\": 0.03236198350928276\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2366412213740458,\n \"acc_stderr\": 0.0372767357559692,\n\
\ \"acc_norm\": 0.2366412213740458,\n \"acc_norm_stderr\": 0.0372767357559692\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n\
\ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.24074074074074073,\n\
\ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.034624199316156234,\n\
\ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.034624199316156234\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.23300970873786409,\n \"acc_stderr\": 0.041858325989283164,\n\
\ \"acc_norm\": 0.23300970873786409,\n \"acc_norm_stderr\": 0.041858325989283164\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.27350427350427353,\n\
\ \"acc_stderr\": 0.029202540153431166,\n \"acc_norm\": 0.27350427350427353,\n\
\ \"acc_norm_stderr\": 0.029202540153431166\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2656449553001277,\n\
\ \"acc_stderr\": 0.015794302487888715,\n \"acc_norm\": 0.2656449553001277,\n\
\ \"acc_norm_stderr\": 0.015794302487888715\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.023618678310069356,\n\
\ \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.023618678310069356\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27150837988826815,\n\
\ \"acc_stderr\": 0.01487425216809527,\n \"acc_norm\": 0.27150837988826815,\n\
\ \"acc_norm_stderr\": 0.01487425216809527\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2765273311897106,\n\
\ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.2765273311897106,\n\
\ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2623456790123457,\n \"acc_stderr\": 0.02447722285613511,\n\
\ \"acc_norm\": 0.2623456790123457,\n \"acc_norm_stderr\": 0.02447722285613511\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.25177304964539005,\n \"acc_stderr\": 0.0258921511567094,\n \
\ \"acc_norm\": 0.25177304964539005,\n \"acc_norm_stderr\": 0.0258921511567094\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24445893089960888,\n\
\ \"acc_stderr\": 0.010976425013113909,\n \"acc_norm\": 0.24445893089960888,\n\
\ \"acc_norm_stderr\": 0.010976425013113909\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.2426470588235294,\n \"acc_stderr\": 0.02604066247420126,\n\
\ \"acc_norm\": 0.2426470588235294,\n \"acc_norm_stderr\": 0.02604066247420126\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2679738562091503,\n \"acc_stderr\": 0.017917974069594722,\n \
\ \"acc_norm\": 0.2679738562091503,\n \"acc_norm_stderr\": 0.017917974069594722\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3090909090909091,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.3090909090909091,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.14285714285714285,\n \"acc_stderr\": 0.02240178743525639,\n\
\ \"acc_norm\": 0.14285714285714285,\n \"acc_norm_stderr\": 0.02240178743525639\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\
\ \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.2537313432835821,\n\
\ \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3313253012048193,\n\
\ \"acc_stderr\": 0.03664314777288087,\n \"acc_norm\": 0.3313253012048193,\n\
\ \"acc_norm_stderr\": 0.03664314777288087\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.21637426900584794,\n \"acc_stderr\": 0.03158149539338734,\n\
\ \"acc_norm\": 0.21637426900584794,\n \"acc_norm_stderr\": 0.03158149539338734\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2631578947368421,\n\
\ \"mc1_stderr\": 0.01541524174023701,\n \"mc2\": 0.4022295069003637,\n\
\ \"mc2_stderr\": 0.01452265399729067\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6637726913970008,\n \"acc_stderr\": 0.013277286593993447\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.019711902956785442,\n \
\ \"acc_stderr\": 0.0038289829787356987\n }\n}\n```"
repo_url: https://huggingface.co/appvoid/palmer-002.5
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|arc:challenge|25_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|gsm8k|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hellaswag|10_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T15-49-21.576501.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T15-49-21.576501.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- '**/details_harness|winogrande|5_2024-01-25T15-49-21.576501.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-25T15-49-21.576501.parquet'
- config_name: results
data_files:
- split: 2024_01_25T15_49_21.576501
path:
- results_2024-01-25T15-49-21.576501.parquet
- split: latest
path:
- results_2024-01-25T15-49-21.576501.parquet
---
# Dataset Card for Evaluation run of appvoid/palmer-002.5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [appvoid/palmer-002.5](https://huggingface.co/appvoid/palmer-002.5) 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_appvoid__palmer-002.5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-25T15:49:21.576501](https://huggingface.co/datasets/open-llm-leaderboard/details_appvoid__palmer-002.5/blob/main/results_2024-01-25T15-49-21.576501.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": 0.26026405988403956,
"acc_stderr": 0.030773563546552134,
"acc_norm": 0.26041347392018827,
"acc_norm_stderr": 0.03152706572860613,
"mc1": 0.2631578947368421,
"mc1_stderr": 0.01541524174023701,
"mc2": 0.4022295069003637,
"mc2_stderr": 0.01452265399729067
},
"harness|arc:challenge|25": {
"acc": 0.3575085324232082,
"acc_stderr": 0.014005494275916571,
"acc_norm": 0.37542662116040953,
"acc_norm_stderr": 0.014150631435111728
},
"harness|hellaswag|10": {
"acc": 0.4645488946425015,
"acc_stderr": 0.004977223485342026,
"acc_norm": 0.6184027086237801,
"acc_norm_stderr": 0.00484785754695748
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536975,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536975
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.037125378336148665,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.037125378336148665
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.19078947368421054,
"acc_stderr": 0.031975658210324984,
"acc_norm": 0.19078947368421054,
"acc_norm_stderr": 0.031975658210324984
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.23,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2679245283018868,
"acc_stderr": 0.027257260322494845,
"acc_norm": 0.2679245283018868,
"acc_norm_stderr": 0.027257260322494845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.25,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.18497109826589594,
"acc_stderr": 0.029605623981771214,
"acc_norm": 0.18497109826589594,
"acc_norm_stderr": 0.029605623981771214
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.042801058373643966,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.042801058373643966
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2297872340425532,
"acc_stderr": 0.027501752944412424,
"acc_norm": 0.2297872340425532,
"acc_norm_stderr": 0.027501752944412424
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.17543859649122806,
"acc_stderr": 0.0357795481394837,
"acc_norm": 0.17543859649122806,
"acc_norm_stderr": 0.0357795481394837
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.23448275862068965,
"acc_stderr": 0.035306258743465914,
"acc_norm": 0.23448275862068965,
"acc_norm_stderr": 0.035306258743465914
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15873015873015872,
"acc_stderr": 0.032684540130117436,
"acc_norm": 0.15873015873015872,
"acc_norm_stderr": 0.032684540130117436
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.25161290322580643,
"acc_stderr": 0.02468597928623997,
"acc_norm": 0.25161290322580643,
"acc_norm_stderr": 0.02468597928623997
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.23645320197044334,
"acc_stderr": 0.029896114291733552,
"acc_norm": 0.23645320197044334,
"acc_norm_stderr": 0.029896114291733552
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.2727272727272727,
"acc_stderr": 0.03477691162163659,
"acc_norm": 0.2727272727272727,
"acc_norm_stderr": 0.03477691162163659
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.21212121212121213,
"acc_stderr": 0.02912652283458682,
"acc_norm": 0.21212121212121213,
"acc_norm_stderr": 0.02912652283458682
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.24870466321243523,
"acc_stderr": 0.031195840877700307,
"acc_norm": 0.24870466321243523,
"acc_norm_stderr": 0.031195840877700307
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2846153846153846,
"acc_stderr": 0.022878322799706287,
"acc_norm": 0.2846153846153846,
"acc_norm_stderr": 0.022878322799706287
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26296296296296295,
"acc_stderr": 0.02684205787383371,
"acc_norm": 0.26296296296296295,
"acc_norm_stderr": 0.02684205787383371
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.24789915966386555,
"acc_stderr": 0.028047967224176892,
"acc_norm": 0.24789915966386555,
"acc_norm_stderr": 0.028047967224176892
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2052980132450331,
"acc_stderr": 0.03297986648473836,
"acc_norm": 0.2052980132450331,
"acc_norm_stderr": 0.03297986648473836
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.24403669724770644,
"acc_stderr": 0.01841528635141641,
"acc_norm": 0.24403669724770644,
"acc_norm_stderr": 0.01841528635141641
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3611111111111111,
"acc_stderr": 0.032757734861009996,
"acc_norm": 0.3611111111111111,
"acc_norm_stderr": 0.032757734861009996
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27450980392156865,
"acc_stderr": 0.031321798030832904,
"acc_norm": 0.27450980392156865,
"acc_norm_stderr": 0.031321798030832904
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.25316455696202533,
"acc_stderr": 0.028304657943035303,
"acc_norm": 0.25316455696202533,
"acc_norm_stderr": 0.028304657943035303
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.36771300448430494,
"acc_stderr": 0.03236198350928276,
"acc_norm": 0.36771300448430494,
"acc_norm_stderr": 0.03236198350928276
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2366412213740458,
"acc_stderr": 0.0372767357559692,
"acc_norm": 0.2366412213740458,
"acc_norm_stderr": 0.0372767357559692
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.24793388429752067,
"acc_stderr": 0.039418975265163025,
"acc_norm": 0.24793388429752067,
"acc_norm_stderr": 0.039418975265163025
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.04133119440243838,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.04133119440243838
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.26380368098159507,
"acc_stderr": 0.034624199316156234,
"acc_norm": 0.26380368098159507,
"acc_norm_stderr": 0.034624199316156234
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04287858751340456,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04287858751340456
},
"harness|hendrycksTest-management|5": {
"acc": 0.23300970873786409,
"acc_stderr": 0.041858325989283164,
"acc_norm": 0.23300970873786409,
"acc_norm_stderr": 0.041858325989283164
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.27350427350427353,
"acc_stderr": 0.029202540153431166,
"acc_norm": 0.27350427350427353,
"acc_norm_stderr": 0.029202540153431166
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2656449553001277,
"acc_stderr": 0.015794302487888715,
"acc_norm": 0.2656449553001277,
"acc_norm_stderr": 0.015794302487888715
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.26011560693641617,
"acc_stderr": 0.023618678310069356,
"acc_norm": 0.26011560693641617,
"acc_norm_stderr": 0.023618678310069356
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.27150837988826815,
"acc_stderr": 0.01487425216809527,
"acc_norm": 0.27150837988826815,
"acc_norm_stderr": 0.01487425216809527
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.25163398692810457,
"acc_stderr": 0.024848018263875195,
"acc_norm": 0.25163398692810457,
"acc_norm_stderr": 0.024848018263875195
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2765273311897106,
"acc_stderr": 0.02540383297817961,
"acc_norm": 0.2765273311897106,
"acc_norm_stderr": 0.02540383297817961
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2623456790123457,
"acc_stderr": 0.02447722285613511,
"acc_norm": 0.2623456790123457,
"acc_norm_stderr": 0.02447722285613511
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.25177304964539005,
"acc_stderr": 0.0258921511567094,
"acc_norm": 0.25177304964539005,
"acc_norm_stderr": 0.0258921511567094
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.24445893089960888,
"acc_stderr": 0.010976425013113909,
"acc_norm": 0.24445893089960888,
"acc_norm_stderr": 0.010976425013113909
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.2426470588235294,
"acc_stderr": 0.02604066247420126,
"acc_norm": 0.2426470588235294,
"acc_norm_stderr": 0.02604066247420126
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2679738562091503,
"acc_stderr": 0.017917974069594722,
"acc_norm": 0.2679738562091503,
"acc_norm_stderr": 0.017917974069594722
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.3090909090909091,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.3090909090909091,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.14285714285714285,
"acc_stderr": 0.02240178743525639,
"acc_norm": 0.14285714285714285,
"acc_norm_stderr": 0.02240178743525639
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.2537313432835821,
"acc_stderr": 0.03076944496729602,
"acc_norm": 0.2537313432835821,
"acc_norm_stderr": 0.03076944496729602
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3313253012048193,
"acc_stderr": 0.03664314777288087,
"acc_norm": 0.3313253012048193,
"acc_norm_stderr": 0.03664314777288087
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.21637426900584794,
"acc_stderr": 0.03158149539338734,
"acc_norm": 0.21637426900584794,
"acc_norm_stderr": 0.03158149539338734
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2631578947368421,
"mc1_stderr": 0.01541524174023701,
"mc2": 0.4022295069003637,
"mc2_stderr": 0.01452265399729067
},
"harness|winogrande|5": {
"acc": 0.6637726913970008,
"acc_stderr": 0.013277286593993447
},
"harness|gsm8k|5": {
"acc": 0.019711902956785442,
"acc_stderr": 0.0038289829787356987
}
}
```
## 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] |
frankier/multiscale_rotten_tomatoes_critic_reviews | ---
language:
- en
language_creators:
- found
license: cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
tags:
- reviews
- ratings
- ordinal
- text
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
---
Cleaned up version of the rotten tomatoes critic reviews dataset. The original
is obtained from Kaggle:
https://www.kaggle.com/datasets/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset
Data has been scraped from the publicly available website
https://www.rottentomatoes.com as of 2020-10-31.
The clean up process drops anything without both a review and a rating, as well
as standardising the ratings onto several integer, ordinal scales.
Requires the `kaggle` library to be installed, and kaggle API keys passed
through environment variables or in ~/.kaggle/kaggle.json. See [the Kaggle
docs](https://www.kaggle.com/docs/api#authentication).
A processed version is available at
https://huggingface.co/datasets/frankier/processed_multiscale_rt_critics
|
bdsaglam/musique-answerable-2hop-subset-erx-reward-2023-12-30T19-00-52 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: reward
dtype: int64
splits:
- name: train
num_bytes: 127182
num_examples: 90
download_size: 19357
dataset_size: 127182
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rfernand/basic_sentence_transforms | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- other
multilinguality:
- monolingual
pretty_name: Active/Passive/Logical Transforms
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
tags:
- struct2struct
- tree2tree
task_categories:
- text2text-generation
task_ids: []
---
# Dataset Card for Active/Passive/Logical Transforms
## 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)
- [Dataset Subsets (Tasks)](#data-tasks)
- [Dataset Splits](#data-splits)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Roland Fernandez](mailto:rfernand@microsoft.com)
### Dataset Summary
This dataset is a synthetic dataset containing structure-to-structure transformation tasks between
English sentences in 3 forms: active, passive, and logical. The dataset also includes several
tree-transformation diagnostic/warm-up tasks.
### Supported Tasks and Leaderboards
[TBD]
### Languages
All data is in English.
## Dataset Structure
The dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a
test split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases).
Each sample in a split contains a source string, a target string, and 0-2 annotation strings.
### Dataset Subsets (Tasks)
The dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms.
The 12 diagnostic/warm-up tasks are:
```
- car_cdr_cons (small phrase translation tasks that require only: CAR, CDR, or CAR+CDR+CONS operations)
- car_cdr_cons_tuc (same task as car_cdr_cons, but requires mapping lowercase fillers to their uppercase tokens)
- car_cdr_rcons (same task as car_cdr_cons, but the CONS samples have their left/right children swapped)
- car_cdr_rcons_tuc (same task as car_cdr_rcons, but requires mapping lowercase fillers to their uppercase tokens)
- car_cdr_seq (each samples requires 1-4 combinations of CAR and CDR, as identified by the root filler oken)
- car_cdr_seq_40k (same task as car_cdr_seq, but train samples increased from 10K to 40K)
- car_cdr_seq_tuc (same task as car_cdr_seq, but requires mapping lowercase fillers to their uppercase tokens)
- car_cdr_seq_40k_tuc (same task as car_cdr_seq_tuc, but train samples increased from 10K to 40K)
- car_cdr_seq_path (similiar to car_cdr_seq, but each needed operation in represented as a node in the left child of the root)
- car_cdr_seq_path_40k (same task as car_cdr_seq_path, but train samples increased from 10K to 40K)
- car_cdr_seq_path_40k_tuc (same task as car_cdr_seq_path_40k, but requires mapping lowercase fillers to their uppercase tokens)
- car_cdr_seq_path_tuc (same task as car_cdr_seq_path, but requires mapping lowercase fillers to their uppercase tokens)
```
There are 22 core tasks are:
```
- active_active_stb (active sentence translation, from sentence to parenthesized tree form, both directions)
- active_active_stb_40k (same task as active_active_stb, but train samples increased from 10K to 40K)
- active_logical_ssb (active to logical sentence translation, in both directions)
- active_logical_ssb_40k (same task as active_logical_ssb, but train samples increased from 10K to 40K)
- active_logical_ttb (active to logical tree translation, in both directions)
- active_logical_ttb_40k (same task as active_logical_ttb, but train samples increased from 10K to 40K)
- active_passive_ssb (active to passive sentence translation, in both directions)
- active_passive_ssb_40k (same task as active_passive_ssb, but train samples increased from 10K to 40K)
- active_passive_ttb (active to passive tree translation, in both directions)
- active_passive_ttb_40k (same task as active_passive_ttb, but train samples increased from 10K to 40K)
- actpass_logical_ss (mixture of active to logical and passive to logical sentence translations, single direction)
- actpass_logical_ss_40k (same task as actpass_logical_ss, but train samples increased from 10K to 40K)
- actpass_logical_tt (mixture of active to logical and passive to logical tree translations, single direction)
- actpass_logical_tt_40k (same task as actpass_logical_tt, but train samples increased from 10K to 40K)
- logical_logical_stb (logical form sentence translation, from sentence to parenthesized tree form, both directions)
- logical_logical_stb_40k (same task as logical_logical_stb, but train samples increased from 10K to 40K)
- passive_logical_ssb (passive to logical sentence translation, in both directions)
- passive_logical_ssb_40k (same task as passive_logical_ssb, but train samples increased from 10K to 40K)
- passive_logical_ttb (passive to logical tree translation, in both directions)
- passive_logical_ttb_40k (same task as passive_logical_ttb, but train samples increased from 10K to 40K)
- passive_passive_stb (passive sentence translation, from sentence to parenthesized tree form, both directions)
- passive_passive_stb_40k (same task as passive_passive_stb, but train samples increased from 10K to 40K)
```
### Data Splits
Most tasks have the following splits:
- train
- validation
- test
- ood_new
- ood_long
- ood_all
Here is a table showing how the number of examples varies by split (for most tasks):
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| train | 10,000 |
| validation | 1,250 |
| test | 1,250 |
| ood_new | 1,250 |
| ood_long | 1,250 |
| ood_all | 1,250 |
### Data Instances
For each sample, there is source and target string. Source and target string are either plain text, or a parenthesized
version of a tree, depending on the task.
Here is an example from the *train* split of the *active_passive_ttb* task:
```
{
'source': '( S ( NP ( DET his ) ( AP ( N cat ) ) ) ( VP ( V discovered ) ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ) )',
'target': '( S ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ( VP ( AUXPS was ) ( VPPS ( V discovered ) ( PPPS ( PPS by ) ( NP ( DET his ) ( AP ( N cat ) ) ) ) ) ) )',
'direction': 'forward'
}
```
### Data Fields
- `source`: the string denoting the sequence or tree structure to be translated
- `target`: the string denoting the gold (aka label) sequence or tree structure
Optional annotation fields (their presence varies by task):
- `direction`: describes the direction of the translation (forward, backward), relative to the task name
- `count` : a string denoting the count of symbolic operations needed (e.g., "s3") to translate the source to the target
- `class` : a string denoting the type of translation needed
## Dataset Creation
### Curation Rationale
We wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus
on two types of out of distribution generalization: longer source sequences and new adjectives.
### Source Data
[N/A]
#### Initial Data Collection and Normalization
[N/A]
#### Who are the source language producers?
The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
### Annotations
Besides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that
describe the category and tree depth of each sample.
#### Annotation process
The annotation columns were generated from the each sample template and source sequence.
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No names or other sensitive information are included in the data.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can translated structured data from one form to another, in a
way that generalizes to out of distribution adjective values and lengths.
### Discussion of Biases
[TBD]
### Other Known Limitations
[TBD]
## Additional Information
The internal name of this dataset is nc_pat.
### Dataset Curators
The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
### Licensing Information
This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/).
### Citation Information
[TBD]
### Contributions
Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.
|
anik550689/logoControl | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2087073.0
num_examples: 16
download_size: 2097792
dataset_size: 2087073.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-133000 | ---
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: 996394
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alexshengzhili/Accel2ActivityCrawl | ---
dataset_info:
features:
- name: x
dtype:
array2_d:
shape:
- null
- 3
dtype: float32
- name: pid
dtype: string
- name: y
dtype: string
splits:
- name: realworld
num_bytes: 60005114
num_examples: 12446
- name: wisdm
num_bytes: 135013789
num_examples: 27996
- name: pamap
num_bytes: 45951178
num_examples: 2869
- name: oppo
num_bytes: 20551308
num_examples: 3882
- name: adl
num_bytes: 3061711
num_examples: 635
- name: capture24_100hz_w10_o0_rawlabel
num_bytes: 22051918703
num_examples: 1372784
- name: capture24_30hz_w10_o0_unfileterd_rawlabel
num_bytes: 7423532399
num_examples: 1372784
download_size: 10411206430
dataset_size: 29740034202
---
# Dataset Card for "Accel2ActivityCrawl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/180ed6b5 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 184
num_examples: 10
download_size: 1339
dataset_size: 184
---
# Dataset Card for "180ed6b5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_BreadAi__MuseCan | ---
pretty_name: Evaluation run of BreadAi/MuseCan
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BreadAi/MuseCan](https://huggingface.co/BreadAi/MuseCan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_BreadAi__MuseCan\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:02:11.200509](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__MuseCan/blob/main/results_2023-12-03T17-02-11.200509.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/BreadAi/MuseCan
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_07_19T19_29_11.706174
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T02_44_47.372597
path:
- '**/details_harness|drop|3_2023-10-13T02-44-47.372597.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T02-44-47.372597.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T02_44_47.372597
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-44-47.372597.parquet'
- split: 2023_12_03T17_02_11.200509
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-02-11.200509.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-02-11.200509.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:29:11.706174.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:29:11.706174.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:29:11.706174.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:29:11.706174.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:29:11.706174.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T02_44_47.372597
path:
- '**/details_harness|winogrande|5_2023-10-13T02-44-47.372597.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T02-44-47.372597.parquet'
- config_name: results
data_files:
- split: 2023_07_19T19_29_11.706174
path:
- results_2023-07-19T19:29:11.706174.parquet
- split: 2023_10_13T02_44_47.372597
path:
- results_2023-10-13T02-44-47.372597.parquet
- split: 2023_12_03T17_02_11.200509
path:
- results_2023-12-03T17-02-11.200509.parquet
- split: latest
path:
- results_2023-12-03T17-02-11.200509.parquet
---
# Dataset Card for Evaluation run of BreadAi/MuseCan
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/BreadAi/MuseCan
- **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 [BreadAi/MuseCan](https://huggingface.co/BreadAi/MuseCan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_BreadAi__MuseCan",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:02:11.200509](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__MuseCan/blob/main/results_2023-12-03T17-02-11.200509.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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] |
awettig/Pile-Gutenberg-0.5B-8K-opt | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500715780
num_examples: 61035
- name: test
num_bytes: 64969880
num_examples: 610
download_size: 1690264712
dataset_size: 6565685660
---
# Dataset Card for "Pile-Gutenberg-0.5B-8K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kamaljp/xsum_3000 | ---
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 7095386
num_examples: 3000
download_size: 4515775
dataset_size: 7095386
---
# Dataset Card for "xsum_3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tr416/dataset_20231006_203612 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73868
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231006_203612"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ImperialIndians23/nlp_cw_data_unprocessed_augmented_both | ---
dataset_info:
features:
- name: par_id
dtype: string
- name: community
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3270870
num_examples: 10757
- name: valid
num_bytes: 616626
num_examples: 2094
download_size: 2445690
dataset_size: 3887496
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
|
jpwahle/machine-paraphrase-dataset | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Machine Paraphrase Dataset (SpinnerChief/SpinBot)
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- spinbot
- spinnerchief
- plagiarism
- paraphrase
- academic integrity
- arxiv
- wikipedia
- theses
task_categories:
- text-classification
- text-generation
task_ids: []
paperswithcode_id: identifying-machine-paraphrased-plagiarism
dataset_info:
- split: train
download_size: 393224
dataset_size: 393224
- split: test
download_size: 655376
dataset_size: 655376
---
# Dataset Card for Machine Paraphrase Dataset (MPC)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/jpwahle/iconf22-paraphrase
- **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-96957-8_34
- **Total size:** 533 MB
- **Train size:** 340 MB
- **Test size:** 193 MB
### Dataset Summary
The Machine Paraphrase Corpus (MPC) consists of ~200k examples of original, and paraphrases using two online paraphrasing tools.
It uses two paraphrasing tools (SpinnerChief, SpinBot) on three source texts (Wikipedia, arXiv, student theses).
The examples are **not** aligned, i.e., we sample different paragraphs for originals and paraphrased versions.
### How to use it
You can load the dataset using the `load_dataset` function:
```python
from datasets import load_dataset
ds = load_dataset("jpwahle/machine-paraphrase-dataset")
print(ds[0])
#OUTPUT:
{
'text': 'The commemoration was revealed on Whit Monday 16 May 1921 by the Prince of Wales later King Edward VIII with Lutyens in participation At the divulging function Lord Fortescue gave a discourse in which he evaluated that 11600 people from Devon had been slaughtered while serving in the war He later expressed that somewhere in the range of 63700 8000 regulars 36700 volunteers and 19000 recruits had served in the military The names of the fallen were recorded on a move of respect of which three duplicates were made one for Exeter Cathedral one to be held by the district chamber and one which the Prince of Wales put in an empty in the base of the war dedication The rulers visit created impressive energy in the zone A large number of individuals lined the road to welcome his motorcade and shops on the High Street hung out pennants with inviting messages After the uncovering Edward went through ten days visiting the neighborhood ',
'label': 1,
'dataset': 'wikipedia',
'method': 'spinbot'
}
```
### Supported Tasks and Leaderboards
Paraphrase Identification
### Languages
English
## Dataset Structure
### Data Instances
```json
{
'text': 'The commemoration was revealed on Whit Monday 16 May 1921 by the Prince of Wales later King Edward VIII with Lutyens in participation At the divulging function Lord Fortescue gave a discourse in which he evaluated that 11600 people from Devon had been slaughtered while serving in the war He later expressed that somewhere in the range of 63700 8000 regulars 36700 volunteers and 19000 recruits had served in the military The names of the fallen were recorded on a move of respect of which three duplicates were made one for Exeter Cathedral one to be held by the district chamber and one which the Prince of Wales put in an empty in the base of the war dedication The rulers visit created impressive energy in the zone A large number of individuals lined the road to welcome his motorcade and shops on the High Street hung out pennants with inviting messages After the uncovering Edward went through ten days visiting the neighborhood ',
'label': 1,
'dataset': 'wikipedia',
'method': 'spinbot'
}
```
### Data Fields
| Feature | Description |
| --- | --- |
| `text` | The unique identifier of the paper. |
| `label` | Whether it is a paraphrase (1) or the original (0). |
| `dataset` | The source dataset (Wikipedia, arXiv, or theses). |
| `method` | The method used (SpinBot, SpinnerChief, original). |
### Data Splits
- train (Wikipedia x Spinbot)
- test ([Wikipedia, arXiv, theses] x [SpinBot, SpinnerChief])
## Dataset Creation
### Curation Rationale
Providing a resource for testing against machine-paraprhased plagiarism.
### Source Data
#### Initial Data Collection and Normalization
- Paragraphs from `featured articles` from the English Wikipedia dump
- Paragraphs from full-text pdfs of arXMLiv
- Paragraphs from full-text pdfs of Czech student thesis (bachelor, master, PhD).
#### 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
[Jan Philip Wahle](https://jpwahle.com/)
### Licensing Information
The Machine Paraphrase Dataset is released under CC BY-NC 4.0. By using this corpus, you agree to its usage terms.
### Citation Information
```bib
@inproceedings{10.1007/978-3-030-96957-8_34,
title = {Identifying Machine-Paraphrased Plagiarism},
author = {Wahle, Jan Philip and Ruas, Terry and Folt{\'y}nek, Tom{\'a}{\v{s}} and Meuschke, Norman and Gipp, Bela},
year = 2022,
booktitle = {Information for a Better World: Shaping the Global Future},
publisher = {Springer International Publishing},
address = {Cham},
pages = {393--413},
isbn = {978-3-030-96957-8},
editor = {Smits, Malte},
abstract = {Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99{\%} (F1 = 99.68{\%} for SpinBot and F1 = 71.64{\%} for SpinnerChief cases), while human evaluators achieved F1 = 78.4{\%} for SpinBot and F1 = 65.6{\%} for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.}
}
```
### Contributions
Thanks to [@jpwahle](https://github.com/jpwahle) for adding this dataset. |
maneshkarun/median3k_10000s | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
- name: hyperpartisan
dtype: bool
- name: url
dtype: string
- name: published_at
dtype: string
- name: bias
dtype:
class_label:
names:
'0': right
'1': right-center
'2': least
'3': left-center
'4': left
- name: word_count
dtype: int64
- name: cleaned_data
dtype: string
- name: pos_tagged
dtype: string
splits:
- name: train
num_bytes: 732080426.0
num_examples: 10000
download_size: 355605083
dataset_size: 732080426.0
---
|
HeshamHaroon/data_2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 843389
num_examples: 500
download_size: 388298
dataset_size: 843389
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bigbio/paramed |
---
language:
- en
- zh
bigbio_language:
- English
- Chinese
license: cc-by-4.0
multilinguality: multilingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: ParaMed
homepage: https://github.com/boxiangliu/ParaMed
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TRANSLATION
---
# Dataset Card for ParaMed
## Dataset Description
- **Homepage:** https://github.com/boxiangliu/ParaMed
- **Pubmed:** False
- **Public:** True
- **Tasks:** TRANSL
NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website.
English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through
http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011.
## Citation Information
```
@article{liu2021paramed,
author = {Liu, Boxiang and Huang, Liang},
title = {ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain},
journal = {BMC Medical Informatics and Decision Making},
volume = {21},
year = {2021},
url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01621-8},
doi = {10.1186/s12911-021-01621-8}
}
```
|
Major-TOM/Core-S2L2A | ---
license: cc-by-sa-4.0
tags:
- earth-observation
- remote-sensing
- sentinel-2
- multi-spectral
- satellite
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
features:
- name: product_id
dtype: string
- name: grid_cell
dtype: string
- name: product_datetime
dtype: string
- name: thumbnail
dtype: image
- name: B01
dtype: binary
- name: B02
dtype: binary
- name: B03
dtype: binary
- name: B04
dtype: binary
- name: B05
dtype: binary
- name: B06
dtype: binary
- name: B07
dtype: binary
- name: B08
dtype: binary
- name: B8A
dtype: binary
- name: B09
dtype: binary
- name: B11
dtype: binary
- name: B12
dtype: binary
- name: cloud_mask
dtype: binary
configs:
- config_name: default
data_files: images/*.parquet
- config_name: metadata
data_files: metadata.parquet
---
# Core-S2L2A
Contains a global coverage of Sentinel-2 (Level 1C) patches, each of size 1,068 x 1,068 pixels.
| Source | Sensing Type | Number of Patches | Patch Size | Total Pixels |
|--------|--------------|-------------------|------------|--------------|
|Sentinel-2 Level-2A |Optical Multispectral|2,245,886|1,068 x 1,068 (10 m) | > 2.564 Trillion |
## Content
| Column | Details | Resolution |
|--------|---------|------------|
| B01 | Coastal aerosol, 442.7 nm (S2A), 442.3 nm (S2B) | 60m |
| B02 | Blue, 492.4 nm (S2A), 492.1 nm (S2B) | 10m |
| B03 | Green, 559.8 nm (S2A), 559.0 nm (S2B) | 10m |
| B04 | Red, 664.6 nm (S2A), 665.0 nm (S2B) | 10m |
| B05 | Vegetation red edge, 704.1 nm (S2A), 703.8 nm (S2B) | 20m |
| B06 | Vegetation red edge, 740.5 nm (S2A), 739.1 nm (S2B) | 20m |
| B07 | Vegetation red edge, 782.8 nm (S2A), 779.7 nm (S2B) | 20m |
| B08 | NIR, 832.8 nm (S2A), 833.0 nm (S2B) | 10m |
| B8A | Narrow NIR, 864.7 nm (S2A), 864.0 nm (S2B) | 20m |
| B09 | Water vapour, 945.1 nm (S2A), 943.2 nm (S2B) | 60m |
| B11 | SWIR, 1613.7 nm (S2A), 1610.4 nm (S2B) | 20m |
| B12 | SWIR, 2202.4 nm (S2A), 2185.7 nm (S2B) | 20m |
| cloud_mask | Cloud Mask produced by SEnSeI | 10m |
| thumbnail | RGB composite [B04, B03, B02] saved as png | 10m |
## Spatial Coverage
This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-2 is contained at least once in this dataset (and only once, excluding some marginal overlaps).
The following figure demonstrates the spatial coverage (only black pixels are absent):
## Example Use
Interface scripts are available at https://github.com/ESA-PhiLab/Major-TOM
Here's a sneak peek with a thumbnail image:
```python
from fsspec.parquet import open_parquet_file
import pyarrow.parquet as pq
from io import BytesIO
from PIL import Image
PARQUET_FILE = 'part_03900' # parquet number
ROW_INDEX = 42 # row number (about 500 per parquet)
url = "https://huggingface.co/datasets/Major-TOM/Core-S2L2A/resolve/main/images/{}.parquet".format(PARQUET_FILE)
with open_parquet_file(url,columns = ["thumbnail"]) as f:
with pq.ParquetFile(f) as pf:
first_row_group = pf.read_row_group(ROW_INDEX, columns=['thumbnail'])
stream = BytesIO(first_row_group['thumbnail'][0].as_py())
image = Image.open(stream)
```
## Cite
[](https://arxiv.org/abs/2402.12095/)
```latex
@inproceedings{Major_TOM,
title={Major TOM: Expandable Datasets for Earth Observation},
author={Alistair Francis and Mikolaj Czerkawski},
year={2024},
eprint={2402.12095},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://huggingface.co/ESA-philab) |
izou3/Food-Prototype | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 74206828.0
num_examples: 400
download_size: 73784241
dataset_size: 74206828.0
---
# Dataset Card for "Food-Prototype"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
minimario/gsm8k-rewritten | ---
dataset_info:
features:
- name: old_question
dtype: string
- name: old_answer
dtype: string
- name: old_solution
dtype: string
- name: old_samenumber_question
dtype: string
- name: old_samenumber_solution
dtype: string
- name: new_question
dtype: string
- name: new_answer
dtype: string
- name: reworded_new_question
dtype: string
- name: reworded_new_question_2
dtype: string
splits:
- name: train
num_bytes: 161317
num_examples: 100
download_size: 109831
dataset_size: 161317
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ademax/vlsp-vie-speech2text1 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
splits:
- name: train
num_bytes: 11494592703.904
num_examples: 55427
- name: test
num_bytes: 203536532.0
num_examples: 1000
download_size: 11661514479
dataset_size: 11698129235.904
---
# Dataset Card for "vlsp-vie-speech2text1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_one-man-army__una-neural-chat-v3-3-P1-OMA | ---
pretty_name: Evaluation run of one-man-army/una-neural-chat-v3-3-P1-OMA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [one-man-army/una-neural-chat-v3-3-P1-OMA](https://huggingface.co/one-man-army/una-neural-chat-v3-3-P1-OMA)\
\ 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_one-man-army__una-neural-chat-v3-3-P1-OMA\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-12T11:36:35.468608](https://huggingface.co/datasets/open-llm-leaderboard/details_one-man-army__una-neural-chat-v3-3-P1-OMA/blob/main/results_2023-12-12T11-36-35.468608.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\": 0.636840960894971,\n\
\ \"acc_stderr\": 0.0325649293445393,\n \"acc_norm\": 0.6380848976326104,\n\
\ \"acc_norm_stderr\": 0.0332251069866789,\n \"mc1\": 0.4847001223990208,\n\
\ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6434691270555646,\n\
\ \"mc2_stderr\": 0.015146860071018828\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175456,\n\
\ \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.013760988200880543\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6734714200358495,\n\
\ \"acc_stderr\": 0.004679847503411345,\n \"acc_norm\": 0.8591913961362279,\n\
\ \"acc_norm_stderr\": 0.0034711315448920418\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.0387813988879761,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.0387813988879761\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\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\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\
\ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113728,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113728\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.7451612903225806,\n \"acc_stderr\": 0.024790118459332208,\n \"\
acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.024790118459332208\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\": 0.72,\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\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091826,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091826\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229872,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229872\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083015,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083015\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\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\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501538,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501538\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849313,\n \"\
acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849313\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\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\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.042365112580946315\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\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077816,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077816\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608311,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608311\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\
\ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39664804469273746,\n\
\ \"acc_stderr\": 0.016361354769822468,\n \"acc_norm\": 0.39664804469273746,\n\
\ \"acc_norm_stderr\": 0.016361354769822468\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\
\ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765137,\n\
\ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765137\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291456,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291456\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43415906127770537,\n\
\ \"acc_stderr\": 0.012659033237067248,\n \"acc_norm\": 0.43415906127770537,\n\
\ \"acc_norm_stderr\": 0.012659033237067248\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031215,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031215\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6601307189542484,\n \"acc_stderr\": 0.019162418588623567,\n \
\ \"acc_norm\": 0.6601307189542484,\n \"acc_norm_stderr\": 0.019162418588623567\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.710204081632653,\n \"acc_stderr\": 0.029043088683304328,\n\
\ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.029043088683304328\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4847001223990208,\n\
\ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6434691270555646,\n\
\ \"mc2_stderr\": 0.015146860071018828\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626913\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6186504927975739,\n \
\ \"acc_stderr\": 0.013379089877400715\n }\n}\n```"
repo_url: https://huggingface.co/one-man-army/una-neural-chat-v3-3-P1-OMA
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_12T11_36_35.468608
path:
- '**/details_harness|arc:challenge|25_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|gsm8k|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hellaswag|10_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T11-36-35.468608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T11-36-35.468608.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- '**/details_harness|winogrande|5_2023-12-12T11-36-35.468608.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-12T11-36-35.468608.parquet'
- config_name: results
data_files:
- split: 2023_12_12T11_36_35.468608
path:
- results_2023-12-12T11-36-35.468608.parquet
- split: latest
path:
- results_2023-12-12T11-36-35.468608.parquet
---
# Dataset Card for Evaluation run of one-man-army/una-neural-chat-v3-3-P1-OMA
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [one-man-army/una-neural-chat-v3-3-P1-OMA](https://huggingface.co/one-man-army/una-neural-chat-v3-3-P1-OMA) 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_one-man-army__una-neural-chat-v3-3-P1-OMA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-12T11:36:35.468608](https://huggingface.co/datasets/open-llm-leaderboard/details_one-man-army__una-neural-chat-v3-3-P1-OMA/blob/main/results_2023-12-12T11-36-35.468608.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": 0.636840960894971,
"acc_stderr": 0.0325649293445393,
"acc_norm": 0.6380848976326104,
"acc_norm_stderr": 0.0332251069866789,
"mc1": 0.4847001223990208,
"mc1_stderr": 0.017495304473187902,
"mc2": 0.6434691270555646,
"mc2_stderr": 0.015146860071018828
},
"harness|arc:challenge|25": {
"acc": 0.6390784982935154,
"acc_stderr": 0.014034761386175456,
"acc_norm": 0.6680887372013652,
"acc_norm_stderr": 0.013760988200880543
},
"harness|hellaswag|10": {
"acc": 0.6734714200358495,
"acc_stderr": 0.004679847503411345,
"acc_norm": 0.8591913961362279,
"acc_norm_stderr": 0.0034711315448920418
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.0387813988879761,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.0387813988879761
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880267,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880267
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
vegeta/testargilla | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
dataset_info:
features:
- name: metadata
dtype: string
- name: text
dtype: string
id: field
- name: label
dtype: string
id: field
- name: question-1
sequence:
- name: user_id
dtype: string
- name: value
dtype: string
- name: status
dtype: string
id: question
- name: question-2
sequence:
- name: user_id
dtype: string
- name: value
dtype: int32
- name: status
dtype: string
id: question
- name: external_id
dtype: string
id: external_id
splits:
- name: train
num_bytes: 148
num_examples: 1
download_size: 0
dataset_size: 148
---
# Dataset Card for testargilla
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.cfg`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("vegeta/testargilla")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("vegeta/testargilla")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | TextField | True | False |
| label | Label | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| question-1 | Question-1 | TextQuestion | True | This is the first question | N/A |
| question-2 | Question-2 | RatingQuestion | True | This is the second question | [1, 2, 3, 4, 5] |
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "entry-1",
"fields": {
"label": "positive",
"text": "This is the first record"
},
"metadata": null,
"responses": [
{
"status": "submitted",
"user_id": null,
"values": {
"question-1": {
"value": "This is the first answer"
},
"question-2": {
"value": 5
}
}
}
]
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "entry-1",
"label": "positive",
"metadata": null,
"question-1": {
"status": [
"submitted"
],
"user_id": [
null
],
"value": [
"This is the first answer"
]
},
"question-2": {
"status": [
"submitted"
],
"user_id": [
null
],
"value": [
5
]
},
"text": "This is the first record"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `TextField`.
* **label** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
* **question-1** is of type `TextQuestion`, and description "This is the first question".
* **question-2** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5], and description "This is the second question".
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
These are the annotation guidelines.
#### 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] |
Lagyamfi/asante_twi_bible | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcript
dtype: string
- name: verse
dtype: string
splits:
- name: train
num_bytes: 15721845939.576
num_examples: 21348
- name: test
num_bytes: 49094317.0
num_examples: 64
- name: validation
num_bytes: 183952529.0
num_examples: 217
download_size: 15531540341
dataset_size: 15954892785.576
license: cc-by-sa-3.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- tw
tags:
- GhanaNLP
- Twi
size_categories:
- 1B<n<10B
---
# Dataset Card for "asante_twi_bible"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Borrri/Borrifinetune2 | ---
license: cc
---
|
adamwatters/half-dome | ---
license: openrail
---
|
bcmaster/jailson16 | ---
license: openrail
---
|
Indic-LLM-Labs/Fleurs-Kn | ---
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: id
dtype: int32
- name: num_samples
dtype: int32
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
- name: gender
dtype:
class_label:
names:
'0': male
'1': female
'2': other
- name: language
dtype: string
- name: lang_group_id
splits:
- name: train
num_bytes: 1910030202.243
num_examples: 2283
- name: validation
num_bytes: 299915580
num_examples: 368
- name: test
num_bytes: 732875657
num_examples: 838
download_size: 2915269155
dataset_size: 2942821439.243
license: mit
task_categories:
- automatic-speech-recognition
language:
- kn
size_categories:
- 1K<n<10K
---
This is a filtered version of the [Fleurs](https://huggingface.co/datasets/google/fleurs) dataset only containing samples of Kannada language.
The dataset contains total of 2283 training, 368 validation and 838 test samples.
### Data Sample:
```python
{'id': 1053,
'num_samples': 226560,
'path': '/home/ravi.naik/.cache/huggingface/datasets/downloads/extracted/e7c8b501d4e6892673b6dc291d42de48e7987b0d2aa6471066a671f686224ed1/10000267636955490843.wav',
'audio': {'path': 'train/10000267636955490843.wav',
'array': array([ 0. , 0. , 0. , ..., -0.00100893,
-0.00109982, -0.00118315]),
'sampling_rate': 16000},
'transcription': 'ವಿದೇಶದಲ್ಲಿ ವಾಸಿಸಿದ ನಂತರ ನೀವು ನಿಮ್ಮಊರಿಗೆ ಮರಳಿದಾಗ ನೀವು ಹೊಸ ಸಂಸ್ಕೃತಿಗೆ ಹೊಂದಿಕೊಂಡಿದ್ದೀರಿ ಮತ್ತು ನಿಮ್ಮ ಕುಟುಂಬ ಸಂಸ್ಕೃತಿಯಿಂದ ಕೆಲವು ಅಭ್ಯಾಸಗಳನ್ನು ಕಳೆದುಕೊಂಡಿದ್ದೀರಿ',
'raw_transcription': 'ವಿದೇಶದಲ್ಲಿ ವಾಸಿಸಿದ ನಂತರ ನೀವು ನಿಮ್ಮಊರಿಗೆ ಮರಳಿದಾಗ, ನೀವು ಹೊಸ ಸಂಸ್ಕೃತಿಗೆ ಹೊಂದಿಕೊಂಡಿದ್ದೀರಿ ಮತ್ತು ನಿಮ್ಮ ಕುಟುಂಬ ಸಂಸ್ಕೃತಿಯಿಂದ ಕೆಲವು ಅಭ್ಯಾಸಗಳನ್ನು ಕಳೆದುಕೊಂಡಿದ್ದೀರಿ.',
'gender': 1,
'lang_id': 47,
'language': 'Kannada',
'lang_group_id': 4}
```
### Data Fields
The data fields are the same among all splits.
- **id** (int): ID of audio sample
- **num_samples** (int): Number of float values
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **raw_transcription** (str): The non-normalized transcription of the audio file
- **transcription** (str): Transcription of the audio file
- **gender** (int): Class id of gender
- **lang_id** (int): Class id of language
- **lang_group_id** (int): Class id of language group
### Use with Datasets
```python
from datasets import load_dataset
fleurs_kn = load_dataset("Indic-LLM-Labs/Fleurs-Kn", split="train", streaming=True)
print(next(iter(fleurs_kn)))
``` |
tyzhu/random_letter_find_passage_train100_eval20_title | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 23400
num_examples: 220
- name: validation
num_bytes: 2522
num_examples: 20
download_size: 0
dataset_size: 25922
---
# Dataset Card for "random_letter_find_passage_train100_eval20_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dlb/plue | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- pt
license:
- lgpl-3.0
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
pretty_name: PLUE (Portuguese Language Understanding Evaluation benchmark)
tags:
- paraphrase-identification
- qa-nli
- coreference-nli
---
# Dataset Card for PLUE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/ju-resplande/PLUE
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese translation of the <a href="https://gluebenchmark.com/">GLUE benchmark</a>, <a href=https://nlp.stanford.edu/projects/snli/>SNLI</a>, and <a href=https://allenai.org/data/scitail> Scitail</a> using <a href=https://github.com/Helsinki-NLP/OPUS-MT>OPUS-MT model</a> and <a href=https://cloud.google.com/translate/docs>Google Cloud Translation</a>.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language data in PLUE is Brazilian Portuguese (BCP-47 pt-BR)
## 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
```bibtex
@misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {PLUE: Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset. |
Forbes21/Silozi | ---
license: openrail
---
|
PixArt-alpha/pixart-sigma-toy-dataset | ---
license: openrail++
---
|
Fuat55/MaskOrNotMask | ---
language:
- tr
--- |
Cohere/wikipedia-22-12-ja-embeddings | ---
language:
- ja
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (ja) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (ja)](https://ja.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ja-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ja-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-ja-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
J0nasW/paperswithcode | ---
license: mit
task_categories:
- text-classification
- feature-extraction
language:
- en
size_categories:
- 10K<n<100K
---
# A cleaned dataset from [paperswithcode.com](https://paperswithcode.com/)
*Last dataset update: July 2023*
This is a cleaned up dataset optained from [paperswithcode.com](https://paperswithcode.com/) through their [API](https://paperswithcode.com/api/v1/docs/) service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information.
It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.
### Contents
It contains the following tables:
- papers.csv (around 56K)
- papers_train.csv (80% from 56K)
- papers_test.csv (20% from 56K)
- tasks.csv
- areas.csv
### Specials
UUIDs were added to the dataset since the PapersWithCode IDs (pwc_ids) are not distinct enough. These UUIDs may change in the future with new versions of the dataset.
Also, embeddings were calculated for all of the 56K papers using the brilliant model [SciNCL](https://huggingface.co/malteos/scincl) as well as dimensionality-redused 2D coordinates using UMAP.
There is also a simple Python Notebook which was used to optain and refactor the dataset. |
peterwz/wiki-filtered-0 | ---
dataset_info:
features:
- name: original
dtype: string
- name: summary
dtype: string
- name: compression_ratio
dtype: string
splits:
- name: train
num_bytes: 13891902
num_examples: 494
download_size: 2372125
dataset_size: 13891902
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wiki-filtered-0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ashnrk/cifar10_lt_r10_text | ---
dataset_info:
features:
- name: img
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
- name: text_label
dtype: string
splits:
- name: train
num_bytes: 9133039.5
num_examples: 4084
download_size: 9126904
dataset_size: 9133039.5
---
# Dataset Card for "cifar10_lt_r10_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MakiPan/Hagrid-mediapipe-hand-enc | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 103636458172.304
num_examples: 470648
download_size: 103950952500
dataset_size: 103636458172.304
---
# Dataset Card for "Hagrid-mediapipe-hand-enc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iara-project/hdbscan_generated_sample_8 | ---
dataset_info:
features:
- name: news_id
dtype: string
- name: embeddings
sequence: float64
- name: sentence
dtype: string
- name: category
dtype: string
- name: labels
dtype: int64
- name: probs
dtype: float64
splits:
- name: train
num_bytes: 1429357.7779391108
num_examples: 152
download_size: 1226866
dataset_size: 1429357.7779391108
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hdbscan_generated_sample_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
roy29fuku/sample-large | ---
configs:
- config_name: default
data_files:
- split: train
path: "data/*.parquet"
---
[Hugging Face データセット作成チュートリアル](https://colab.research.google.com/drive/11rl9Wie22JVIB5bjj3W6bnygfWFlNijW#scrollTo=XXlFnTh04WLc)で用いるサンプルデータです。
データはPMC OS Subsetの[oa_comm_xml.PMC010xxxxxx.baseline.2023-12-18.tar.gz](https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_comm/xml/)から約40万件分の論文のAbstractを抽出して作成しました。 |
vhtran/de-en | ---
license: cc-by-4.0
---
|
kye/instruct-math-gptneox-8k | ---
license: mit
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-8000 | ---
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: 1018980
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mehr32/divar | ---
license: gpl-3.0
---
|
lshowway/wikipedia.reorder.sov.fr | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 886603410
num_examples: 490371
download_size: 402994481
dataset_size: 886603410
---
# Dataset Card for "wikipedia.reorder.sov.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adidayno/test | ---
license: apache-2.0
---
|
jonathanli/legal-advice-reddit | ---
language:
- en
tags:
- reddit
- law
pretty_name: Legal Advice Reddit
---
# Dataset Card for Legal Advice Reddit Dataset
## Dataset Description
- **Paper: [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10/)**
- **Point of Contact: jxl@queensu.ca**
### Dataset Summary
New dataset introduced in [Parameter-Efficient Legal Domain Adaptation](https://aclanthology.org/2022.nllp-1.10) (Li et al., NLLP 2022) from the Legal Advice Reddit community (known as "/r/legaldvice"), sourcing the Reddit posts from the Pushshift
Reddit dataset. The dataset maps the text and title of each legal question posted into one of eleven classes, based on the original Reddit
post's "flair" (i.e., tag). Questions are typically informal and use non-legal-specific language. Per the Legal Advice Reddit rules, posts
must be about actual personal circumstances or situations. We limit the number of labels to the top eleven classes and remove the other
samples from the dataset.
### Citation Information
```
@inproceedings{li-etal-2022-parameter,
title = "Parameter-Efficient Legal Domain Adaptation",
author = "Li, Jonathan and
Bhambhoria, Rohan and
Zhu, Xiaodan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.10",
pages = "119--129",
}
``` |
ohsuz/DACON_CORPUS | ---
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2853521
num_examples: 4791
download_size: 496163
dataset_size: 2853521
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HamdanXI/modified_daily_dialog_sentence_v2 | ---
dataset_info:
features:
- name: dialogue
dtype: string
splits:
- name: train
num_bytes: 5620176
num_examples: 137271
download_size: 3639530
dataset_size: 5620176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "modified_daily_dialog_sentence_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nadav/pixel_glue_rte | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 52129350.75
num_examples: 2490
- name: validation
num_bytes: 5691033.0
num_examples: 277
download_size: 57449363
dataset_size: 57820383.75
---
# Dataset Card for "pixel_glue_rte"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jlbaker361/kaggle_males_dim_128_5k | ---
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: src
dtype: string
- name: style
dtype: string
splits:
- name: train
num_bytes: 106825871.0
num_examples: 5000
download_size: 106639763
dataset_size: 106825871.0
---
# Dataset Card for "kaggle_males_dim_128_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
winglian/financial_phrasebank_augmented-validation | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: label_text
dtype: string
- name: analysis
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 845976
num_examples: 720
download_size: 339475
dataset_size: 845976
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kekunh/stock-related-tweets-vol2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 14520743
num_examples: 100000
download_size: 9829810
dataset_size: 14520743
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
izaq09/starwars_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2966960.0
num_examples: 7
download_size: 2933224
dataset_size: 2966960.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "starwars_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Undi95__Mixtral-4x7B-DPO-RPChat | ---
pretty_name: Evaluation run of Undi95/Mixtral-4x7B-DPO-RPChat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Undi95/Mixtral-4x7B-DPO-RPChat](https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat)\
\ 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_Undi95__Mixtral-4x7B-DPO-RPChat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-21T18:02:50.805151](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mixtral-4x7B-DPO-RPChat/blob/main/results_2023-12-21T18-02-50.805151.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\": 0.6364758984846143,\n\
\ \"acc_stderr\": 0.03248435449110896,\n \"acc_norm\": 0.6395418451801348,\n\
\ \"acc_norm_stderr\": 0.03313279974443489,\n \"mc1\": 0.3402692778457772,\n\
\ \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.4987103568406069,\n\
\ \"mc2_stderr\": 0.01525862718504237\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6092150170648464,\n \"acc_stderr\": 0.01425856388051378,\n\
\ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756557\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6632144991037642,\n\
\ \"acc_stderr\": 0.004716449792353791,\n \"acc_norm\": 0.8536148177653854,\n\
\ \"acc_norm_stderr\": 0.003527695149823511\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\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\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797611,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797611\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\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\": 0.34,\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\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\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\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"\
acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7575757575757576,\n \"acc_stderr\": 0.03053289223393202,\n \"\
acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03053289223393202\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n\
\ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524572,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524572\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291946,\n\
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291946\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659807,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659807\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.818348623853211,\n \"acc_stderr\": 0.01653061740926685,\n \"acc_norm\"\
: 0.818348623853211,\n \"acc_norm_stderr\": 0.01653061740926685\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n\
\ \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.5138888888888888,\n\
\ \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n\
\ \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\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\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757485,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757485\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7395498392282959,\n\
\ \"acc_stderr\": 0.02492672322484554,\n \"acc_norm\": 0.7395498392282959,\n\
\ \"acc_norm_stderr\": 0.02492672322484554\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495036,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495036\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \
\ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4602346805736636,\n\
\ \"acc_stderr\": 0.012729785386598564,\n \"acc_norm\": 0.4602346805736636,\n\
\ \"acc_norm_stderr\": 0.012729785386598564\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6437908496732027,\n \"acc_stderr\": 0.0193733324207245,\n \
\ \"acc_norm\": 0.6437908496732027,\n \"acc_norm_stderr\": 0.0193733324207245\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675596,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\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\": 0.3402692778457772,\n\
\ \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.4987103568406069,\n\
\ \"mc2_stderr\": 0.01525862718504237\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.01149338468724977\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5314632297194845,\n \
\ \"acc_stderr\": 0.013745189948450415\n }\n}\n```"
repo_url: https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat
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_21T18_02_50.805151
path:
- '**/details_harness|arc:challenge|25_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|gsm8k|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hellaswag|10_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-21T18-02-50.805151.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-21T18-02-50.805151.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- '**/details_harness|winogrande|5_2023-12-21T18-02-50.805151.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-21T18-02-50.805151.parquet'
- config_name: results
data_files:
- split: 2023_12_21T18_02_50.805151
path:
- results_2023-12-21T18-02-50.805151.parquet
- split: latest
path:
- results_2023-12-21T18-02-50.805151.parquet
---
# Dataset Card for Evaluation run of Undi95/Mixtral-4x7B-DPO-RPChat
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Undi95/Mixtral-4x7B-DPO-RPChat](https://huggingface.co/Undi95/Mixtral-4x7B-DPO-RPChat) 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_Undi95__Mixtral-4x7B-DPO-RPChat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-21T18:02:50.805151](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mixtral-4x7B-DPO-RPChat/blob/main/results_2023-12-21T18-02-50.805151.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": 0.6364758984846143,
"acc_stderr": 0.03248435449110896,
"acc_norm": 0.6395418451801348,
"acc_norm_stderr": 0.03313279974443489,
"mc1": 0.3402692778457772,
"mc1_stderr": 0.016586304901762557,
"mc2": 0.4987103568406069,
"mc2_stderr": 0.01525862718504237
},
"harness|arc:challenge|25": {
"acc": 0.6092150170648464,
"acc_stderr": 0.01425856388051378,
"acc_norm": 0.6459044368600683,
"acc_norm_stderr": 0.013975454122756557
},
"harness|hellaswag|10": {
"acc": 0.6632144991037642,
"acc_stderr": 0.004716449792353791,
"acc_norm": 0.8536148177653854,
"acc_norm_stderr": 0.003527695149823511
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"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": 0.6513157894736842,
"acc_stderr": 0.03878139888797611,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.03878139888797611
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493857,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493857
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"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": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.048108401480826346,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.048108401480826346
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"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": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.025487187147859375,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.025487187147859375
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7677419354838709,
"acc_stderr": 0.024022256130308235,
"acc_norm": 0.7677419354838709,
"acc_norm_stderr": 0.024022256130308235
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03053289223393202,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03053289223393202
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
"acc_stderr": 0.024233532297758733,
"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.024233532297758733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6410256410256411,
"acc_stderr": 0.024321738484602354,
"acc_norm": 0.6410256410256411,
"acc_norm_stderr": 0.024321738484602354
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524572,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524572
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6974789915966386,
"acc_stderr": 0.029837962388291946,
"acc_norm": 0.6974789915966386,
"acc_norm_stderr": 0.029837962388291946
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.39072847682119205,
"acc_stderr": 0.03983798306659807,
"acc_norm": 0.39072847682119205,
"acc_norm_stderr": 0.03983798306659807
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.818348623853211,
"acc_stderr": 0.01653061740926685,
"acc_norm": 0.818348623853211,
"acc_norm_stderr": 0.01653061740926685
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.027865942286639318,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.027865942286639318
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"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": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.04058042015646034,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.04058042015646034
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993457,
"acc_norm": 0.8237547892720306,
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
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"harness|truthfulqa:mc|0": {
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"harness|gsm8k|5": {
"acc": 0.5314632297194845,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
drt/kqa_pro | ---
annotations_creators:
- machine-generated
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: KQA-Pro
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- knowledge graph
- freebase
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
# Dataset Card for KQA Pro
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Configs](#data-configs)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [How to run SPARQLs and programs](#how-to-run-sparqls-and-programs)
- [Knowledge Graph File](#knowledge-graph-file)
- [How to Submit to Leaderboard](#how-to-submit-results-of-test-set)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://thukeg.gitee.io/kqa-pro/
- **Repository:** https://github.com/shijx12/KQAPro_Baselines
- **Paper:** [KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base](https://aclanthology.org/2022.acl-long.422/)
- **Leaderboard:** http://thukeg.gitee.io/kqa-pro/leaderboard.html
- **Point of Contact:** shijx12 at gmail dot com
### Dataset Summary
KQA Pro is a large-scale dataset of complex question answering over knowledge base. The questions are very diverse and challenging, requiring multiple reasoning capabilities including compositional reasoning, multi-hop reasoning, quantitative comparison, set operations, and etc. Strong supervisions of SPARQL and program are provided for each question.
### Supported Tasks and Leaderboards
It supports knowlege graph based question answering. Specifically, it provides SPARQL and *program* for each question.
### Languages
English
## Dataset Structure
**train.json/val.json**
```
[
{
'question': str,
'sparql': str, # executable in our virtuoso engine
'program':
[
{
'function': str, # function name
'dependencies': [int], # functional inputs, representing indices of the preceding functions
'inputs': [str], # textual inputs
}
],
'choices': [str], # 10 answer choices
'answer': str, # golden answer
}
]
```
**test.json**
```
[
{
'question': str,
'choices': [str], # 10 answer choices
}
]
```
### Data Configs
This dataset has two configs: `train_val` and `test` because they have different available fields. Please specify this like `load_dataset('drt/kqa_pro', 'train_val')`.
### Data Splits
train, val, test
## Additional Information
### Knowledge Graph File
You can find the knowledge graph file `kb.json` in the original github repository. It comes with the format:
```json
{
'concepts':
{
'<id>':
{
'name': str,
'instanceOf': ['<id>', '<id>'], # ids of parent concept
}
},
'entities': # excluding concepts
{
'<id>':
{
'name': str,
'instanceOf': ['<id>', '<id>'], # ids of parent concept
'attributes':
[
{
'key': str, # attribute key
'value': # attribute value
{
'type': 'string'/'quantity'/'date'/'year',
'value': float/int/str, # float or int for quantity, int for year, 'yyyy/mm/dd' for date
'unit': str, # for quantity
},
'qualifiers':
{
'<qk>': # qualifier key, one key may have multiple corresponding qualifier values
[
{
'type': 'string'/'quantity'/'date'/'year',
'value': float/int/str,
'unit': str,
}, # the format of qualifier value is similar to attribute value
]
}
},
]
'relations':
[
{
'predicate': str,
'object': '<id>', # NOTE: it may be a concept id
'direction': 'forward'/'backward',
'qualifiers':
{
'<qk>': # qualifier key, one key may have multiple corresponding qualifier values
[
{
'type': 'string'/'quantity'/'date'/'year',
'value': float/int/str,
'unit': str,
}, # the format of qualifier value is similar to attribute value
]
}
},
]
}
}
}
```
### How to run SPARQLs and programs
We implement multiple baselines in our [codebase](https://github.com/shijx12/KQAPro_Baselines), which includes a supervised SPARQL parser and program parser.
In the SPARQL parser, we implement a query engine based on [Virtuoso](https://github.com/openlink/virtuoso-opensource.git).
You can install the engine based on our [instructions](https://github.com/shijx12/KQAPro_Baselines/blob/master/SPARQL/README.md), and then feed your predicted SPARQL to get the answer.
In the program parser, we implement a rule-based program executor, which receives a predicted program and returns the answer.
Detailed introductions of our functions can be found in our [paper](https://arxiv.org/abs/2007.03875).
### How to submit results of test set
You need to predict answers for all questions of test set and write them in a text file **in order**, one per line.
Here is an example:
```
Tron: Legacy
Palm Beach County
1937-03-01
The Queen
...
```
Then you need to send the prediction file to us by email <caosl19@mails.tsinghua.edu.cn>, we will reply to you with the performance as soon as possible.
To appear in the learderboard, you need to also provide following information:
- model name
- affiliation
- open-ended or multiple-choice
- whether use the supervision of SPARQL in your model or not
- whether use the supervision of program in your model or not
- single model or ensemble model
- (optional) paper link
- (optional) code link
### Licensing Information
MIT License
### Citation Information
If you find our dataset is helpful in your work, please cite us by
```
@inproceedings{KQAPro,
title={{KQA P}ro: A Large Diagnostic Dataset for Complex Question Answering over Knowledge Base},
author={Cao, Shulin and Shi, Jiaxin and Pan, Liangming and Nie, Lunyiu and Xiang, Yutong and Hou, Lei and Li, Juanzi and He, Bin and Zhang, Hanwang},
booktitle={ACL'22},
year={2022}
}
```
### Contributions
Thanks to [@happen2me](https://github.com/happen2me) for adding this dataset.
|
mstz/mushroom | ---
language:
- en
tags:
- mushroom
- tabular_classification
- binary_classification
- UCI
pretty_name: Mushroom
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- mushroom
license: cc
---
# Mushroom
The [Mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------|
| mushroom | Binary classification | Is the mushroom poisonous?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/mushroom")["train"]
``` |
xDAN-datasets/SystemChat | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 19887468
num_examples: 7020
download_size: 9880827
dataset_size: 19887468
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This dataset by AbacusAI was crafted by Eric Hartford
This is a synthetic dataset, generated mainly with Mistral-Medium and dolphin-2.7-mixtral-8x7b
The purpose of this dataset is to train the model to respect the System Prompt throughout the entire conversation, no matter how unconventional the system prompt might be.
This dataset is under continued development - my intent is to grow it to 100k conversations.
But, for now, it is good enough to start using.
AbacusAI 的这个数据集由 Eric Hartford 制作
这是一个合成数据集，主要由 Mistral-Medium 和 dolphin-2.7-mixtral-8x7b 生成。
该数据集的目的是训练模型在整个对话过程中都尊重系统提示，无论系统提示多么不合常规。
这个数据集还在继续开发中--我的目标是将其增加到 10 万个对话。
但现在，它已经足够好，可以开始使用了。
|
botbot-ai/physics-ptbr | ---
license: cc-by-nc-4.0
language:
- pt
tags:
- instruction-finetuning
pretty_name: CAMEL Physics PTBR
task_categories:
- text-generation
---
## Tradução do Camel Pyysics dataset para Portuguese (PT-BR) usando NLLB 3.3b.
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
We provide the data in `physics.zip`.
## Data Fields
**The data fields for files in `physics.zip` are as follows:**
* `role_1`: assistant role
* `topic`: physics topic
* `sub_topic`: physics subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/physics", repo_type="dataset", filename="physics.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
--- |
ServiceNow/hotpot_test_pos__0_9 | ---
dataset_info:
features:
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dtype: string
- name: contexts_list
sequence: string
- name: titles_list
sequence: string
- name: useful_contexts
sequence: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: sample_idx
dtype: int64
- name: dataset
dtype: string
splits:
- name: test
num_bytes: 255384789
num_examples: 22215
download_size: 150863371
dataset_size: 255384789
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
gigant/tib_01 | ---
dataset_info:
features:
- name: doi
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: video_url
dtype: string
- name: license
dtype: string
- name: subject
dtype: string
- name: genre
dtype: string
- name: release_year
dtype: string
- name: author
dtype: string
- name: contributors
dtype: string
- name: abstract
dtype: string
- name: transcript
dtype: string
- name: transcript_segments
sequence:
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dtype: int32
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dtype: int32
- name: start
dtype: float32
- name: end
dtype: float32
- name: text
dtype: string
- name: tokens
sequence: int32
- name: temperature
dtype: float32
- name: avg_logprob
dtype: float32
- name: compression_ratio
dtype: float32
- name: no_speech_prob
dtype: float32
- name: keyframes
sequence:
- name: slide
dtype: string
- name: frames
sequence: int32
- name: timestamp
sequence: float32
splits:
- name: train
num_bytes: 1074314815.9313533
num_examples: 9381
download_size: 513790688
dataset_size: 1074314815.9313533
---
# Dataset Card for "tib_01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CohleM/CNN_small | ---
dataset_info:
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 161497336.8
num_examples: 40000
- name: test
num_bytes: 40374334.2
num_examples: 10000
download_size: 128544758
dataset_size: 201871671.0
---
# Dataset Card for "CNN_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wbxlala/Dreamer_Arousal_sub1 | ---
dataset_info:
features:
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sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
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num_examples: 18
download_size: 21683447
dataset_size: 21724848
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
automated-research-group/llama2_7b_chat-boolq-results | ---
dataset_info:
- config_name: '{''do_sample''=False, ''beams''=10}'
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dtype: string
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''top_p''=0.5}'
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---
# Dataset Card for "llama2_7b_chat-boolq-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rzvn/Visual-Stable-Diffusion-pretrained-Datasets | ---
license: afl-3.0
---
|
Mitsuki-Sakamoto/fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.5_seed_1_t_1.0_eval | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
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download_size: 709545794
dataset_size: 1343516779
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
# Dataset Card for "fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.5_seed_1_t_1.0_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Protao/small-0715_shard6_part0000 | ---
dataset_info:
features:
- name: text
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 644785539
num_examples: 20000
- name: validation
num_bytes: 322575921
num_examples: 10000
download_size: 323280269
dataset_size: 967361460
---
# Dataset Card for "small0715_shard6_part0000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quan246/conv_train | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: translation
struct:
- name: en
dtype: string
- name: vi
dtype: string
splits:
- name: train
num_bytes: 303580
num_examples: 1000
- name: dev
num_bytes: 29080
num_examples: 100
download_size: 199263
dataset_size: 332660
---
# Dataset Card for "conv_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hlillemark/flores200_devtest_mt5-3b-flores200-packed | ---
dataset_info:
features:
- name: id
dtype: int32
- name: source_lang
dtype: string
- name: target_lang
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: prediction
dtype: string
- name: chrf_unreduced
dtype: string
splits:
- name: devtest
num_bytes: 364836085
num_examples: 500000
download_size: 258100736
dataset_size: 364836085
---
# Dataset Card for "flores200_devtest_mt5-3b-flores200-packed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eduagarcia/acerola | ---
language:
- pt
--- |
cjvt/rsdo4_en_sl | ---
annotations_creators:
- expert-generated
- found
language:
- en
- sl
language_creators:
- crowdsourced
license:
- cc-by-sa-4.0
multilinguality:
- translation
pretty_name: RSDO4 en-sl parallel corpus
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- parallel data
- rsdo
task_categories:
- translation
- text2text-generation
- text-generation
task_ids: []
---
# Dataset Card for RSDO4 en-sl parallel corpus
### Dataset Summary
The RSDO4 parallel corpus of English-Slovene and Slovene-English translation pairs was collected as part of work package 4 of the Slovene in the Digital Environment project. It contains texts collected from public institutions and texts submitted by individual donors through the text collection portal created within the project. The corpus consists of 964433 translation pairs (extracted from standard translation formats (TMX, XLIFF) or manually aligned) in randomized order which can be used for machine translation training.
### Supported Tasks and Leaderboards
Machine translation.
### Languages
English, Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```
{
'en_seq': 'the total value of its assets exceeds EUR 30000000000;',
'sl_seq': 'skupna vrednost njenih sredstev presega 30000000000 EUR'
}
```
### Data Fields
- `en_seq`: a string containing the English sequence;
- `sl_seq`: a string containing the Slovene sequence.
## Additional Information
### Dataset Curators
Andraž Repar and Iztok Lebar Bajec.
### Licensing Information
CC BY-SA 4.0.
### Citation Information
```
@misc{rsdo4_en_sl,
title = {Parallel corpus {EN}-{SL} {RSDO4} 1.0},
author = {Repar, Andra{\v z} and Lebar Bajec, Iztok},
url = {http://hdl.handle.net/11356/1457},
year = {2021}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_General | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_General
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/1140?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Chinese Mandarin Synthesis Corpus-Female, General. It is recorded by Chinese native speaker. It covers oral sentences, audio books, news, advertising, customer service and movie commentary, and the phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1140?source=Huggingface
### Supported Tasks and Leaderboards
tts: The dataset can be used to train a model for Text to Speech (TTS).
### Languages
Chinese Mandarin
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-0e2388-51771145320 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: Firat/roberta-base-finetuned-squad
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Firat/roberta-base-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@tp](https://huggingface.co/tp) for evaluating this model. |
aisquared/dais-2023 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: Databricks Data and AI Summit 2023 Website Content
---
# DAIS-2023 Dataset
This dataset contains scraped text data from the Databricks Data and AI Summit 2023 (DAIS 2023) [homepage](https://www.databricks.com/dataaisummit/)
as well as text from any public page that is linked in that page or is a two-hop linked page.
We have used this dataset to fine-tune our [DAIS DLite model](https://huggingface.co/aisquared/dlite-dais-2023), along with our dataset of [AI-generated
question-answer pairs](https://huggingface.co/datasets/aisquared/dais-question-answers) generated from this dataset. Feel free to check them out!
|
CyberHarem/aki_shizuha_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of aki_shizuha/秋静葉 (Touhou)
This is the dataset of aki_shizuha/秋静葉 (Touhou), containing 500 images and their tags.
The core tags of this character are `blonde_hair, short_hair, hair_ornament, leaf_hair_ornament, yellow_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 474.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aki_shizuha_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 344.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aki_shizuha_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1041 | 636.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aki_shizuha_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 446.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aki_shizuha_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1041 | 787.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aki_shizuha_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/aki_shizuha_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 | 20 |  |  |  |  |  | 1girl, red_skirt, solo, long_sleeves, looking_at_viewer, bangs, buttons, open_mouth, blush, cowboy_shot, autumn_leaves, maple_leaf, orange_eyes, :d, hair_between_eyes, skirt_hold, collared_shirt |
| 1 | 15 |  |  |  |  |  | 1girl, long_sleeves, solo, buttons, red_skirt, collared_shirt, looking_at_viewer, bangs, closed_mouth, shoes, white_socks, maple_leaf, autumn_leaves, full_body, simple_background, smile, white_background, black_footwear, skirt_hold, blush, leaf_on_head |
| 2 | 6 |  |  |  |  |  | 1girl, maple_leaf, solo, dress, leaf_on_head, blush |
| 3 | 5 |  |  |  |  |  | 2girls, leaf, long_sleeves, smile, bangs, closed_mouth, red_shirt, red_skirt, sisters, solo_focus, blush, hair_between_eyes, looking_at_viewer, red_dress, autumn_leaves, buttons |
| 4 | 7 |  |  |  |  |  | 1girl, black_ribbon, black_skirt, grapes, long_sleeves, mob_cap, neck_ribbon, solo, looking_at_viewer, red_apron, red_headwear, wide_sleeves, yellow_shirt, autumn_leaves, bangs, food-themed_hair_ornament, frills, full_body, hat_ornament, open_mouth, red_eyes, maple_leaf, puffy_sleeves, :d, barefoot, choker, simple_background, white_background |
| 5 | 7 |  |  |  |  |  | 2girls, grapes, long_sleeves, mob_cap, open_mouth, red_apron, red_headwear, dress, sisters, wide_sleeves, :d, black_skirt, leaf, yellow_shirt, red_eyes, solo_focus, barefoot, blush, holding_hands, looking_at_viewer, neck_ribbon, puffy_sleeves |
| 6 | 16 |  |  |  |  |  | 1girl, grapes, hat, solo, red_eyes, smile, open_mouth, leaf, dress |
| 7 | 7 |  |  |  |  |  | 2girls, sisters, open_mouth, leaf_on_head, grapes, hat |
| 8 | 7 |  |  |  |  |  | 1girl, blush, cowboy_shot, frilled_shirt_collar, looking_at_viewer, marker_(medium), open_mouth, sample_watermark, solo, :d, autumn_leaves, hair_ribbon, leaf_print, medium_hair, long_sleeves, maple_leaf, puffy_sleeves, red_eyes, red_ribbon, red_skirt, bangs, fang, skirt_hold, black_ribbon, bowtie, buttons, embellished_costume, frilled_skirt, frilled_sleeves, hair_between_eyes, orange_background, orange_theme, print_skirt, red_dress |
| 9 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, solo_focus, open_mouth, penis, pussy, navel, sex, vaginal, censored, leaf, medium_breasts, tears, nude, small_breasts, lying, one_eye_closed, pubic_hair, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | red_skirt | solo | long_sleeves | looking_at_viewer | bangs | buttons | open_mouth | blush | cowboy_shot | autumn_leaves | maple_leaf | orange_eyes | :d | hair_between_eyes | skirt_hold | collared_shirt | closed_mouth | shoes | white_socks | full_body | simple_background | smile | white_background | black_footwear | leaf_on_head | dress | 2girls | leaf | red_shirt | sisters | solo_focus | red_dress | black_ribbon | black_skirt | grapes | mob_cap | neck_ribbon | red_apron | red_headwear | wide_sleeves | yellow_shirt | food-themed_hair_ornament | frills | hat_ornament | red_eyes | puffy_sleeves | barefoot | choker | holding_hands | hat | frilled_shirt_collar | marker_(medium) | sample_watermark | hair_ribbon | leaf_print | medium_hair | red_ribbon | fang | bowtie | embellished_costume | frilled_skirt | frilled_sleeves | orange_background | orange_theme | print_skirt | 1boy | hetero | nipples | penis | pussy | navel | sex | vaginal | censored | medium_breasts | tears | nude | small_breasts | lying | one_eye_closed | pubic_hair | spread_legs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------|:---------------|:--------------------|:--------|:----------|:-------------|:--------|:--------------|:----------------|:-------------|:--------------|:-----|:--------------------|:-------------|:-----------------|:---------------|:--------|:--------------|:------------|:--------------------|:--------|:-------------------|:-----------------|:---------------|:--------|:---------|:-------|:------------|:----------|:-------------|:------------|:---------------|:--------------|:---------|:----------|:--------------|:------------|:---------------|:---------------|:---------------|:----------------------------|:---------|:---------------|:-----------|:----------------|:-----------|:---------|:----------------|:------|:-----------------------|:------------------|:-------------------|:--------------|:-------------|:--------------|:-------------|:-------|:---------|:----------------------|:----------------|:------------------|:--------------------|:---------------|:--------------|:-------|:---------|:----------|:--------|:--------|:--------|:------|:----------|:-----------|:-----------------|:--------|:-------|:----------------|:--------|:-----------------|:-------------|:--------------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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|
nicoy/zhizunbao_large | ---
license: cc
---
|
jinwoos/cartoonizer-dataset-new | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 2523475002.0
num_examples: 180
download_size: 2523246746
dataset_size: 2523475002.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
polsum | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Polish Summaries Corpus
dataset_info:
features:
- name: id
dtype: string
- name: date
dtype: string
- name: title
dtype: string
- name: section
dtype: string
- name: authors
dtype: string
- name: body
dtype: string
- name: summaries
sequence:
- name: ratio
dtype: int32
- name: type
dtype: string
- name: author
dtype: string
- name: body
dtype: string
- name: spans
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: span_text
dtype: string
splits:
- name: train
num_bytes: 34787575
num_examples: 569
download_size: 6082812
dataset_size: 34787575
---
# Dataset Card for Polish Summaries Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Repository:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Paper:** http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mateusz Kopeć](http://zil.ipipan.waw.pl/MateuszKopec)
### Dataset Summary
The Corpus contains a large number of manual summaries of news articles,
with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Polish
## Dataset Structure
### Data Instances
See below an example from the dataset. Detailed descriptions of the fields are provided in the following section.
```
{'authors': 'Krystyna Forowicz',
'body': "ROZMOWA\n\nProf. Krzysztof Ernst, kierownik Zakładu Optyki Instytutu Fizyki Doświadczalnej Uniwersytetu Warszawskiego\n\nLidarowe oczy\n\nRYS. MAREK KONECKI\n\nJutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.\n\nCzy to kosztowne urządzenie będzie służyło tylko naukowcom?\n\nTego typu lidar jest rzeczywiście drogi, kosztuje około miliona marek niemieckich. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Nad lidarem pracują specjaliści od laserów i od komputerów. Współpracujemy z doskonałym laboratorium prof. Ludgera Wöste z Freie Universitat Berlin rozwijającym m.in. problematykę lidarową. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią lepiej i dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. \n\nBadania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Ale np. obecnie prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen. Tym szkodliwym gazem może być skażone powietrze w miastach, w których zlokalizowane są zakłady chemiczne, np. w Bydgoszczy pewne ilości fosgenu emitują Zakłady Chemiczne Organika- Zachem. \n\nLidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie. Możemy np. badać zawartość ozonu w troposferze. Okazuje się bowiem, że o ile brak tego gazu w wysokich warstwach atmosfery powoduje groźny efekt cieplarniany, to jego nadmiar tuż nad Ziemią jest szkodliwy. Groźne są też substancje gazowe, jak np. tlenki azotu, będące następstwem spalin samochodowych. A samochodów przybywa.\n\nCzy stać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nKoszt jednego dnia kampanii pomiarowej firmy zachodnie szacują na kilka tysięcy DM. Potrzebne są pieniądze na utrzymanie lidaru, na prowadzenie badań. Nasze przedsięwzięcie nie ma charakteru komercyjnego. Koszt pomiarów będzie znacznie niższy. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Chcielibyśmy rozwinąć tutaj współpracę z państwowymi i wojewódzkimi służbami ochrony środowiska. Tego typu badania były prowadzone np. w Lyonie. Okazało się, że najwięcej tlenków azotu występuje niekoniecznie tam gdzie są one produkowane, to znaczy nie przy najruchliwszych ulicach, jeśli są one dobrze wentylowane a gromadzą się one w małych uliczkach. Przede wszystkim jednak do końca tego roku zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu trzech granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. Prowadziliśmy pomiary w samym Turowie, gdzie elektrownia Turoszowska jest głównym źródłem emisji. W planie mamy Bogatynię, zagłębie miedziowe. \n\nW Czarnym Trójkącie istnieje wiele stacjonarnych stacji monitoringowych.\n\nNasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych. \n\nJak wypadł Czarny Trójkąt?\n\nKiedy występowaliśmy o finansowanie tego projektu do Fundacji Współpracy Polsko-Niemieckiej zanieczyszczenie powietrza w Czarnym Trójkącie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać. Obecnie stężenie dwutlenku siarki jest na granicy naszych możliwości pomiarowych. Dla regionu Turoszowskiego to dobra wiadomość i dla stosunków polsko-niemieckich też.\n\nTypów lidarów jest wiele \n\nTen lidar pracuje w obszarze bliskiego nadfioletu i promieniowania widzialnego, które jest wynikiem wykorzystania drugiej lub trzeciej harmonicznej lasera szafirowego, pracującego na granicy czerwieni i podczerwieni. DIAL jest tym typem lidara, który dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Stanach Zjednoczonych lidary umieszcza się na satelitach (program NASA). Określają na przestrzeni kilkudziesięciu kilometrów rozkłady temperatury, wilgotności, ciśnienia, a także prędkości wiatru. Wykrywają pojawianie się huraganów, a nawet mogą określać rozmiary oka tajfunu.\n\nIle takich urządzeń jest w Europie?\n\n- W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Wykrywanie toluenu i benzenu jest oryginalnym rozwiązaniem. Długość fali dla benzenu jest już na skraju możliwości widmowych. Nasz lidar typu DIAL jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. Ale historia lidarów w naszym kraju jest dłuższa i zaczęła się na początku lat 60. Pierwsze próby prowadzone były w stacji geofizycznej PAN w Belsku, niedługo po skonstruowaniu pierwszego w świecie lasera rubinowego. Potem powstał lidar stacjonarny, również typu DIAL, w Gdańsku, a w Krakowie sodary - urządzenia oparte na falach akustycznych, wygodne np. do pomiarów szybkości wiatru. Lidar umieszczony na samochodzie i zbudowany w latach 80 na Politechnice Poznańskiej w perspektywie miał być lidarem typu DIAL.\n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji (zdjęć satelitarnych) Instytutu Geofizyki i, co bardzo ważne, współpraca z Freie Universität Berlin. Mamy również na UW Międzywydziałowe Studia Ochrony Środowiska i studentom przekazujemy informacje o lidarze i fizycznych metodach badania środowiska. Nasze działania dydaktyczne bardzo efektywnie wspiera NFOŚ.\n\nRozmawiała Krystyna Forowicz",
'date': '1997-04-21',
'id': '199704210011',
'section': 'Nauka i Technika',
'summaries': {'author': ['I',
'I',
'I',
'C',
'C',
'C',
'K',
'K',
'K',
'G',
'G',
'G',
'J',
'J',
'J'],
'body': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Możemy np. badać zawartość ozonu w troposferze. W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Fizycy dotychczas nie zajmowali się ochroną środowiska?Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną. Żeby przetworzyć sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych. Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Tego typu lidar jest drogi, kosztuje około miliona marek niemieckich. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową i dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\n\nto kosztowne urządzenie będzie służyło tylko naukowcom?\n\nlidar jest rzeczywiście drogi. to najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze. Ale prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.\n\nstać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. zanieczyszczenie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.\nDIAL dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.',
'Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie na inne substancje występujące w atmosferze. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
"Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. \n\nChcemy mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. \n\nDIAL jest tym typem lidara, który dzisiaj ma największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Europie takich lidarów jak nasz jest zaledwie kilka. Nasz lidar jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.",
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
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'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.'],
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'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?',
'Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie.',
'Możemy np. badać zawartość ozonu w troposferze.',
'W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu.',
'',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
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'',
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'lidar',
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'dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.',
'',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
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'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.'],
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{'end': [246, 396, 480, 542, 1021, 1102, 2920, 4989],
'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Tego typu lidar jest',
'drogi, kosztuje około miliona marek niemieckich.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.'],
'start': [153, 247, 459, 493, 590, 1022, 2602, 4555]},
{'end': [246, 360, 626, 883, 920, 1102],
'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową',
'i',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
'start': [153, 247, 625, 760, 919, 1032]},
{'end': [158,
262,
271,
359,
397,
590,
761,
803,
867,
907,
922,
1025,
1102,
3311,
3516,
3595,
3623,
3675,
4226,
4332],
'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu',
'.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.'],
'start': [153,
172,
263,
279,
396,
548,
699,
769,
806,
875,
911,
1022,
1033,
3310,
3462,
3556,
3596,
3674,
4158,
4233]},
{'end': [158,
262,
271,
359,
398,
459,
498,
543,
590,
761,
803,
867,
922,
1025,
1102,
2242,
2300,
2406,
3247,
3311,
3516,
3595,
3675,
4226,
4333,
5130,
5241,
5439,
5661,
5756,
7113],
'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to kosztowne urządzenie będzie służyło tylko naukowcom?',
'lidar jest rzeczywiście drogi',
'.',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze',
'. Ale',
'prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.',
'',
'stać nas będzie na prowadzenie pomiarów ozonu w miastach?',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'zanieczyszczenie',
'było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.',
'',
'DIAL',
'dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.'],
'start': [153,
172,
263,
279,
396,
402,
469,
541,
548,
699,
769,
806,
875,
1022,
1033,
2062,
2294,
2312,
3245,
3251,
3462,
3556,
3596,
4158,
4233,
5114,
5160,
5438,
5656,
5690,
6990]},
{'end': [262, 271, 359, 397, 590, 761, 803, 807, 867, 907, 922, 1025, 1102],
'span_text': ['Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'',
'wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
'start': [227,
263,
279,
396,
548,
699,
769,
806,
824,
875,
911,
1022,
1033]},
{'end': [245,
360,
761,
936,
971,
1022,
1733,
1878,
4159,
4614,
4772,
4818,
4860,
4906,
7283,
7326,
7383],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie',
'na inne substancje występujące w atmosferze.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.',
'Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
'start': [227,
246,
699,
924,
942,
977,
1631,
1876,
4076,
4555,
4765,
4778,
4823,
4904,
7114,
7305,
7344]},
{'end': [245,
360,
625,
761,
936,
1022,
1311,
1357,
1436,
1733,
1878,
3247,
3311,
3563,
3676,
4159,
4614,
4772,
4818,
4906,
5410,
5439,
5701,
5789,
6163,
6364,
6472,
7048,
7283,
7326,
7383],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej',
'potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
"Pakiet software'u",
'wzbogacamy o nowe algorytmy, które potrafią',
'dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'',
'',
'Chcemy',
'mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi.',
'',
'',
'DIAL jest tym typem lidara, który dzisiaj ma',
'największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia.',
'W Europie takich lidarów jak nasz jest zaledwie kilka.',
'Nasz lidar',
'jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
'start': [227,
246,
591,
668,
924,
942,
1293,
1313,
1366,
1631,
1876,
3246,
3310,
3556,
3567,
4076,
4555,
4765,
4778,
4823,
5409,
5438,
5656,
5714,
6108,
6353,
6374,
6990,
7049,
7305,
7344]},
{'end': [245, 271, 360, 761, 4159, 4614, 4772, 4818, 4860, 4905],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST:',
'to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.'],
'start': [227, 246, 276, 699, 4076, 4555, 4765, 4778, 4823, 4904]}],
'type': ['extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract']},
'title': 'Lidarowe oczy'}
```
### Data Fields
- `id`: a `string` example identifier
- `date`: date of the original article (`string`)
- `title`: title of the original article (`string`)
- `section`: the section of the newspaper the original article belonged to (`string`)
- `authors`: original article authors (`string`)
- `body`: original article body (list of `string`s)
- `summaries`: a dictionary feature containing summaries of the original article with the following attributes:
- `ratio`: ratio of summary - percentage of the original article (list of `int32`s)
- `type`: type of summary - extractive (`extract`) or abstractive (`abstract`) (list of `string`s)
- `author`: acronym of summary author (list of `string`)
- `body`: body of summary (list of `string`)
- `spans`: a list containing spans for extractive summaries (empty for abstractive summaries):
- `start`: start of span (`int32`)
- `end`: end of span (`int32`)
- `span_text`: span text (`string`)
### Data Splits
Single train split
## 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
```
@inproceedings{
ogro:kop:14:lrec,
author = "Ogrodniczuk, Maciej and Kopeć, Mateusz",
pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf",
title = "The {P}olish {S}ummaries {C}orpus",
pages = "3712--3715",
crossref = "lrec:14"
}
@proceedings{
lrec:14,
editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios",
isbn = "978-2-9517408-8-4",
title = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html",
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
address = "Reykjavík, Iceland",
key = "LREC",
year = "2014",
organization = "European Language Resources Association (ELRA)"
}
```
### Contributions
Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset. |
liuyanchen1015/MULTI_VALUE_qqp_transitive_suffix | ---
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: 2842170
num_examples: 16844
- name: test
num_bytes: 28411698
num_examples: 167470
- name: train
num_bytes: 25420238
num_examples: 150324
download_size: 35393943
dataset_size: 56674106
---
# Dataset Card for "MULTI_VALUE_qqp_transitive_suffix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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