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Lifan-Z/tox-antitox-proteins
|
Lifan-Z
| 2023-11-13T09:00:37Z | 82 | 0 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"biology",
"medical",
"region:us"
] |
[
"text-generation"
] | 2023-11-13T02:52:31Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- biology
- medical
pretty_name: tox-antitox-proteins
size_categories:
- n<1K
---
This dataset is used for finetuning protGPT2. The features are ['attention_mask', 'input_ids'], no 'labels'.
After using DataCollatorForLanguageModeling and DataLoader, the features will be ['attention_mask', 'input_ids', 'labels'].
|
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Cohere/miracl-fr-corpus-22-12
|
Cohere
| 2023-02-06T11:57:34Z | 81 | 0 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:fr",
"license:apache-2.0",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-31T06:02:06Z |
---
annotations_creators:
- expert-generated
language:
- fr
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (fr) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-fr-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-fr-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## 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/).
## Loading the dataset
In [miracl-fr-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-fr-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fr-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-fr-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-fr-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# 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['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
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] |
medalpaca/medical_meadow_health_advice
|
medalpaca
| 2023-04-06T16:51:22Z | 81 | 3 |
[
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"question-answering",
"text-classification"
] | 2023-04-06T16:47:45Z |
---
task_categories:
- question-answering
- text-classification
language:
- en
---
# Health Advice
## Dataset Description
- **Paper:** https://experts.syr.edu/en/publications/detecting-causal-language-use-in-science-findings
### Dataset Summary
This is the dataset use in the paper: Detecting Causal Language Use in Science Findings.
It was cleaned and formated to fit into the alpaca template.
### Citation Information
```
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1473",
doi = "10.18653/v1/D19-1473",
pages = "4664--4674",
}
```
|
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] |
hpprc/jsick
|
hpprc
| 2023-04-11T06:18:09Z | 81 | 4 |
[
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|sick",
"language:ja",
"language:en",
"license:cc-by-sa-4.0",
"semantic-textual-similarity",
"sts",
"region:us"
] |
[
"sentence-similarity",
"text-classification"
] | 2023-04-08T16:02:06Z |
---
annotations_creators:
- expert-generated
language:
- ja
- en
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- translation
pretty_name: JSICK
size_categories:
- 10K<n<100K
source_datasets:
- extended|sick
tags:
- semantic-textual-similarity
- sts
task_categories:
- sentence-similarity
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
---
# Dataset Card for JSICK
## Table of Contents
- [Dataset Card for JSICK](#dataset-card-for-jsick)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.](#japanese-sentences-involving-compositional-knowledge-jsick-dataset)
- [JSICK-stress Test set](#jsick-stress-test-set)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [base](#base)
- [stress](#stress)
- [Data Fields](#data-fields)
- [base](#base-1)
- [stress](#stress-1)
- [Data Splits](#data-splits)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/verypluming/JSICK
- **Repository:** https://github.com/verypluming/JSICK
- **Paper:** https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00518/113850/Compositional-Evaluation-on-Japanese-Textual
- **Paper:** https://www.jstage.jst.go.jp/article/pjsai/JSAI2021/0/JSAI2021_4J3GS6f02/_pdf/-char/ja
### Dataset Summary
From official [GitHub](https://github.com/verypluming/JSICK):
#### Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.
JSICK is the Japanese NLI and STS dataset by manually translating the English dataset [SICK (Marelli et al., 2014)](https://aclanthology.org/L14-1314/) into Japanese.
We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference.
#### JSICK-stress Test set
The JSICK-stress test set is a dataset to investigate whether models capture word order and case particles in Japanese.
The JSICK-stress test set is provided by transforming syntactic structures of sentence pairs in JSICK, where we analyze whether models are attentive to word order and case particles to predict entailment labels and similarity scores.
The JSICK test set contains 1666, 797, and 1006 sentence pairs (A, B) whose premise sentences A (the column `sentence_A_Ja_origin`) include the basic word order involving
ga-o (nominative-accusative), ga-ni (nominative-dative), and ga-de (nominative-instrumental/locative) relations, respectively.
We provide the JSICK-stress test set by transforming syntactic structures of these pairs by the following three ways:
- `scrum_ga_o`: a scrambled pair, where the word order of premise sentences A is scrambled into o-ga, ni-ga, and de-ga order, respectively.
- `ex_ga_o`: a rephrased pair, where the only case particles (ga, o, ni, de) in the premise A are swapped
- `del_ga_o`: a rephrased pair, where the only case particles (ga, o, ni) in the premise A are deleted
### Languages
The language data in JSICK is in Japanese and English.
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
import datasets as ds
dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
# num_rows: 4500
# })
# test: Dataset({
# features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
# num_rows: 4927
# })
# })
dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick", name="stress")
print(dataset)
# DatasetDict({
# test: Dataset({
# features: ['id', 'premise', 'hypothesis', 'label', 'score', 'sentence_A_Ja_origin', 'entailment_label_origin', 'relatedness_score_Ja_origin', 'rephrase_type', 'case_particles'],
# num_rows: 900
# })
# })
```
#### base
An example of looks as follows:
```json
{
'id': 1,
'premise': '子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている',
'hypothesis': '庭にいる男の子たちのグループが遊んでいて、男性が後ろの方に立っている',
'label': 1, // (neutral)
'score': 3.700000047683716,
'premise_en': 'A group of kids is playing in a yard and an old man is standing in the background',
'hypothesis_en': 'A group of boys in a yard is playing and a man is standing in the background',
'label_en': 1, // (neutral)
'score_en': 4.5,
'corr_entailment_labelAB_En': 'nan',
'corr_entailment_labelBA_En': 'nan',
'image_ID': '3155657768_b83a7831e5.jpg',
'original_caption': 'A group of children playing in a yard , a man in the background .',
'semtag_short': 'nan',
'semtag_long': 'nan',
}
```
#### stress
An example of looks as follows:
```json
{
'id': '5818_de_d',
'premise': '女性火の近くダンスをしている',
'hypothesis': '火の近くでダンスをしている女性は一人もいない',
'label': 2, // (contradiction)
'score': 4.0,
'sentence_A_Ja_origin': '女性が火の近くでダンスをしている',
'entailment_label_origin': 2,
'relatedness_score_Ja_origin': 3.700000047683716,
'rephrase_type': 'd',
'case_particles': 'de'
}
```
### Data Fields
#### base
A version adopting the column names of a typical NLI dataset.
| Name | Description |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| id | The ids (the same with original SICK). |
| premise | The first sentence in Japanese. |
| hypothesis | The second sentence in Japanese. |
| label | The entailment label in Japanese. |
| score | The relatedness score in the range [1-5] in Japanese. |
| premise_en | The first sentence in English. |
| hypothesis_en | The second sentence in English. |
| label_en | The original entailment label in English. |
| score_en | The original relatedness score in the range [1-5] in English. |
| semtag_short | The linguistic phenomena tags in Japanese. |
| semtag_long | The details of linguistic phenomena tags in Japanese. |
| image_ID | The original image in [8K ImageFlickr dataset](https://www.kaggle.com/datasets/adityajn105/flickr8k). |
| original_caption | The original caption in [8K ImageFlickr dataset](https://www.kaggle.com/datasets/adityajn105/flickr8k). |
| corr_entailment_labelAB_En | The corrected entailment label from A to B in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf). |
| corr_entailment_labelBA_En | The corrected entailment label from B to A in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf). |
#### stress
| Name | Description |
| --------------------------- | ------------------------------------------------------------------------------------------------- |
| id | Ids (the same with original SICK). |
| premise | The first sentence in Japanese. |
| hypothesis | The second sentence in Japanese. |
| label | The entailment label in Japanese |
| score | The relatedness score in the range [1-5] in Japanese. |
| sentence_A_Ja_origin | The original premise sentences A from the JSICK test set. |
| entailment_label_origin | The original entailment labels. |
| relatedness_score_Ja_origin | The original relatedness scores. |
| rephrase_type | The type of transformation applied to the syntactic structures of the sentence pairs. |
| case_particles | The grammatical particles in Japanese that indicate the function or role of a noun in a sentence. |
### Data Splits
| name | train | validation | test |
| --------------- | ----: | ---------: | ----: |
| base | 4,500 | | 4,927 |
| original | 4,500 | | 4,927 |
| stress | | | 900 |
| stress-original | | | 900 |
### Annotations
To annotate the JSICK dataset, they used the crowdsourcing platform "Lancers" to re-annotate entailment labels and similarity scores for JSICK.
They had six native Japanese speakers as annotators, who were randomly selected from the platform.
The annotators were asked to fully understand the guidelines and provide the same labels as gold labels for ten test questions.
For entailment labels, they adopted annotations that were agreed upon by a majority vote as gold labels and checked whether the majority judgment vote was semantically valid for each example.
For similarity scores, they used the average of the annotation results as gold scores.
The raw annotations with the JSICK dataset are [publicly available](https://github.com/verypluming/JSICK/blob/main/jsick/jsick-all-annotations.tsv).
The average annotation time was 1 minute per pair, and Krippendorff's alpha for the entailment labels was 0.65.
## Additional Information
- [verypluming/JSICK](https://github.com/verypluming/JSICK)
- [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00518/113850/Compositional-Evaluation-on-Japanese-Textual)
- [JSICK: 日本語構成的推論・類似度データセットの構築](https://www.jstage.jst.go.jp/article/pjsai/JSAI2021/0/JSAI2021_4J3GS6f02/_article/-char/ja)
### Licensing Information
CC BY-SA 4.0
### Citation Information
```bibtex
@article{yanaka-mineshima-2022-compositional,
title = "Compositional Evaluation on {J}apanese Textual Entailment and Similarity",
author = "Yanaka, Hitomi and
Mineshima, Koji",
journal = "Transactions of the Association for Computational Linguistics",
volume = "10",
year = "2022",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2022.tacl-1.73",
doi = "10.1162/tacl_a_00518",
pages = "1266--1284",
}
@article{谷中 瞳2021,
title={JSICK: 日本語構成的推論・類似度データセットの構築},
author={谷中 瞳 and 峯島 宏次},
journal={人工知能学会全国大会論文集},
volume={JSAI2021},
number={ },
pages={4J3GS6f02-4J3GS6f02},
year={2021},
doi={10.11517/pjsai.JSAI2021.0_4J3GS6f02}
}
```
### Contributions
Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and [Koji Mineshima](https://abelard.flet.keio.ac.jp/person/minesima/index-j.html) for creating this dataset.
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] |
mstz/page_blocks
|
mstz
| 2023-04-16T17:57:31Z | 81 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"page_blocks",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-13T09:52:40Z |
---
language:
- en
tags:
- page_blocks
- tabular_classification
- binary_classification
- multiclass_classification
pretty_name: Page Blocks
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- page_blocks
- page_blocks_binary
license: cc
---
# PageBlocks
The [PageBlocks dataset](https://archive-beta.ics.uci.edu/dataset/76/page_blocks) from the [UCI repository](https://archive-beta.ics.uci.edu/).
How many transitions does the page block have?
# Configurations and tasks
| **Configuration** | **Task** |
|-------------------|---------------------------|
| page_blocks | Multiclass classification |
| page_blocks_binary| Binary classification |
|
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] |
atasoglu/databricks-dolly-15k-tr
|
atasoglu
| 2023-05-01T10:30:39Z | 81 | 7 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"question-answering"
] | 2023-05-01T10:22:31Z |
---
license: cc-by-sa-3.0
task_categories:
- question-answering
language:
- tr
pretty_name: databricks-dolly-15k-tr
size_categories:
- 10K<n<100K
---
This dataset is machine-translated version of [databricks-dolly-15k.jsonl](https://github.com/databrickslabs/dolly/tree/master/data) into Turkish.
Used `googletrans==3.1.0a0` to translation.
|
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] |
VMware/open-instruct-v1-oasst-dolly-hhrlhf
|
VMware
| 2023-07-13T14:21:14Z | 81 | 15 |
[
"language:en",
"region:us"
] | null | 2023-05-10T23:36:12Z |
---
language: en
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: alpaca_prompt
dtype: string
- name: response
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 60252132
num_examples: 62971
download_size: 33232110
dataset_size: 60252132
---
# Dataset Card for "open-instruct-v1-oasst-dolly-hhrlhf"
This dataset is a combination of:
1. Filtered subset of[OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
2. train split of [Mosaic-dolly-hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) (consists of [Databrick's dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset and a filtered subset of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)).
## Dataset
The dataset consists of 3 columns:
1. instruction: The natural language instruction without any prompt templates (we extracted them out of the alpaca-format in Mosaic-dolly-hhrlhf)
2. alpaca_prompt: Alpaca prompt template versions of instruction
3. response: The response to the instruction
## License
- It is usable for commercial purposes so long as you follow the terms of the license.
- Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:
- Wikipedia (various pages) - https://www.wikipedia.org/
- Copyright © Wikipedia editors and contributors.
- Databricks (https://www.databricks.com)
- Copyright © Databricks
- Mosaic ML (https://www.mosaicml.com/)
- Copyright © Mosaic ML
- VMware
- Copyright © VMware
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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edarchimbaud/news-stocks
|
edarchimbaud
| 2023-11-21T05:06:42Z | 81 | 3 |
[
"region:us"
] | null | 2023-05-17T17:23:09Z |
---
dataset_info:
features:
- name: symbol
dtype: string
- name: body
dtype: string
- name: publisher
dtype: string
- name: publish_time
dtype: timestamp[ns, tz=GMT]
- name: title
dtype: string
- name: url
dtype: string
- name: uuid
dtype: string
splits:
- name: train
num_bytes: 112563283
num_examples: 22025
download_size: 55028670
dataset_size: 112563283
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "news-sp500"
## 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://edarchimbaud.substack.com
- **Repository:** https://github.com/edarchimbaud
- **Point of Contact:** [email protected]
### Dataset Summary
The news-sp500 dataset provides news articles related to companies in the S&P 500 index.
### Supported Tasks and Leaderboards
The dataset can be used for various natural language processing tasks such as text classification, sentiment analysis, information extraction, etc. It does not have a specific leaderboard associated with it.
### Languages
The dataset contains news articles in multiple languages.
## Dataset Structure
### Data Instances
The dataset consists of [1563] data instances.
### Data Fields
- symbol (string): A string representing the ticker symbol or abbreviation used to identify the company.
- body (string): The main content of the news article.
- publisher (string): The name of the publisher or news agency.
- publish_time (timestamp[ns, tz=GMT]): A timestamp indicating the publication time of the news article in GMT timezone.
- title (string): The title or headline of the news article.
- url (string): The URL or link to the original news article.
- uuid (string): A unique identifier for the news article.
### Data Splits
The dataset consists of a single split called train.
## Dataset Creation
### Curation Rationale
The news-sp500 dataset was created to provide a collection of news articles related to companies in the S&P 500 index for research and analysis purposes.
### Source Data
#### Initial Data Collection and Normalization
The data was collected from various online news sources and normalized for consistency.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The news-sp500 dataset was collected by https://edarchimbaud.substack.com.
### Licensing Information
The news-sp500 dataset is licensed under the MIT License.
### Citation Information
> https://edarchimbaud.substack.com, news-sp500 dataset, GitHub repository, https://github.com/edarchimbaud
### Contributions
Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset.
|
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] |
Tverous/claim-amr
|
Tverous
| 2023-07-28T00:08:45Z | 81 | 0 |
[
"region:us"
] | null | 2023-07-21T09:16:55Z |
---
dataset_info:
features:
- name: uid
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
- name: claim_cleaned_amr
dtype: string
splits:
- name: train
num_bytes: 60227369
num_examples: 100459
- name: dev
num_bytes: 853786
num_examples: 1200
- name: test
num_bytes: 846997
num_examples: 1200
download_size: 21047805
dataset_size: 61928152
---
# Dataset Card for "claim-amr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_chargoddard__llama2-22b-blocktriangular
|
open-llm-leaderboard
| 2023-10-18T10:03:01Z | 81 | 0 |
[
"region:us"
] | null | 2023-08-18T18:57:28Z |
---
pretty_name: Evaluation run of chargoddard/llama2-22b-blocktriangular
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/llama2-22b-blocktriangular](https://huggingface.co/chargoddard/llama2-22b-blocktriangular)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__llama2-22b-blocktriangular\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T10:02:48.850156](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama2-22b-blocktriangular/blob/main/results_2023-10-18T10-02-48.850156.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002202181208053691,\n\
\ \"em_stderr\": 0.000480051081661935,\n \"f1\": 0.06165897651006692,\n\
\ \"f1_stderr\": 0.0013848407345463738,\n \"acc\": 0.4357400460634537,\n\
\ \"acc_stderr\": 0.010354651175233286\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.000480051081661935,\n\
\ \"f1\": 0.06165897651006692,\n \"f1_stderr\": 0.0013848407345463738\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11220621683093253,\n \
\ \"acc_stderr\": 0.008693743138242383\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224186\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/llama2-22b-blocktriangular
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|arc:challenge|25_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T05_53_32.762527
path:
- '**/details_harness|drop|3_2023-10-18T05-53-32.762527.parquet'
- split: 2023_10_18T10_02_48.850156
path:
- '**/details_harness|drop|3_2023-10-18T10-02-48.850156.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T10-02-48.850156.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T05_53_32.762527
path:
- '**/details_harness|gsm8k|5_2023-10-18T05-53-32.762527.parquet'
- split: 2023_10_18T10_02_48.850156
path:
- '**/details_harness|gsm8k|5_2023-10-18T10-02-48.850156.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T10-02-48.850156.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hellaswag|10_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T16:15:19.075132.parquet'
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- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T16:15:19.075132.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T16:15:19.075132.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T16:15:19.075132.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T16:15:19.075132.parquet'
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- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T16:15:19.075132.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
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- split: latest
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- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T16:15:19.075132.parquet'
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path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T16:15:19.075132.parquet'
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
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- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T16:15:19.075132.parquet'
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T16:15:19.075132.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T16:15:19.075132.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
- '**/details_harness|winogrande|5_2023-10-18T05-53-32.762527.parquet'
- split: 2023_10_18T10_02_48.850156
path:
- '**/details_harness|winogrande|5_2023-10-18T10-02-48.850156.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T10-02-48.850156.parquet'
- config_name: results
data_files:
- split: 2023_08_17T16_15_19.075132
path:
- results_2023-08-17T16:15:19.075132.parquet
- split: 2023_10_18T05_53_32.762527
path:
- results_2023-10-18T05-53-32.762527.parquet
- split: 2023_10_18T10_02_48.850156
path:
- results_2023-10-18T10-02-48.850156.parquet
- split: latest
path:
- results_2023-10-18T10-02-48.850156.parquet
---
# Dataset Card for Evaluation run of chargoddard/llama2-22b-blocktriangular
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama2-22b-blocktriangular
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/llama2-22b-blocktriangular](https://huggingface.co/chargoddard/llama2-22b-blocktriangular) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__llama2-22b-blocktriangular",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T10:02:48.850156](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama2-22b-blocktriangular/blob/main/results_2023-10-18T10-02-48.850156.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002202181208053691,
"em_stderr": 0.000480051081661935,
"f1": 0.06165897651006692,
"f1_stderr": 0.0013848407345463738,
"acc": 0.4357400460634537,
"acc_stderr": 0.010354651175233286
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.000480051081661935,
"f1": 0.06165897651006692,
"f1_stderr": 0.0013848407345463738
},
"harness|gsm8k|5": {
"acc": 0.11220621683093253,
"acc_stderr": 0.008693743138242383
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.012015559212224186
}
}
```
### 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]
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] |
silk-road/Chat-Haruhi-Fusion-A_B
|
silk-road
| 2023-08-24T16:47:29Z | 81 | 3 |
[
"region:us"
] | null | 2023-08-24T16:46:38Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: context
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 259951538
num_examples: 66519
download_size: 0
dataset_size: 259951538
---
# Dataset Card for "Chat-Haruhi-Fusion-A_B"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k
|
open-llm-leaderboard
| 2023-09-23T08:31:09Z | 81 | 0 |
[
"region:us"
] | null | 2023-09-17T03:22:01Z |
---
pretty_name: Evaluation run of conceptofmind/LLongMA-2-13b-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [conceptofmind/LLongMA-2-13b-16k](https://huggingface.co/conceptofmind/LLongMA-2-13b-16k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T08:30:56.994435](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k/blob/main/results_2023-09-23T08-30-56.994435.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002202181208053691,\n\
\ \"em_stderr\": 0.0004800510816619487,\n \"f1\": 0.05451552013422845,\n\
\ \"f1_stderr\": 0.001321043219231616,\n \"acc\": 0.39035575610663886,\n\
\ \"acc_stderr\": 0.009395368385266412\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.0004800510816619487,\n\
\ \"f1\": 0.05451552013422845,\n \"f1_stderr\": 0.001321043219231616\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05458680818802123,\n \
\ \"acc_stderr\": 0.006257444037912527\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620296\n\
\ }\n}\n```"
repo_url: https://huggingface.co/conceptofmind/LLongMA-2-13b-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|drop|3_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|drop|3_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T08-30-56.994435.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|gsm8k|5_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-30-56.994435.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- '**/details_harness|winogrande|5_2023-09-17T03-21-57.837796.parquet'
- split: 2023_09_23T08_30_56.994435
path:
- '**/details_harness|winogrande|5_2023-09-23T08-30-56.994435.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T08-30-56.994435.parquet'
- config_name: results
data_files:
- split: 2023_09_17T03_21_57.837796
path:
- results_2023-09-17T03-21-57.837796.parquet
- split: 2023_09_23T08_30_56.994435
path:
- results_2023-09-23T08-30-56.994435.parquet
- split: latest
path:
- results_2023-09-23T08-30-56.994435.parquet
---
# Dataset Card for Evaluation run of conceptofmind/LLongMA-2-13b-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/conceptofmind/LLongMA-2-13b-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [conceptofmind/LLongMA-2-13b-16k](https://huggingface.co/conceptofmind/LLongMA-2-13b-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T08:30:56.994435](https://huggingface.co/datasets/open-llm-leaderboard/details_conceptofmind__LLongMA-2-13b-16k/blob/main/results_2023-09-23T08-30-56.994435.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002202181208053691,
"em_stderr": 0.0004800510816619487,
"f1": 0.05451552013422845,
"f1_stderr": 0.001321043219231616,
"acc": 0.39035575610663886,
"acc_stderr": 0.009395368385266412
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.0004800510816619487,
"f1": 0.05451552013422845,
"f1_stderr": 0.001321043219231616
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.006257444037912527
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620296
}
}
```
### 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]
|
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open-llm-leaderboard/details_JosephusCheung__Guanaco
|
open-llm-leaderboard
| 2023-09-23T06:44:14Z | 81 | 0 |
[
"region:us"
] | null | 2023-09-23T06:44:06Z |
---
pretty_name: Evaluation run of JosephusCheung/Guanaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [JosephusCheung/Guanaco](https://huggingface.co/JosephusCheung/Guanaco) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_JosephusCheung__Guanaco\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T06:44:02.813633](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Guanaco/blob/main/results_2023-09-23T06-44-02.813633.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.23343120805369127,\n\
\ \"em_stderr\": 0.004332062137833453,\n \"f1\": 0.2960843120805377,\n\
\ \"f1_stderr\": 0.004351433413685765,\n \"acc\": 0.34333070244672453,\n\
\ \"acc_stderr\": 0.006518256048373988\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.23343120805369127,\n \"em_stderr\": 0.004332062137833453,\n\
\ \"f1\": 0.2960843120805377,\n \"f1_stderr\": 0.004351433413685765\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6866614048934491,\n\
\ \"acc_stderr\": 0.013036512096747976\n }\n}\n```"
repo_url: https://huggingface.co/JosephusCheung/Guanaco
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T06_44_02.813633
path:
- '**/details_harness|drop|3_2023-09-23T06-44-02.813633.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T06-44-02.813633.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T06_44_02.813633
path:
- '**/details_harness|gsm8k|5_2023-09-23T06-44-02.813633.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T06-44-02.813633.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T06_44_02.813633
path:
- '**/details_harness|winogrande|5_2023-09-23T06-44-02.813633.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T06-44-02.813633.parquet'
- config_name: results
data_files:
- split: 2023_09_23T06_44_02.813633
path:
- results_2023-09-23T06-44-02.813633.parquet
- split: latest
path:
- results_2023-09-23T06-44-02.813633.parquet
---
# Dataset Card for Evaluation run of JosephusCheung/Guanaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/JosephusCheung/Guanaco
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [JosephusCheung/Guanaco](https://huggingface.co/JosephusCheung/Guanaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_JosephusCheung__Guanaco",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T06:44:02.813633](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Guanaco/blob/main/results_2023-09-23T06-44-02.813633.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.23343120805369127,
"em_stderr": 0.004332062137833453,
"f1": 0.2960843120805377,
"f1_stderr": 0.004351433413685765,
"acc": 0.34333070244672453,
"acc_stderr": 0.006518256048373988
},
"harness|drop|3": {
"em": 0.23343120805369127,
"em_stderr": 0.004332062137833453,
"f1": 0.2960843120805377,
"f1_stderr": 0.004351433413685765
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6866614048934491,
"acc_stderr": 0.013036512096747976
}
}
```
### 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]
|
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open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg
|
open-llm-leaderboard
| 2023-09-23T08:58:34Z | 81 | 0 |
[
"region:us"
] | null | 2023-09-23T08:58:26Z |
---
pretty_name: Evaluation run of openaccess-ai-collective/manticore-13b-chat-pyg
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openaccess-ai-collective/manticore-13b-chat-pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T08:58:22.598379](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg/blob/main/results_2023-09-23T08-58-22.598379.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.02925755033557047,\n\
\ \"em_stderr\": 0.0017258801842771152,\n \"f1\": 0.09186136744966467,\n\
\ \"f1_stderr\": 0.0021533865918944134,\n \"acc\": 0.4337145226735951,\n\
\ \"acc_stderr\": 0.009944810794409672\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.02925755033557047,\n \"em_stderr\": 0.0017258801842771152,\n\
\ \"f1\": 0.09186136744966467,\n \"f1_stderr\": 0.0021533865918944134\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09552691432903715,\n \
\ \"acc_stderr\": 0.008096605771155745\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.0117930158176636\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T08_58_22.598379
path:
- '**/details_harness|drop|3_2023-09-23T08-58-22.598379.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T08-58-22.598379.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T08_58_22.598379
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-58-22.598379.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T08-58-22.598379.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T08_58_22.598379
path:
- '**/details_harness|winogrande|5_2023-09-23T08-58-22.598379.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T08-58-22.598379.parquet'
- config_name: results
data_files:
- split: 2023_09_23T08_58_22.598379
path:
- results_2023-09-23T08-58-22.598379.parquet
- split: latest
path:
- results_2023-09-23T08-58-22.598379.parquet
---
# Dataset Card for Evaluation run of openaccess-ai-collective/manticore-13b-chat-pyg
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [openaccess-ai-collective/manticore-13b-chat-pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T08:58:22.598379](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg/blob/main/results_2023-09-23T08-58-22.598379.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.02925755033557047,
"em_stderr": 0.0017258801842771152,
"f1": 0.09186136744966467,
"f1_stderr": 0.0021533865918944134,
"acc": 0.4337145226735951,
"acc_stderr": 0.009944810794409672
},
"harness|drop|3": {
"em": 0.02925755033557047,
"em_stderr": 0.0017258801842771152,
"f1": 0.09186136744966467,
"f1_stderr": 0.0021533865918944134
},
"harness|gsm8k|5": {
"acc": 0.09552691432903715,
"acc_stderr": 0.008096605771155745
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.0117930158176636
}
}
```
### 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]
|
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open-llm-leaderboard/details_Aeala__Alpaca-elina-65b
|
open-llm-leaderboard
| 2023-10-08T22:34:40Z | 81 | 0 |
[
"region:us"
] | null | 2023-10-08T22:34:32Z |
---
pretty_name: Evaluation run of Aeala/Alpaca-elina-65b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aeala/Alpaca-elina-65b](https://huggingface.co/Aeala/Alpaca-elina-65b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aeala__Alpaca-elina-65b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-08T22:34:28.379829](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Alpaca-elina-65b/blob/main/results_2023-10-08T22-34-28.379829.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.27736996644295303,\n\
\ \"em_stderr\": 0.004584873651869028,\n \"f1\": 0.33694211409395997,\n\
\ \"f1_stderr\": 0.004497646539610947,\n \"acc\": 0.5520523608267965,\n\
\ \"acc_stderr\": 0.011722735218747584\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.27736996644295303,\n \"em_stderr\": 0.004584873651869028,\n\
\ \"f1\": 0.33694211409395997,\n \"f1_stderr\": 0.004497646539610947\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.29037149355572406,\n \
\ \"acc_stderr\": 0.012503592481818955\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.813733228097869,\n \"acc_stderr\": 0.010941877955676211\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aeala/Alpaca-elina-65b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_08T22_34_28.379829
path:
- '**/details_harness|drop|3_2023-10-08T22-34-28.379829.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-08T22-34-28.379829.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_08T22_34_28.379829
path:
- '**/details_harness|gsm8k|5_2023-10-08T22-34-28.379829.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-08T22-34-28.379829.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_08T22_34_28.379829
path:
- '**/details_harness|winogrande|5_2023-10-08T22-34-28.379829.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-08T22-34-28.379829.parquet'
- config_name: results
data_files:
- split: 2023_10_08T22_34_28.379829
path:
- results_2023-10-08T22-34-28.379829.parquet
- split: latest
path:
- results_2023-10-08T22-34-28.379829.parquet
---
# Dataset Card for Evaluation run of Aeala/Alpaca-elina-65b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aeala/Alpaca-elina-65b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aeala/Alpaca-elina-65b](https://huggingface.co/Aeala/Alpaca-elina-65b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aeala__Alpaca-elina-65b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-08T22:34:28.379829](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Alpaca-elina-65b/blob/main/results_2023-10-08T22-34-28.379829.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.27736996644295303,
"em_stderr": 0.004584873651869028,
"f1": 0.33694211409395997,
"f1_stderr": 0.004497646539610947,
"acc": 0.5520523608267965,
"acc_stderr": 0.011722735218747584
},
"harness|drop|3": {
"em": 0.27736996644295303,
"em_stderr": 0.004584873651869028,
"f1": 0.33694211409395997,
"f1_stderr": 0.004497646539610947
},
"harness|gsm8k|5": {
"acc": 0.29037149355572406,
"acc_stderr": 0.012503592481818955
},
"harness|winogrande|5": {
"acc": 0.813733228097869,
"acc_stderr": 0.010941877955676211
}
}
```
### 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]
|
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] |
bh8648/split_dataset_16-2
|
bh8648
| 2023-10-19T23:27:27Z | 81 | 0 |
[
"region:us"
] | null | 2023-10-19T23:27:24Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: page_num
dtype: int64
splits:
- name: train
num_bytes: 475910
num_examples: 105
download_size: 217195
dataset_size: 475910
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "split_dataset_16-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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maxolotl/must-c-en-es-wait3-02
|
maxolotl
| 2023-10-22T07:48:24Z | 81 | 0 |
[
"region:us"
] | null | 2023-10-22T07:48:05Z |
---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 995120593
num_examples: 5240243
- name: test
num_bytes: 9960448
num_examples: 57187
- name: validation
num_bytes: 5429701
num_examples: 27549
download_size: 184348036
dataset_size: 1010510742
---
# Dataset Card for "must-c-en-es-wait3-02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_shaohang__SparseOPT-1.3B
|
open-llm-leaderboard
| 2023-10-24T23:18:51Z | 81 | 0 |
[
"region:us"
] | null | 2023-10-24T23:18:45Z |
---
pretty_name: Evaluation run of shaohang/SparseOPT-1.3B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shaohang__SparseOPT-1.3B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003145973154362416,\n\
\ \"em_stderr\": 0.0005734993648436396,\n \"f1\": 0.048264471476510196,\n\
\ \"f1_stderr\": 0.0013025272598396927,\n \"acc\": 0.29319675461487227,\n\
\ \"acc_stderr\": 0.007301498172995543\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436396,\n\
\ \"f1\": 0.048264471476510196,\n \"f1_stderr\": 0.0013025272598396927\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225237\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.585635359116022,\n \"acc_stderr\": 0.013844846232268563\n\
\ }\n}\n```"
repo_url: https://huggingface.co/shaohang/SparseOPT-1.3B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet'
- config_name: results
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- results_2023-10-24T23-18-41.395628.parquet
- split: latest
path:
- results_2023-10-24T23-18-41.395628.parquet
---
# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/shaohang/SparseOPT-1.3B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_shaohang__SparseOPT-1.3B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927,
"acc": 0.29319675461487227,
"acc_stderr": 0.007301498172995543
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225237
},
"harness|winogrande|5": {
"acc": 0.585635359116022,
"acc_stderr": 0.013844846232268563
}
}
```
### 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]
|
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] |
beanham/wikiqa
|
beanham
| 2023-11-27T18:59:53Z | 81 | 0 |
[
"task_categories:question-answering",
"language:en",
"region:us"
] |
[
"question-answering"
] | 2023-11-27T18:57:35Z |
---
task_categories:
- question-answering
language:
- en
---
Original dataset: https://www.microsoft.com/en-us/download/details.aspx?id=52419
Original paper: https://aclanthology.org/D15-1237.pdf
|
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lmqg/qg_squadshifts
|
lmqg
| 2022-12-02T18:56:15Z | 80 | 1 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:subjqa",
"language:en",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-06-02T18:56:40Z |
---
license: cc-by-4.0
pretty_name: SubjQA for question generation
language: en
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: subjqa
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_squadshifts"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
Modified version of [SQuADShifts](https://modestyachts.github.io/squadshifts-website/index.html) for question generation (QG) task.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset can be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"question": "has there ever been a legal challange?",
"paragraph": "The status of the Armenian Apostolic Church within the Republic of Armenia is defined in the country's constitution. Article 8.1 of the Constitution of Armenia states: "The Republic of Armenia recognizes the exclusive historical mission of the Armenian Apostolic Holy Church as a national church, in the spiritual life, development of the national culture and preservation of the national identity of the people of Armenia." Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church".",
"answer": "Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church",
"sentence": "Article 8.1 of the Constitution of Armenia states: "The Republic of Armenia recognizes the exclusive historical mission of the Armenian Apostolic Holy Church as a national church, in the spiritual life, development of the national culture and preservation of the national identity of the people of Armenia." Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church",
"paragraph_sentence": "The status of the Armenian Apostolic Church within the Republic of Armenia is defined in the country's constitution. <hl> Article 8.1 of the Constitution of Armenia states: "The Republic of Armenia recognizes the exclusive historical mission of the Armenian Apostolic Holy Church as a national church, in the spiritual life, development of the national culture and preservation of the national identity of the people of Armenia." Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church". <hl>",
"paragraph_answer": "The status of the Armenian Apostolic Church within the Republic of Armenia is defined in the country's constitution. Article 8.1 of the Constitution of Armenia states: "The Republic of Armenia recognizes the exclusive historical mission of the Armenian Apostolic Holy Church as a national church, in the spiritual life, development of the national culture and preservation of the national identity of the people of Armenia." <hl> Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church". <hl>",
"sentence_answer": "Article 8.1 of the Constitution of Armenia states: "The Republic of Armenia recognizes the exclusive historical mission of the Armenian Apostolic Holy Church as a national church, in the spiritual life, development of the national culture and preservation of the national identity of the people of Armenia." <hl> Among others, ethnographer Hranush Kharatyan has questioned the constitutionality of the phrase "national church". <hl>"
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
### Data Splits
| name |train | valid | test |
|-------------|------:|------:|-----:|
|default (all)|9209|6283 |18,844|
| amazon |3295|1648|4942|
| new_wiki |2646|1323|3969|
| nyt |3355|1678|5032|
| reddit |3268|1634|4901|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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PlanTL-GOB-ES/wnli-es
|
PlanTL-GOB-ES
| 2022-11-18T12:03:25Z | 80 | 2 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|glue",
"language:es",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-09-16T13:51:45Z |
---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: wnli-es
size_categories:
- unknown
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# WNLI-es
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Website:** https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
"A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from Terry Winograd." Source: [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
The [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) presents 855 sentence pairs, in which the first sentence contains an ambiguity and the second one a possible interpretation of it. The label indicates if the interpretation is correct (1) or not (0).
This dataset is a professional translation into Spanish of [Winograd NLI dataset](https://dl.fbaipublicfiles.com/glue/data/WNLI.zip) as published in [GLUE Benchmark](https://gluebenchmark.com/tasks).
Both the original dataset and this translation are licenced under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model.
### Languages
* Spanish (es)
## Dataset Structure
### Data Instances
Three tsv files.
### Data Fields
- index
- sentence 1: first sentence of the pair
- sentence 2: second sentence of the pair
- label: relation between the two sentences:
* 0: the second sentence does not entail a correct interpretation of the first one (neutral)
* 1: the second sentence entails a correct interpretation of the first one (entailment)
### Data Splits
- wnli-train-es.csv: 636 sentence pairs
- wnli-dev-es.csv: 72 sentence pairs
- wnli-test-shuffled-es.csv: 147 sentence pairs
## Dataset Creation
### Curation Rationale
We translated this dataset to contribute to the development of language models in Spanish.
### Source Data
- [GLUE Benchmark site](https://gluebenchmark.com)
#### Initial Data Collection and Normalization
This is a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish, commissioned by [BSC TeMU](https://temu.bsc.es/) within the the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
#### Who are the source language producers?
For more information on how the Winograd NLI dataset was created, visit the webpage [The Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html).
### Annotations
#### Annotation process
We comissioned a professional translation of [WNLI dataset](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html) into Spanish.
#### Who are the annotators?
Translation was commisioned to a professional translation agency.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Spanish.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]).
For further information, send an email to ([email protected]).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Contributions
[N/A]
|
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] |
its5Q/yandex-q
|
its5Q
| 2023-04-02T16:48:29Z | 80 | 6 |
[
"task_categories:text-generation",
"task_categories:question-answering",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ru",
"license:cc0-1.0",
"region:us"
] |
[
"text-generation",
"question-answering"
] | 2022-12-04T06:56:33Z |
---
annotations_creators:
- crowdsourced
language:
- ru
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Yandex.Q
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- text-generation
- question-answering
task_ids:
- language-modeling
- open-domain-qa
---
# Dataset Card for Yandex.Q
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/its5Q/yandex-q
### Dataset Summary
This is a dataset of questions and answers scraped from [Yandex.Q](https://yandex.ru/q/). There are 836810 answered questions out of the total of 1297670.
The full dataset that includes all metadata returned by Yandex.Q APIs and contains unanswered questions can be found in `full.jsonl.gz`
### Languages
The dataset is mostly in Russian, but there may be other languages present
## Dataset Structure
### Data Fields
The dataset consists of 3 fields:
- `question` - question title (`string`)
- `description` - question description (`string` or `null`)
- `answer` - answer to the question (`string`)
### Data Splits
All 836810 examples are in the train split, there is no validation split.
## Dataset Creation
The data was scraped through some "hidden" APIs using several scripts, located in [my GitHub repository](https://github.com/its5Q/yandex-q)
## Additional Information
### Dataset Curators
- https://github.com/its5Q
|
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] |
suolyer/webqa
|
suolyer
| 2023-02-23T02:12:12Z | 80 | 16 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-02-22T11:17:52Z |
---
license: apache-2.0
---
|
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] |
Amani27/massive_translation_dataset
|
Amani27
| 2023-07-25T14:54:44Z | 80 | 3 |
[
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"language:es",
"language:hi",
"language:fr",
"language:it",
"language:ar",
"language:nl",
"language:ja",
"language:pt",
"license:cc-by-4.0",
"region:us"
] |
[
"translation"
] | 2023-07-20T16:09:42Z |
---
configs:
- config_name: default
data_files:
- split: train
path: "train.csv"
- split: validation
path: "validation.csv"
- split: test
path: "test.csv"
license: cc-by-4.0
task_categories:
- translation
language:
- en
- de
- es
- hi
- fr
- it
- ar
- nl
- ja
- pt
size_categories:
- 10K<n<100K
---
# Dataset Card for Massive Dataset for Translation
### Dataset Summary
This dataset is derived from AmazonScience/MASSIVE dataset for translation task purpose.
### Supported Tasks and Leaderboards
Translation
### Languages
1. English (en_US)
2. German (de_DE)
3. Hindi (hi_IN)
4. Spanish (es_ES)
5. French (fr_FR)
6. Italian (it_IT)
7. Arabic (ar_SA)
8. Dutch (nl_NL)
9. Japanese (ja_JP)
10. Portugese (pt_PT)
|
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jxie/lipop
|
jxie
| 2023-08-04T22:25:41Z | 80 | 0 |
[
"region:us"
] | null | 2023-08-04T22:25:28Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: float64
splits:
- name: train_0
num_bytes: 200193
num_examples: 3360
- name: val_0
num_bytes: 24928
num_examples: 420
- name: test_0
num_bytes: 24770
num_examples: 420
- name: train_1
num_bytes: 199909
num_examples: 3360
- name: val_1
num_bytes: 25212
num_examples: 420
- name: test_1
num_bytes: 24770
num_examples: 420
- name: train_2
num_bytes: 200080
num_examples: 3360
- name: val_2
num_bytes: 24726
num_examples: 420
- name: test_2
num_bytes: 25085
num_examples: 420
download_size: 387383
dataset_size: 749673
---
# Dataset Card for "lipop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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google/trueteacher
|
google
| 2023-09-06T19:39:38Z | 80 | 9 |
[
"language:en",
"license:cc-by-nc-4.0",
"natural-language-inference",
"news-articles-summarization",
"arxiv:2305.11171",
"arxiv:2210.11416",
"region:us"
] | null | 2023-08-15T11:56:54Z |
---
license: cc-by-nc-4.0
language:
- en
tags:
- natural-language-inference
- news-articles-summarization
---
# **TrueTeacher**
## Dataset Summary
This is a large-scale synthetic dataset for training **Factual Consistency Evaluation** models, introduced in the [TrueTeacher paper (Gekhman et al, 2023)](https://arxiv.org/pdf/2305.11171.pdf).
## Dataset Details
The dataset contains model-generated summaries of articles from the train split of the **CNN/DailyMail** dataset [(Hermann et al., 2015)](https://proceedings.neurips.cc/paper_files/paper/2015/file/afdec7005cc9f14302cd0474fd0f3c96-Paper.pdf)
which are annotated for factual consistency using **FLAN-PaLM 540B** [(Chung et al.,2022)](https://arxiv.org/pdf/2210.11416.pdf).
Summaries were generated using summarization models with different capacities, which were created by fine-tuning **T5** [(Raffel et al., 2020)](https://jmlr.org/papers/volume21/20-074/20-074.pdf) on the **XSum** dataset [(Narayan et al., 2018)](https://aclanthology.org/D18-1206.pdf).
We used the following 5 capacities: T5-11B, T5-3B, T5-large, T5-base and T5-small.
## Data format
The data contains json lines with the following keys:
- `"summarization_model"` - The summarization model used to generate the summary.
- `"cnndm_id"` - The original id from the CNN/DailyMail dataset, this need to be used in order to retrieve the corresponding article from CNN/DailyMail (which was used as the grounding document).
- `"summary"` - The model-generated summary.
- `"label"` - A binary label ('1' - Factualy Consistent, '0' - Factualy Inconsistent).
Here is an example of a single data item:
```json
{
"summarization_model": "T5-11B",
"cnndm_id": "f72048a23154de8699c307e2f41157abbfcae261",
"summary": "Children's brains are being damaged by prolonged internet access, a former children's television presenter has warned."
"label": "1",
}
```
## Loading the dataset
To use the dataset, you need to fetch the relevant documents from the CNN/DailyMail dataset. The follwoing code can be used for that purpose:
```python
from datasets import load_dataset
from tqdm import tqdm
trueteacher_data = load_dataset("google/trueteacher", split='train')
cnn_dailymail_data = load_dataset("cnn_dailymail", version="3.0.0", split='train')
cnn_dailymail_articles_by_id = {example['id']: example['article'] for example in cnn_dailymail_data}
trueteacher_data_with_documents = []
for example in tqdm(trueteacher_data):
example['document'] = cnn_dailymail_articles_by_id[example['cnndm_id']]
trueteacher_data_with_documents.append(example)
```
## Intended Use
This dataset is intended for a research use (**non-commercial**) in English.
The recommended use case is training factual consistency evaluation models for summarization.
## Out-of-scope use
Any use cases which violate the **cc-by-nc-4.0** license.
Usage in languages other than English.
## Citation
If you use this dataset for a research publication, please cite the TrueTeacher paper (using the bibtex entry below), as well as the CNN/DailyMail, XSum, T5 and FLAN papers mentioned above.
```
@misc{gekhman2023trueteacher,
title={TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models},
author={Zorik Gekhman and Jonathan Herzig and Roee Aharoni and Chen Elkind and Idan Szpektor},
year={2023},
eprint={2305.11171},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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BEE-spoke-data/SYSK-Transcripts
|
BEE-spoke-data
| 2023-11-08T01:40:22Z | 80 | 0 |
[
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"region:us"
] | null | 2023-09-17T21:38:31Z |
---
license: cc-by-4.0
size_categories:
- 1K<n<10K
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: title
dtype: string
- name: link
dtype: string
- name: desc
dtype: string
- name: summary
dtype: string
- name: pubDate
dtype: string
- name: pubFormatted
dtype: string
- name: enc_len
dtype: string
- name: enc_type
dtype: string
- name: audio_url
dtype: string
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 72117648
num_examples: 1965
download_size: 40830798
dataset_size: 72117648
- config_name: seg-no-ads
features:
- name: id
dtype: string
- name: title
dtype: string
- name: link
dtype: string
- name: desc
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 69771386
num_examples: 1911
download_size: 40026675
dataset_size: 69771386
- config_name: segmented
features:
- name: id
dtype: string
- name: title
dtype: string
- name: link
dtype: string
- name: desc
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 69521425.32519084
num_examples: 1911
download_size: 40832338
dataset_size: 69521425.32519084
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: seg-no-ads
data_files:
- split: train
path: seg-no-ads/train-*
- config_name: segmented
data_files:
- split: train
path: segmented/train-*
---
# Dataset Card for "SYSK_Transcripts"
Transcriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT
```python
Dataset({
features: ['id', 'title', 'link', 'desc', 'summary', 'pubDate', 'pubFormatted', 'enc_len', 'enc_type', 'audio_url', 'transcript'],
num_rows: 1965
})
```
## citation
```
@article{https://doi.org/10.17605/osf.io/vm9nt,
doi = {10.17605/OSF.IO/VM9NT},
url = {https://osf.io/vm9nt/},
author = {Pierson, Britt},
keywords = {audio processing, audio timestamp, corpus, dataset, natural language processing, nlp, podcast, podcasts trancripts, podcasts transcript, podcast transcript, podcast transcripts, sentiment analysis, transcript, transcripts, transcripts of podcast, transcripts of podcasts},
title = {"Stuff You Should Know" Podcast Transcripts - Full Dataset with Transcript of All Episodes (SYSK_Transcripts)},
publisher = {Open Science Framework},
year = {2022},
copyright = {CC-By Attribution 4.0 International}
}
```
|
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open-llm-leaderboard/details_FabbriSimo01__Cerebras_1.3b_Quantized
|
open-llm-leaderboard
| 2023-09-22T16:09:05Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T16:08:57Z |
---
pretty_name: Evaluation run of FabbriSimo01/Cerebras_1.3b_Quantized
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FabbriSimo01/Cerebras_1.3b_Quantized](https://huggingface.co/FabbriSimo01/Cerebras_1.3b_Quantized)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FabbriSimo01__Cerebras_1.3b_Quantized\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T16:08:53.530245](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Cerebras_1.3b_Quantized/blob/main/results_2023-09-22T16-08-53.530245.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.0002773614457335628,\n \"f1\": 0.03707739093959742,\n\
\ \"f1_stderr\": 0.0010591502361020477,\n \"acc\": 0.2694565433979606,\n\
\ \"acc_stderr\": 0.007855236930515893\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.0002773614457335628,\n\
\ \"f1\": 0.03707739093959742,\n \"f1_stderr\": 0.0010591502361020477\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.0016927007401502038\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5351223362273086,\n \"acc_stderr\": 0.014017773120881582\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FabbriSimo01/Cerebras_1.3b_Quantized
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T16_08_53.530245
path:
- '**/details_harness|drop|3_2023-09-22T16-08-53.530245.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T16-08-53.530245.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T16_08_53.530245
path:
- '**/details_harness|gsm8k|5_2023-09-22T16-08-53.530245.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T16-08-53.530245.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T16_08_53.530245
path:
- '**/details_harness|winogrande|5_2023-09-22T16-08-53.530245.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T16-08-53.530245.parquet'
- config_name: results
data_files:
- split: 2023_09_22T16_08_53.530245
path:
- results_2023-09-22T16-08-53.530245.parquet
- split: latest
path:
- results_2023-09-22T16-08-53.530245.parquet
---
# Dataset Card for Evaluation run of FabbriSimo01/Cerebras_1.3b_Quantized
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FabbriSimo01/Cerebras_1.3b_Quantized
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [FabbriSimo01/Cerebras_1.3b_Quantized](https://huggingface.co/FabbriSimo01/Cerebras_1.3b_Quantized) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FabbriSimo01__Cerebras_1.3b_Quantized",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T16:08:53.530245](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Cerebras_1.3b_Quantized/blob/main/results_2023-09-22T16-08-53.530245.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335628,
"f1": 0.03707739093959742,
"f1_stderr": 0.0010591502361020477,
"acc": 0.2694565433979606,
"acc_stderr": 0.007855236930515893
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335628,
"f1": 0.03707739093959742,
"f1_stderr": 0.0010591502361020477
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401502038
},
"harness|winogrande|5": {
"acc": 0.5351223362273086,
"acc_stderr": 0.014017773120881582
}
}
```
### 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]
|
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open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16
|
open-llm-leaderboard
| 2023-09-22T17:43:22Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T17:43:14Z |
---
pretty_name: Evaluation run of bhenrym14/airophin-v2-13b-PI-8k-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bhenrym14/airophin-v2-13b-PI-8k-fp16](https://huggingface.co/bhenrym14/airophin-v2-13b-PI-8k-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:43:10.494860](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16/blob/main/results_2023-09-22T17-43-10.494860.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0921770134228188,\n\
\ \"em_stderr\": 0.00296245358879876,\n \"f1\": 0.2086210151006714,\n\
\ \"f1_stderr\": 0.0033790655527750446,\n \"acc\": 0.4199589150853921,\n\
\ \"acc_stderr\": 0.009541015115774397\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0921770134228188,\n \"em_stderr\": 0.00296245358879876,\n\
\ \"f1\": 0.2086210151006714,\n \"f1_stderr\": 0.0033790655527750446\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07354056103108415,\n \
\ \"acc_stderr\": 0.007189835754365268\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183525\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bhenrym14/airophin-v2-13b-PI-8k-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_43_10.494860
path:
- '**/details_harness|drop|3_2023-09-22T17-43-10.494860.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-43-10.494860.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_43_10.494860
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-43-10.494860.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-43-10.494860.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_43_10.494860
path:
- '**/details_harness|winogrande|5_2023-09-22T17-43-10.494860.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-43-10.494860.parquet'
- config_name: results
data_files:
- split: 2023_09_22T17_43_10.494860
path:
- results_2023-09-22T17-43-10.494860.parquet
- split: latest
path:
- results_2023-09-22T17-43-10.494860.parquet
---
# Dataset Card for Evaluation run of bhenrym14/airophin-v2-13b-PI-8k-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bhenrym14/airophin-v2-13b-PI-8k-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bhenrym14/airophin-v2-13b-PI-8k-fp16](https://huggingface.co/bhenrym14/airophin-v2-13b-PI-8k-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:43:10.494860](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16/blob/main/results_2023-09-22T17-43-10.494860.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0921770134228188,
"em_stderr": 0.00296245358879876,
"f1": 0.2086210151006714,
"f1_stderr": 0.0033790655527750446,
"acc": 0.4199589150853921,
"acc_stderr": 0.009541015115774397
},
"harness|drop|3": {
"em": 0.0921770134228188,
"em_stderr": 0.00296245358879876,
"f1": 0.2086210151006714,
"f1_stderr": 0.0033790655527750446
},
"harness|gsm8k|5": {
"acc": 0.07354056103108415,
"acc_stderr": 0.007189835754365268
},
"harness|winogrande|5": {
"acc": 0.7663772691397001,
"acc_stderr": 0.011892194477183525
}
}
```
### 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]
|
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open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k
|
open-llm-leaderboard
| 2023-09-22T18:49:20Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T18:49:12Z |
---
pretty_name: Evaluation run of GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k](https://huggingface.co/GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T18:49:08.232237](https://huggingface.co/datasets/open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k/blob/main/results_2023-09-22T18-49-08.232237.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.03838087248322147,\n\
\ \"em_stderr\": 0.0019674269651511014,\n \"f1\": 0.12162856543624175,\n\
\ \"f1_stderr\": 0.0024752721568615517,\n \"acc\": 0.28326869809409316,\n\
\ \"acc_stderr\": 0.00781704702542305\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.03838087248322147,\n \"em_stderr\": 0.0019674269651511014,\n\
\ \"f1\": 0.12162856543624175,\n \"f1_stderr\": 0.0024752721568615517\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.0016927007401501821\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5627466456195738,\n \"acc_stderr\": 0.013941393310695918\n\
\ }\n}\n```"
repo_url: https://huggingface.co/GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T18_49_08.232237
path:
- '**/details_harness|drop|3_2023-09-22T18-49-08.232237.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T18-49-08.232237.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T18_49_08.232237
path:
- '**/details_harness|gsm8k|5_2023-09-22T18-49-08.232237.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T18-49-08.232237.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T18_49_08.232237
path:
- '**/details_harness|winogrande|5_2023-09-22T18-49-08.232237.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T18-49-08.232237.parquet'
- config_name: results
data_files:
- split: 2023_09_22T18_49_08.232237
path:
- results_2023-09-22T18-49-08.232237.parquet
- split: latest
path:
- results_2023-09-22T18-49-08.232237.parquet
---
# Dataset Card for Evaluation run of GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k](https://huggingface.co/GeorgiaTechResearchInstitute/galactica-6.7b-evol-instruct-70k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T18:49:08.232237](https://huggingface.co/datasets/open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k/blob/main/results_2023-09-22T18-49-08.232237.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.03838087248322147,
"em_stderr": 0.0019674269651511014,
"f1": 0.12162856543624175,
"f1_stderr": 0.0024752721568615517,
"acc": 0.28326869809409316,
"acc_stderr": 0.00781704702542305
},
"harness|drop|3": {
"em": 0.03838087248322147,
"em_stderr": 0.0019674269651511014,
"f1": 0.12162856543624175,
"f1_stderr": 0.0024752721568615517
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401501821
},
"harness|winogrande|5": {
"acc": 0.5627466456195738,
"acc_stderr": 0.013941393310695918
}
}
```
### 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]
|
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open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b
|
open-llm-leaderboard
| 2023-09-22T19:56:23Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T19:56:15Z |
---
pretty_name: Evaluation run of zarakiquemparte/zarafusionix-l2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.20669043624161074,\n\
\ \"em_stderr\": 0.004146877317311672,\n \"f1\": 0.29368812919463155,\n\
\ \"f1_stderr\": 0.004195906469994281,\n \"acc\": 0.40933494018871774,\n\
\ \"acc_stderr\": 0.009672451208885371\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.20669043624161074,\n \"em_stderr\": 0.004146877317311672,\n\
\ \"f1\": 0.29368812919463155,\n \"f1_stderr\": 0.004195906469994281\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07202426080363912,\n \
\ \"acc_stderr\": 0.007121147983537124\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233618\n\
\ }\n}\n```"
repo_url: https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T19_56_11.100071
path:
- '**/details_harness|drop|3_2023-09-22T19-56-11.100071.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T19-56-11.100071.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T19_56_11.100071
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-56-11.100071.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-56-11.100071.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T19_56_11.100071
path:
- '**/details_harness|winogrande|5_2023-09-22T19-56-11.100071.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T19-56-11.100071.parquet'
- config_name: results
data_files:
- split: 2023_09_22T19_56_11.100071
path:
- results_2023-09-22T19-56-11.100071.parquet
- split: latest
path:
- results_2023-09-22T19-56-11.100071.parquet
---
# Dataset Card for Evaluation run of zarakiquemparte/zarafusionix-l2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [zarakiquemparte/zarafusionix-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionix-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T19:56:11.100071](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b/blob/main/results_2023-09-22T19-56-11.100071.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.20669043624161074,
"em_stderr": 0.004146877317311672,
"f1": 0.29368812919463155,
"f1_stderr": 0.004195906469994281,
"acc": 0.40933494018871774,
"acc_stderr": 0.009672451208885371
},
"harness|drop|3": {
"em": 0.20669043624161074,
"em_stderr": 0.004146877317311672,
"f1": 0.29368812919463155,
"f1_stderr": 0.004195906469994281
},
"harness|gsm8k|5": {
"acc": 0.07202426080363912,
"acc_stderr": 0.007121147983537124
},
"harness|winogrande|5": {
"acc": 0.7466456195737964,
"acc_stderr": 0.012223754434233618
}
}
```
### 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]
|
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open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca
|
open-llm-leaderboard
| 2023-09-22T20:56:21Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T20:56:13Z |
---
pretty_name: Evaluation run of chavinlo/gpt4-x-alpaca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chavinlo/gpt4-x-alpaca](https://huggingface.co/chavinlo/gpt4-x-alpaca) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T20:56:09.987040](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca/blob/main/results_2023-09-22T20-56-09.987040.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.15478187919463088,\n\
\ \"em_stderr\": 0.003704111989193061,\n \"f1\": 0.24988045302013467,\n\
\ \"f1_stderr\": 0.00385619985047934,\n \"acc\": 0.3648545063856345,\n\
\ \"acc_stderr\": 0.008703557271933391\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.15478187919463088,\n \"em_stderr\": 0.003704111989193061,\n\
\ \"f1\": 0.24988045302013467,\n \"f1_stderr\": 0.00385619985047934\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.028051554207733132,\n \
\ \"acc_stderr\": 0.004548229533836362\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7016574585635359,\n \"acc_stderr\": 0.012858885010030421\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chavinlo/gpt4-x-alpaca
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T20_56_09.987040
path:
- '**/details_harness|drop|3_2023-09-22T20-56-09.987040.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T20-56-09.987040.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T20_56_09.987040
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-56-09.987040.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-56-09.987040.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T20_56_09.987040
path:
- '**/details_harness|winogrande|5_2023-09-22T20-56-09.987040.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T20-56-09.987040.parquet'
- config_name: results
data_files:
- split: 2023_09_22T20_56_09.987040
path:
- results_2023-09-22T20-56-09.987040.parquet
- split: latest
path:
- results_2023-09-22T20-56-09.987040.parquet
---
# Dataset Card for Evaluation run of chavinlo/gpt4-x-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chavinlo/gpt4-x-alpaca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chavinlo/gpt4-x-alpaca](https://huggingface.co/chavinlo/gpt4-x-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T20:56:09.987040](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca/blob/main/results_2023-09-22T20-56-09.987040.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.15478187919463088,
"em_stderr": 0.003704111989193061,
"f1": 0.24988045302013467,
"f1_stderr": 0.00385619985047934,
"acc": 0.3648545063856345,
"acc_stderr": 0.008703557271933391
},
"harness|drop|3": {
"em": 0.15478187919463088,
"em_stderr": 0.003704111989193061,
"f1": 0.24988045302013467,
"f1_stderr": 0.00385619985047934
},
"harness|gsm8k|5": {
"acc": 0.028051554207733132,
"acc_stderr": 0.004548229533836362
},
"harness|winogrande|5": {
"acc": 0.7016574585635359,
"acc_stderr": 0.012858885010030421
}
}
```
### 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]
|
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open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca
|
open-llm-leaderboard
| 2023-09-22T21:36:50Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-22T21:20:16Z |
---
pretty_name: Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca](https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T21:36:39.212716](https://huggingface.co/datasets/open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca/blob/main/results_2023-09-22T21-36-39.212716.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0004404362416107381,\n \"f1_stderr\"\
: 6.976502994544788e-05,\n \"acc\": 0.2541436464088398,\n \"acc_stderr\"\
: 0.007025277661412096\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 0.0004404362416107381,\n \"\
f1_stderr\": 6.976502994544788e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5082872928176796,\n \"acc_stderr\": 0.014050555322824192\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T21_20_12.395485
path:
- '**/details_harness|drop|3_2023-09-22T21-20-12.395485.parquet'
- split: 2023_09_22T21_36_39.212716
path:
- '**/details_harness|drop|3_2023-09-22T21-36-39.212716.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T21-36-39.212716.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T21_20_12.395485
path:
- '**/details_harness|gsm8k|5_2023-09-22T21-20-12.395485.parquet'
- split: 2023_09_22T21_36_39.212716
path:
- '**/details_harness|gsm8k|5_2023-09-22T21-36-39.212716.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T21-36-39.212716.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T21_20_12.395485
path:
- '**/details_harness|winogrande|5_2023-09-22T21-20-12.395485.parquet'
- split: 2023_09_22T21_36_39.212716
path:
- '**/details_harness|winogrande|5_2023-09-22T21-36-39.212716.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T21-36-39.212716.parquet'
- config_name: results
data_files:
- split: 2023_09_22T21_20_12.395485
path:
- results_2023-09-22T21-20-12.395485.parquet
- split: 2023_09_22T21_36_39.212716
path:
- results_2023-09-22T21-36-39.212716.parquet
- split: latest
path:
- results_2023-09-22T21-36-39.212716.parquet
---
# Dataset Card for Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca](https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T21:36:39.212716](https://huggingface.co/datasets/open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca/blob/main/results_2023-09-22T21-36-39.212716.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0004404362416107381,
"f1_stderr": 6.976502994544788e-05,
"acc": 0.2541436464088398,
"acc_stderr": 0.007025277661412096
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0004404362416107381,
"f1_stderr": 6.976502994544788e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5082872928176796,
"acc_stderr": 0.014050555322824192
}
}
```
### 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]
|
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] |
open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct
|
open-llm-leaderboard
| 2023-09-23T05:54:45Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-23T05:54:37Z |
---
pretty_name: Evaluation run of VMware/open-llama-7b-open-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [VMware/open-llama-7b-open-instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T05:54:33.646620](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct/blob/main/results_2023-09-23T05-54-33.646620.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.24811241610738255,\n\
\ \"em_stderr\": 0.004423238498303271,\n \"f1\": 0.3074643456375843,\n\
\ \"f1_stderr\": 0.004402791070678147,\n \"acc\": 0.3298042752007123,\n\
\ \"acc_stderr\": 0.007683951336441218\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.24811241610738255,\n \"em_stderr\": 0.004423238498303271,\n\
\ \"f1\": 0.3074643456375843,\n \"f1_stderr\": 0.004402791070678147\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.0020013057209480527\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.013366596951934383\n\
\ }\n}\n```"
repo_url: https://huggingface.co/VMware/open-llama-7b-open-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T05_54_33.646620
path:
- '**/details_harness|drop|3_2023-09-23T05-54-33.646620.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T05-54-33.646620.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T05_54_33.646620
path:
- '**/details_harness|gsm8k|5_2023-09-23T05-54-33.646620.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T05-54-33.646620.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T05_54_33.646620
path:
- '**/details_harness|winogrande|5_2023-09-23T05-54-33.646620.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T05-54-33.646620.parquet'
- config_name: results
data_files:
- split: 2023_09_23T05_54_33.646620
path:
- results_2023-09-23T05-54-33.646620.parquet
- split: latest
path:
- results_2023-09-23T05-54-33.646620.parquet
---
# Dataset Card for Evaluation run of VMware/open-llama-7b-open-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/VMware/open-llama-7b-open-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [VMware/open-llama-7b-open-instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T05:54:33.646620](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct/blob/main/results_2023-09-23T05-54-33.646620.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.24811241610738255,
"em_stderr": 0.004423238498303271,
"f1": 0.3074643456375843,
"f1_stderr": 0.004402791070678147,
"acc": 0.3298042752007123,
"acc_stderr": 0.007683951336441218
},
"harness|drop|3": {
"em": 0.24811241610738255,
"em_stderr": 0.004423238498303271,
"f1": 0.3074643456375843,
"f1_stderr": 0.004402791070678147
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480527
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.013366596951934383
}
}
```
### 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]
|
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open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct
|
open-llm-leaderboard
| 2023-09-23T09:06:38Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-23T09:06:30Z |
---
pretty_name: Evaluation run of sartmis1/starcoder-finetune-selfinstruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [sartmis1/starcoder-finetune-selfinstruct](https://huggingface.co/sartmis1/starcoder-finetune-selfinstruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T09:06:26.158683](https://huggingface.co/datasets/open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct/blob/main/results_2023-09-23T09-06-26.158683.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\
\ \"em_stderr\": 0.00036305608931189545,\n \"f1\": 0.04220742449664442,\n\
\ \"f1_stderr\": 0.0011048606881245398,\n \"acc\": 0.31919735419373096,\n\
\ \"acc_stderr\": 0.01022815770603217\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931189545,\n\
\ \"f1\": 0.04220742449664442,\n \"f1_stderr\": 0.0011048606881245398\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.060652009097801364,\n \
\ \"acc_stderr\": 0.0065747333814057925\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5777426992896606,\n \"acc_stderr\": 0.013881582030658549\n\
\ }\n}\n```"
repo_url: https://huggingface.co/sartmis1/starcoder-finetune-selfinstruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T09_06_26.158683
path:
- '**/details_harness|drop|3_2023-09-23T09-06-26.158683.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T09-06-26.158683.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T09_06_26.158683
path:
- '**/details_harness|gsm8k|5_2023-09-23T09-06-26.158683.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T09-06-26.158683.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T09_06_26.158683
path:
- '**/details_harness|winogrande|5_2023-09-23T09-06-26.158683.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T09-06-26.158683.parquet'
- config_name: results
data_files:
- split: 2023_09_23T09_06_26.158683
path:
- results_2023-09-23T09-06-26.158683.parquet
- split: latest
path:
- results_2023-09-23T09-06-26.158683.parquet
---
# Dataset Card for Evaluation run of sartmis1/starcoder-finetune-selfinstruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/sartmis1/starcoder-finetune-selfinstruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [sartmis1/starcoder-finetune-selfinstruct](https://huggingface.co/sartmis1/starcoder-finetune-selfinstruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T09:06:26.158683](https://huggingface.co/datasets/open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct/blob/main/results_2023-09-23T09-06-26.158683.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189545,
"f1": 0.04220742449664442,
"f1_stderr": 0.0011048606881245398,
"acc": 0.31919735419373096,
"acc_stderr": 0.01022815770603217
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931189545,
"f1": 0.04220742449664442,
"f1_stderr": 0.0011048606881245398
},
"harness|gsm8k|5": {
"acc": 0.060652009097801364,
"acc_stderr": 0.0065747333814057925
},
"harness|winogrande|5": {
"acc": 0.5777426992896606,
"acc_stderr": 0.013881582030658549
}
}
```
### 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]
|
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open-llm-leaderboard/details_Corianas__Quokka_256m
|
open-llm-leaderboard
| 2023-09-23T10:44:09Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-23T10:44:01Z |
---
pretty_name: Evaluation run of Corianas/Quokka_256m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Corianas/Quokka_256m](https://huggingface.co/Corianas/Quokka_256m) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Corianas__Quokka_256m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T10:43:58.208940](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__Quokka_256m/blob/main/results_2023-09-23T10-43-58.208940.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003984899328859061,\n\
\ \"em_stderr\": 0.0006451805848102272,\n \"f1\": 0.04266883389261752,\n\
\ \"f1_stderr\": 0.0013952300953918367,\n \"acc\": 0.2612470402525651,\n\
\ \"acc_stderr\": 0.007019128912029941\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003984899328859061,\n \"em_stderr\": 0.0006451805848102272,\n\
\ \"f1\": 0.04266883389261752,\n \"f1_stderr\": 0.0013952300953918367\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5224940805051302,\n\
\ \"acc_stderr\": 0.014038257824059881\n }\n}\n```"
repo_url: https://huggingface.co/Corianas/Quokka_256m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T10_43_58.208940
path:
- '**/details_harness|drop|3_2023-09-23T10-43-58.208940.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T10-43-58.208940.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T10_43_58.208940
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-43-58.208940.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-43-58.208940.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T10_43_58.208940
path:
- '**/details_harness|winogrande|5_2023-09-23T10-43-58.208940.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T10-43-58.208940.parquet'
- config_name: results
data_files:
- split: 2023_09_23T10_43_58.208940
path:
- results_2023-09-23T10-43-58.208940.parquet
- split: latest
path:
- results_2023-09-23T10-43-58.208940.parquet
---
# Dataset Card for Evaluation run of Corianas/Quokka_256m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Corianas/Quokka_256m
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Corianas/Quokka_256m](https://huggingface.co/Corianas/Quokka_256m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Corianas__Quokka_256m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T10:43:58.208940](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__Quokka_256m/blob/main/results_2023-09-23T10-43-58.208940.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102272,
"f1": 0.04266883389261752,
"f1_stderr": 0.0013952300953918367,
"acc": 0.2612470402525651,
"acc_stderr": 0.007019128912029941
},
"harness|drop|3": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102272,
"f1": 0.04266883389261752,
"f1_stderr": 0.0013952300953918367
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5224940805051302,
"acc_stderr": 0.014038257824059881
}
}
```
### 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]
|
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open-llm-leaderboard/details_FabbriSimo01__GPT_Large_Quantized
|
open-llm-leaderboard
| 2023-09-23T20:31:23Z | 80 | 0 |
[
"region:us"
] | null | 2023-09-23T20:31:15Z |
---
pretty_name: Evaluation run of FabbriSimo01/GPT_Large_Quantized
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FabbriSimo01/GPT_Large_Quantized](https://huggingface.co/FabbriSimo01/GPT_Large_Quantized)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FabbriSimo01__GPT_Large_Quantized\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T20:31:12.168542](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__GPT_Large_Quantized/blob/main/results_2023-09-23T20-31-12.168542.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 3.3557046979865775e-05,\n \"f1_stderr\"\
: 2.2973574047539685e-05,\n \"acc\": 0.24664561957379638,\n \"acc_stderr\"\
: 0.0070256103461651745\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 3.3557046979865775e-05,\n \"\
f1_stderr\": 2.2973574047539685e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.49329123914759276,\n \"acc_stderr\": 0.014051220692330349\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FabbriSimo01/GPT_Large_Quantized
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T20_31_12.168542
path:
- '**/details_harness|drop|3_2023-09-23T20-31-12.168542.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T20-31-12.168542.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T20_31_12.168542
path:
- '**/details_harness|gsm8k|5_2023-09-23T20-31-12.168542.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T20-31-12.168542.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T20_31_12.168542
path:
- '**/details_harness|winogrande|5_2023-09-23T20-31-12.168542.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T20-31-12.168542.parquet'
- config_name: results
data_files:
- split: 2023_09_23T20_31_12.168542
path:
- results_2023-09-23T20-31-12.168542.parquet
- split: latest
path:
- results_2023-09-23T20-31-12.168542.parquet
---
# Dataset Card for Evaluation run of FabbriSimo01/GPT_Large_Quantized
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FabbriSimo01/GPT_Large_Quantized
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [FabbriSimo01/GPT_Large_Quantized](https://huggingface.co/FabbriSimo01/GPT_Large_Quantized) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FabbriSimo01__GPT_Large_Quantized",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T20:31:12.168542](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__GPT_Large_Quantized/blob/main/results_2023-09-23T20-31-12.168542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 3.3557046979865775e-05,
"f1_stderr": 2.2973574047539685e-05,
"acc": 0.24664561957379638,
"acc_stderr": 0.0070256103461651745
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 3.3557046979865775e-05,
"f1_stderr": 2.2973574047539685e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.49329123914759276,
"acc_stderr": 0.014051220692330349
}
}
```
### 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]
|
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open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA
|
open-llm-leaderboard
| 2023-10-09T18:55:57Z | 80 | 0 |
[
"region:us"
] | null | 2023-10-09T18:55:49Z |
---
pretty_name: Evaluation run of v2ray/LLaMA-2-Jannie-70B-QLoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [v2ray/LLaMA-2-Jannie-70B-QLoRA](https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-09T18:55:45.725131](https://huggingface.co/datasets/open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA/blob/main/results_2023-10-09T18-55-45.725131.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.5506501677852349,\n\
\ \"em_stderr\": 0.0050941277409732805,\n \"f1\": 0.5974674916107394,\n\
\ \"f1_stderr\": 0.004813528422862131,\n \"acc\": 0.5735917227001633,\n\
\ \"acc_stderr\": 0.011696543872157381\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.5506501677852349,\n \"em_stderr\": 0.0050941277409732805,\n\
\ \"f1\": 0.5974674916107394,\n \"f1_stderr\": 0.004813528422862131\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.31766489764973466,\n \
\ \"acc_stderr\": 0.012824066621488854\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825909\n\
\ }\n}\n```"
repo_url: https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_09T18_55_45.725131
path:
- '**/details_harness|drop|3_2023-10-09T18-55-45.725131.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-09T18-55-45.725131.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_09T18_55_45.725131
path:
- '**/details_harness|gsm8k|5_2023-10-09T18-55-45.725131.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-09T18-55-45.725131.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_09T18_55_45.725131
path:
- '**/details_harness|winogrande|5_2023-10-09T18-55-45.725131.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-09T18-55-45.725131.parquet'
- config_name: results
data_files:
- split: 2023_10_09T18_55_45.725131
path:
- results_2023-10-09T18-55-45.725131.parquet
- split: latest
path:
- results_2023-10-09T18-55-45.725131.parquet
---
# Dataset Card for Evaluation run of v2ray/LLaMA-2-Jannie-70B-QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [v2ray/LLaMA-2-Jannie-70B-QLoRA](https://huggingface.co/v2ray/LLaMA-2-Jannie-70B-QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-09T18:55:45.725131](https://huggingface.co/datasets/open-llm-leaderboard/details_v2ray__LLaMA-2-Jannie-70B-QLoRA/blob/main/results_2023-10-09T18-55-45.725131.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.5506501677852349,
"em_stderr": 0.0050941277409732805,
"f1": 0.5974674916107394,
"f1_stderr": 0.004813528422862131,
"acc": 0.5735917227001633,
"acc_stderr": 0.011696543872157381
},
"harness|drop|3": {
"em": 0.5506501677852349,
"em_stderr": 0.0050941277409732805,
"f1": 0.5974674916107394,
"f1_stderr": 0.004813528422862131
},
"harness|gsm8k|5": {
"acc": 0.31766489764973466,
"acc_stderr": 0.012824066621488854
},
"harness|winogrande|5": {
"acc": 0.829518547750592,
"acc_stderr": 0.010569021122825909
}
}
```
### 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]
|
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open-llm-leaderboard/details_bigscience__bloomz-7b1-mt
|
open-llm-leaderboard
| 2023-12-04T12:55:35Z | 80 | 0 |
[
"region:us"
] | null | 2023-10-13T00:23:18Z |
---
pretty_name: Evaluation run of bigscience/bloomz-7b1-mt
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 8 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_bigscience__bloomz-7b1-mt\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T12:55:29.467627](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-7b1-mt/blob/main/results_2023-12-04T12-55-29.467627.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/bigscience/bloomz-7b1-mt
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T00_23_14.934221
path:
- '**/details_harness|drop|3_2023-10-13T00-23-14.934221.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T00-23-14.934221.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T00_23_14.934221
path:
- '**/details_harness|gsm8k|5_2023-10-13T00-23-14.934221.parquet'
- split: 2023_12_03T14_51_42.619501
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-51-42.619501.parquet'
- split: 2023_12_03T15_54_23.973238
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-54-23.973238.parquet'
- split: 2023_12_03T15_55_23.937859
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-55-23.937859.parquet'
- split: 2023_12_04T09_44_40.128001
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-44-40.128001.parquet'
- split: 2023_12_04T09_44_57.525053
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-44-57.525053.parquet'
- split: 2023_12_04T12_55_16.770565
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-55-16.770565.parquet'
- split: 2023_12_04T12_55_29.467627
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-55-29.467627.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-55-29.467627.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T00_23_14.934221
path:
- '**/details_harness|winogrande|5_2023-10-13T00-23-14.934221.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T00-23-14.934221.parquet'
- config_name: results
data_files:
- split: 2023_10_13T00_23_14.934221
path:
- results_2023-10-13T00-23-14.934221.parquet
- split: 2023_12_03T14_51_42.619501
path:
- results_2023-12-03T14-51-42.619501.parquet
- split: 2023_12_03T15_54_23.973238
path:
- results_2023-12-03T15-54-23.973238.parquet
- split: 2023_12_03T15_55_23.937859
path:
- results_2023-12-03T15-55-23.937859.parquet
- split: 2023_12_04T09_44_40.128001
path:
- results_2023-12-04T09-44-40.128001.parquet
- split: 2023_12_04T09_44_57.525053
path:
- results_2023-12-04T09-44-57.525053.parquet
- split: 2023_12_04T12_55_16.770565
path:
- results_2023-12-04T12-55-16.770565.parquet
- split: 2023_12_04T12_55_29.467627
path:
- results_2023-12-04T12-55-29.467627.parquet
- split: latest
path:
- results_2023-12-04T12-55-29.467627.parquet
---
# Dataset Card for Evaluation run of bigscience/bloomz-7b1-mt
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-7b1-mt
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 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_bigscience__bloomz-7b1-mt",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:55:29.467627](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-7b1-mt/blob/main/results_2023-12-04T12-55-29.467627.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]
|
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open-llm-leaderboard/details_ziqingyang__chinese-llama-2-13b
|
open-llm-leaderboard
| 2023-10-13T02:16:41Z | 80 | 0 |
[
"region:us"
] | null | 2023-10-13T02:16:32Z |
---
pretty_name: Evaluation run of ziqingyang/chinese-llama-2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ziqingyang/chinese-llama-2-13b](https://huggingface.co/ziqingyang/chinese-llama-2-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ziqingyang__chinese-llama-2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-13T02:16:28.624292](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-llama-2-13b/blob/main/results_2023-10-13T02-16-28.624292.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.37741191275167785,\n\
\ \"em_stderr\": 0.004964183842623747,\n \"f1\": 0.42850880872483355,\n\
\ \"f1_stderr\": 0.004835429715953239,\n \"acc\": 0.39816494163081856,\n\
\ \"acc_stderr\": 0.008707972830386747\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.37741191275167785,\n \"em_stderr\": 0.004964183842623747,\n\
\ \"f1\": 0.42850880872483355,\n \"f1_stderr\": 0.004835429715953239\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.039423805913570885,\n \
\ \"acc_stderr\": 0.005360280030342443\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7569060773480663,\n \"acc_stderr\": 0.01205566563043105\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ziqingyang/chinese-llama-2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T02_16_28.624292
path:
- '**/details_harness|drop|3_2023-10-13T02-16-28.624292.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T02-16-28.624292.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T02_16_28.624292
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-16-28.624292.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-16-28.624292.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T02_16_28.624292
path:
- '**/details_harness|winogrande|5_2023-10-13T02-16-28.624292.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T02-16-28.624292.parquet'
- config_name: results
data_files:
- split: 2023_10_13T02_16_28.624292
path:
- results_2023-10-13T02-16-28.624292.parquet
- split: latest
path:
- results_2023-10-13T02-16-28.624292.parquet
---
# Dataset Card for Evaluation run of ziqingyang/chinese-llama-2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ziqingyang/chinese-llama-2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ziqingyang/chinese-llama-2-13b](https://huggingface.co/ziqingyang/chinese-llama-2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ziqingyang__chinese-llama-2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T02:16:28.624292](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-llama-2-13b/blob/main/results_2023-10-13T02-16-28.624292.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.37741191275167785,
"em_stderr": 0.004964183842623747,
"f1": 0.42850880872483355,
"f1_stderr": 0.004835429715953239,
"acc": 0.39816494163081856,
"acc_stderr": 0.008707972830386747
},
"harness|drop|3": {
"em": 0.37741191275167785,
"em_stderr": 0.004964183842623747,
"f1": 0.42850880872483355,
"f1_stderr": 0.004835429715953239
},
"harness|gsm8k|5": {
"acc": 0.039423805913570885,
"acc_stderr": 0.005360280030342443
},
"harness|winogrande|5": {
"acc": 0.7569060773480663,
"acc_stderr": 0.01205566563043105
}
}
```
### 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]
|
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open-llm-leaderboard/details_TehVenom__DiffMerge_Pygmalion_Main-onto-V8P4
|
open-llm-leaderboard
| 2023-10-15T10:36:08Z | 80 | 0 |
[
"region:us"
] | null | 2023-10-15T10:36:00Z |
---
pretty_name: Evaluation run of TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4](https://huggingface.co/TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TehVenom__DiffMerge_Pygmalion_Main-onto-V8P4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T10:35:56.777835](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__DiffMerge_Pygmalion_Main-onto-V8P4/blob/main/results_2023-10-15T10-35-56.777835.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002726510067114094,\n\
\ \"em_stderr\": 0.0005340111700415918,\n \"f1\": 0.05529781879194656,\n\
\ \"f1_stderr\": 0.0013448797167935412,\n \"acc\": 0.31823545497683364,\n\
\ \"acc_stderr\": 0.008263105361288367\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415918,\n\
\ \"f1\": 0.05529781879194656,\n \"f1_stderr\": 0.0013448797167935412\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \
\ \"acc_stderr\": 0.002920666198788727\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6250986582478295,\n \"acc_stderr\": 0.013605544523788008\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T10_35_56.777835
path:
- '**/details_harness|drop|3_2023-10-15T10-35-56.777835.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T10-35-56.777835.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T10_35_56.777835
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-35-56.777835.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-35-56.777835.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T10_35_56.777835
path:
- '**/details_harness|winogrande|5_2023-10-15T10-35-56.777835.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T10-35-56.777835.parquet'
- config_name: results
data_files:
- split: 2023_10_15T10_35_56.777835
path:
- results_2023-10-15T10-35-56.777835.parquet
- split: latest
path:
- results_2023-10-15T10-35-56.777835.parquet
---
# Dataset Card for Evaluation run of TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4](https://huggingface.co/TehVenom/DiffMerge_Pygmalion_Main-onto-V8P4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TehVenom__DiffMerge_Pygmalion_Main-onto-V8P4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T10:35:56.777835](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__DiffMerge_Pygmalion_Main-onto-V8P4/blob/main/results_2023-10-15T10-35-56.777835.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415918,
"f1": 0.05529781879194656,
"f1_stderr": 0.0013448797167935412,
"acc": 0.31823545497683364,
"acc_stderr": 0.008263105361288367
},
"harness|drop|3": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415918,
"f1": 0.05529781879194656,
"f1_stderr": 0.0013448797167935412
},
"harness|gsm8k|5": {
"acc": 0.011372251705837756,
"acc_stderr": 0.002920666198788727
},
"harness|winogrande|5": {
"acc": 0.6250986582478295,
"acc_stderr": 0.013605544523788008
}
}
```
### 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]
|
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open-llm-leaderboard/details_bhenrym14__airoboros-33b-gpt4-1.4.1-PI-8192-fp16
|
open-llm-leaderboard
| 2023-10-15T19:12:51Z | 80 | 0 |
[
"region:us"
] | null | 2023-10-15T19:12:38Z |
---
pretty_name: Evaluation run of bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bhenrym14__airoboros-33b-gpt4-1.4.1-PI-8192-fp16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T19:12:34.050776](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airoboros-33b-gpt4-1.4.1-PI-8192-fp16/blob/main/results_2023-10-15T19-12-34.050776.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.03544463087248322,\n\
\ \"em_stderr\": 0.0018935573437954016,\n \"f1\": 0.08440436241610706,\n\
\ \"f1_stderr\": 0.002470333585036359,\n \"acc\": 0.2841357537490134,\n\
\ \"acc_stderr\": 0.0069604360550053574\n },\n \"harness|drop|3\":\
\ {\n \"em\": 0.03544463087248322,\n \"em_stderr\": 0.0018935573437954016,\n\
\ \"f1\": 0.08440436241610706,\n \"f1_stderr\": 0.002470333585036359\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5682715074980268,\n\
\ \"acc_stderr\": 0.013920872110010715\n }\n}\n```"
repo_url: https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T19_12_34.050776
path:
- '**/details_harness|drop|3_2023-10-15T19-12-34.050776.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T19-12-34.050776.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T19_12_34.050776
path:
- '**/details_harness|gsm8k|5_2023-10-15T19-12-34.050776.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T19-12-34.050776.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T19_12_34.050776
path:
- '**/details_harness|winogrande|5_2023-10-15T19-12-34.050776.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T19-12-34.050776.parquet'
- config_name: results
data_files:
- split: 2023_10_15T19_12_34.050776
path:
- results_2023-10-15T19-12-34.050776.parquet
- split: latest
path:
- results_2023-10-15T19-12-34.050776.parquet
---
# Dataset Card for Evaluation run of bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bhenrym14__airoboros-33b-gpt4-1.4.1-PI-8192-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T19:12:34.050776](https://huggingface.co/datasets/open-llm-leaderboard/details_bhenrym14__airoboros-33b-gpt4-1.4.1-PI-8192-fp16/blob/main/results_2023-10-15T19-12-34.050776.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.03544463087248322,
"em_stderr": 0.0018935573437954016,
"f1": 0.08440436241610706,
"f1_stderr": 0.002470333585036359,
"acc": 0.2841357537490134,
"acc_stderr": 0.0069604360550053574
},
"harness|drop|3": {
"em": 0.03544463087248322,
"em_stderr": 0.0018935573437954016,
"f1": 0.08440436241610706,
"f1_stderr": 0.002470333585036359
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5682715074980268,
"acc_stderr": 0.013920872110010715
}
}
```
### 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]
|
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] |
ernlavr/IDMGSP-danish
|
ernlavr
| 2023-11-18T13:21:17Z | 80 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-12T18:52:42Z |
---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype: int64
- name: abstract
dtype: string
- name: translation
dtype: string
splits:
- name: train
num_bytes: 24634068
num_examples: 16000
- name: test
num_bytes: 12269388
num_examples: 8000
download_size: 19382041
dataset_size: 36903456
---
## IDMGSP Danish Translated
Danish translated version of the original [IDMGSP](https://huggingface.co/datasets/tum-nlp/IDMGSP) dataset. This Danish version contains all `abstracts` from the `classifier_input` subset. Translated using an [`Nllb-200-Distilled-600M`](https://huggingface.co/facebook/nllb-200-distilled-600M). Made as part of the [DM-AI 2023](https://dmiai.dk/) competition
|
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deokhk/en_wiki_sentences_1000
|
deokhk
| 2023-11-21T10:48:21Z | 80 | 0 |
[
"region:us"
] | null | 2023-11-21T10:48:16Z |
---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 128906
num_examples: 1000
- name: dev
num_bytes: 124329
num_examples: 1000
download_size: 157596
dataset_size: 253235
---
# Dataset Card for "en_wiki_sentences_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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surrey-nlp/PLOD-filtered
|
surrey-nlp
| 2023-01-14T23:30:12Z | 79 | 0 |
[
"task_categories:token-classification",
"annotations_creators:Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"abbreviation-detection",
"arxiv:2204.12061",
"region:us"
] |
[
"token-classification"
] | 2022-04-16T14:50:15Z |
---
annotations_creators:
- Leonardo Zilio, Hadeel Saadany, Prashant Sharma, Diptesh Kanojia, Constantin Orasan
language_creators:
- found
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: plod-filtered
pretty_name: 'PLOD: An Abbreviation Detection Dataset'
tags:
- abbreviation-detection
---
# PLOD: An Abbreviation Detection Dataset
This is the repository for PLOD Dataset published at LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection.
### Dataset
We provide two variants of our dataset - Filtered and Unfiltered. They are described in our paper here.
1. The Filtered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>
2. The Unfiltered version can be accessed via [Huggingface Datasets here](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) and a [CONLL format is present here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection).<br/>
3. The [SDU Shared Task](https://sites.google.com/view/sdu-aaai22/home) data we use for zero-shot testing is [available here](https://huggingface.co/datasets/surrey-nlp/SDU-test).
# Dataset Card for PLOD-filtered
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/surrey-nlp/PLOD-AbbreviationDetection
- **Paper:** https://arxiv.org/abs/2204.12061
- **Leaderboard:** https://paperswithcode.com/sota/abbreviationdetection-on-plod-filtered
- **Point of Contact:** [Diptesh Kanojia](mailto:[email protected])
### Dataset Summary
This PLOD Dataset is an English-language dataset of abbreviations and their long-forms tagged in text. The dataset has been collected for research from the PLOS journals indexing of abbreviations and long-forms in the text. This dataset was created to support the Natural Language Processing task of abbreviation detection and covers the scientific domain.
### Supported Tasks and Leaderboards
This dataset primarily supports the Abbreviation Detection Task. It has also been tested on a train+dev split provided by the Acronym Detection Shared Task organized as a part of the Scientific Document Understanding (SDU) workshop at AAAI 2022.
### Languages
English
## Dataset Structure
### Data Instances
A typical data point comprises an ID, a set of `tokens` present in the text, a set of `pos_tags` for the corresponding tokens obtained via Spacy NER, and a set of `ner_tags` which are limited to `AC` for `Acronym` and `LF` for `long-forms`.
An example from the dataset:
{'id': '1',
'tokens': ['Study', '-', 'specific', 'risk', 'ratios', '(', 'RRs', ')', 'and', 'mean', 'BW', 'differences', 'were', 'calculated', 'using', 'linear', 'and', 'log', '-', 'binomial', 'regression', 'models', 'controlling', 'for', 'confounding', 'using', 'inverse', 'probability', 'of', 'treatment', 'weights', '(', 'IPTW', ')', 'truncated', 'at', 'the', '1st', 'and', '99th', 'percentiles', '.'],
'pos_tags': [8, 13, 0, 8, 8, 13, 12, 13, 5, 0, 12, 8, 3, 16, 16, 0, 5, 0, 13, 0, 8, 8, 16, 1, 8, 16, 0, 8, 1, 8, 8, 13, 12, 13, 16, 1, 6, 0, 5, 0, 8, 13],
'ner_tags': [0, 0, 0, 3, 4, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
### Data Fields
- id: the row identifier for the dataset point.
- tokens: The tokens contained in the text.
- pos_tags: the Part-of-Speech tags obtained for the corresponding token above from Spacy NER.
- ner_tags: The tags for abbreviations and long-forms.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Filtered | 112652 | 24140 | 24140|
| Unfiltered | 113860 | 24399 | 24399|
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Extracting the data from PLOS Journals online and then tokenization, normalization.
#### Who are the source language producers?
PLOS Journal
## Additional Information
### Dataset Curators
The dataset was initially created by Leonardo Zilio, Hadeel Saadany, Prashant Sharma,
Diptesh Kanojia, Constantin Orasan.
### Licensing Information
CC-BY-SA 4.0
### Citation Information
[Needs More Information]
### Installation
We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/).<br/>
Please see the instructions at these websites to setup your own custom training with our dataset to reproduce the experiments using Spacy.
OR<br/>
However, you can also reproduce the experiments via the Python notebook we [provide here](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/blob/main/nbs/fine_tuning_abbr_det.ipynb) which uses HuggingFace Trainer class to perform the same experiments. The exact hyperparameters can be obtained from the models readme cards linked below. Before starting, please perform the following steps:
```bash
git clone https://github.com/surrey-nlp/PLOD-AbbreviationDetection
cd PLOD-AbbreviationDetection
pip install -r requirements.txt
```
Now, you can use the notebook to reproduce the experiments.
### Model(s)
Our best performing models are hosted on the HuggingFace models repository
| Models | [`PLOD - Unfiltered`](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) | [`PLOD - Filtered`](https://huggingface.co/datasets/surrey-nlp/PLOD-filtered) | Description |
| --- | :---: | :---: | --- |
| [RoBERTa<sub>large</sub>](https://huggingface.co/roberta-large) | [RoBERTa<sub>large</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | -soon- | Fine-tuning on the RoBERTa<sub>large</sub> language model |
| [RoBERTa<sub>base</sub>](https://huggingface.co/roberta-base) | -soon- | [RoBERTa<sub>base</sub>-finetuned-abbr](https://huggingface.co/surrey-nlp/roberta-large-finetuned-abbr) | Fine-tuning on the RoBERTa<sub>base</sub> language model |
| [AlBERT<sub>large-v2</sub>](https://huggingface.co/albert-large-v2) | [AlBERT<sub>large-v2</sub>-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) | -soon- | Fine-tuning on the AlBERT<sub>large-v2</sub> language model |
On the link provided above, the model(s) can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.<br/>
### Usage
You can use the HuggingFace Model link above to find the instructions for using this model in Python locally using the notebook provided in the Git repo.
|
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] |
bigbio/euadr
|
bigbio
| 2022-12-22T15:44:36Z | 79 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:08:25Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: EU-ADR
homepage: https://www.sciencedirect.com/science/article/pii/S1532046412000573
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for EU-ADR
## Dataset Description
- **Homepage:** https://www.sciencedirect.com/science/article/pii/S1532046412000573
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE
Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.
## Citation Information
```
@article{VANMULLIGEN2012879,
title = {The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships},
journal = {Journal of Biomedical Informatics},
volume = {45},
number = {5},
pages = {879-884},
year = {2012},
note = {Text Mining and Natural Language Processing in Pharmacogenomics},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2012.04.004},
url = {https://www.sciencedirect.com/science/article/pii/S1532046412000573},
author = {Erik M. {van Mulligen} and Annie Fourrier-Reglat and David Gurwitz and Mariam Molokhia and Ainhoa Nieto and Gianluca Trifiro and Jan A. Kors and Laura I. Furlong},
keywords = {Text mining, Corpus development, Machine learning, Adverse drug reactions},
abstract = {Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug–disorder, drug–target, and target–disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts.}
}
```
|
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Jacobvs/PoliticalTweets
|
Jacobvs
| 2023-02-22T19:19:34Z | 79 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-02-22T19:18:37Z |
---
license: mit
---
|
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KK04/LogicInference_OA
|
KK04
| 2023-04-05T15:38:22Z | 79 | 5 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"Logic Inference",
"region:us"
] |
[
"question-answering"
] | 2023-04-05T14:35:16Z |
---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 30414202
num_examples: 54607
download_size: 7588805
dataset_size: 30414202
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- Logic Inference
size_categories:
- 10K<n<100K
---
# Dataset Card for "LogicInference_OA"
This is an re-produce of the dataset from LogicInference Dataset in paper: https://openreview.net/pdf?id=HAGeIS_Lcg9.
The github page of LogicInference Dataset: https://github.com/google-research/google-research/tree/master/logic_inference_dataset.
This dataset is aimed to offer more dataset for Open Assistant project, depending on their demands, there three columns: INSTRUCTION, RESPONSE, SOURCE.
The results in this dataset is a little different from which was introduced in the original paper:
1.For all three splits (IID/OOD/length), only IID is used. In the original paper, it seems that model can reach better performance with data generated by this split method.
2.In the original paper, there are two form of responses: LOGICINFERENCE<sub>b</sub> (with the answer at the beginning) and LOGICINFERENCE<sub>e</sub> (with the answer at the end). This dataset uses LOGICINFERENCE<sub>e</sub>, that means: for all questions, the model will first do logic inference, and give the final answer at the end.
3.The original paper, some parameters in generate_dataset.py are:
N_INFERENCE_PROBLEMS = 5000
N_VARIATIONS = 25
N_EXAMPLES = 200000
TRAIN_RATIO = 0.9
LENGTH_SPLIT_THRESHOLD = 4
RANDOM_SEED = 0
I choose some new parameters:
N_INFERENCE_PROBLEMS = 10000
N_VARIATIONS = 25
N_EXAMPLES = 55000
TRAIN_RATIO = 1
LENGTH_SPLIT_THRESHOLD = 4
RANDOM_SEED = 1111
The original script generated 4814 different inference problems and extended all those inference problems to around 200,000 Q-A pairs. My settings generated 5491 different inference problems and extended them to around 54,607 Instruction-Response pairs. I think for Open Assistant projects, maybe the number of different inference problems is more important, and generated many similar Instruction-Response pairs will only add training time and doesn't make much sense.
|
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mstz/vertebral_column
|
mstz
| 2023-04-16T18:03:50Z | 79 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"vertebral_column",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-07T10:20:48Z |
---
language:
- en
tags:
- vertebral_column
- tabular_classification
- binary_classification
- UCI
pretty_name: Vertebral Column
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- vertebral
license: cc
---
# Vertebral Column
The [Vertebral Column dataset](https://archive.ics.uci.edu/ml/datasets/vertebral+column) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| abnormal | Binary classification | Is the spine abnormal?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/vertebral_column")["train"]
```
|
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Nahrawy/FAID-Depth-ControlNet
|
Nahrawy
| 2023-05-06T18:28:28Z | 79 | 0 |
[
"region:us"
] | null | 2023-04-29T13:28:14Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: depth_map
dtype: image
- name: scene
dtype: string
- name: caption
dtype: string
- name: state
dtype: string
splits:
- name: train
num_bytes: 11835627985.25
num_examples: 5550
download_size: 12139477164
dataset_size: 11835627985.25
---
# A Dataset of Flash and Ambient Illumination Pairs from the Crowd
This is a version of the [A Dataset of Flash and Ambient Illumination Pairs from the Crowd](http://yaksoy.github.io/flashambient/) dataset equipped for training ControlNet using depth maps conditioning.
The dataset includes 2775 pairs of flash light and ambient light images. It includes images of people, shelves, plants, toys, rooms and objects.
Captions were generated using the [BLIP-2, Flan T5-xxl](https://huggingface.co/Salesforce/blip2-flan-t5-xxl) model.
Depth maps were generated using the [GLPN fine-tuned on NYUv2 ](https://huggingface.co/vinvino02/glpn-nyu) model.
## Examples
## Disclaimer
I do not own any of this data.
|
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nicholasKluge/reward-aira-dataset
|
nicholasKluge
| 2023-12-02T14:19:34Z | 79 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:pt",
"language:en",
"license:apache-2.0",
"reward model",
"instruction",
"alignment",
"region:us"
] |
[
"text-classification"
] | 2023-06-07T18:14:57Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- pt
- en
tags:
- reward model
- instruction
- alignment
pretty_name: Reward-Aira Dataset
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: instruction
dtype: string
- name: chosen_response
dtype: string
- name: rejected_response
dtype: string
splits:
- name: english
num_bytes: 53232958
num_examples: 32675
- name: portuguese
num_bytes: 59650447
num_examples: 32675
download_size: 65455319
dataset_size: 112883405
---
# Dataset (`Reward-Aira Dataset`)
## Overview
This dataset contains a collection of prompt + completion examples of LLM following instructions in a conversational manner. All prompts come with two possible completions (one better than the other). The dataset is available in both Portuguese and English languages.
## Dataset Details
- **Dataset Name:** Reward-Aira Dataset
- **Language:** Portuguese and English
- **Total Size:** 32,675 pairs of demonstrations (in Portuguese and English)
## Contents
The dataset consists of data frames with the following columns:
- **Prompt (`instruction`):** The initial prompt provided to the model.
- **Completion (`chosen_response`):** A completion to the prompt.
- **Completion (`rejected_response`):** A worst completion to the prompt.
```python
{
"instruction":"Why is AI Ethics important?",
"chosen_response": "The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior.",
"rejected_response": "Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do."
}
```
## Use Cases
`Reward-Aira Dataset` can be utilized to train a reward/preference model.
## How to use
Available splits are `portuguese` and `english`.
```python
from datasets import load_dataset
dataset = load_dataset("nicholasKluge/reward-aira-dataset")
```
## Disclaimer
This dataset is provided as is, without any warranty or guarantee of its accuracy or suitability for any purpose. The creators and contributors of this dataset are not liable for any damages or losses arising from its use. Please review and comply with the licenses and terms of the original datasets before use.
|
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HausaNLP/NaijaSenti-Twitter
|
HausaNLP
| 2023-06-16T16:42:04Z | 79 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:hau",
"language:ibo",
"language:pcm",
"language:yor",
"license:cc-by-nc-sa-4.0",
"sentiment analysis, Twitter, tweets",
"sentiment",
"region:us"
] |
[
"text-classification"
] | 2023-06-16T08:49:27Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
language:
- hau
- ibo
- pcm
- yor
pretty_name: NaijaSenti
---
<p align="center">
<img src="https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/image/naijasenti_logo1.png", width="500">
--------------------------------------------------------------------------------
## Dataset Description
- **Homepage:** https://github.com/hausanlp/NaijaSenti
- **Repository:** [GitHub](https://github.com/hausanlp/NaijaSenti)
- **Paper:** [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://aclanthology.org/2022.lrec-1.63/)
- **Leaderboard:** N/A
- **Point of Contact:** [Shamsuddeen Hassan Muhammad]([email protected])
### Dataset Summary
NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
### Supported Tasks and Leaderboards
The NaijaSenti can be used for a wide range of sentiment analysis tasks in Nigerian languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages. It was part of the datasets that were used for [SemEval 2023 Task 12: Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320).
### Languages
4 most spoken Nigerian languages
* Hausa (hau)
* Igbo (ibo)
* Nigerian Pidgin (pcm)
* Yoruba (yor)
## Dataset Structure
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the NaijaSenti [dataset viewer](https://huggingface.co/datasets/HausaNLP/NaijaSenti-Twitter/viewer/hau/train) to explore more examples.
```
{
"tweet": "string",
"label": "string"
}
```
### Data Fields
The data fields are:
```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```
### Data Splits
The NaijaSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
| | hau | ibo | pcm | yor |
|---|---|---|---|---|
| train | 14,172 | 10,192 | 5,121 | 8,522 |
| dev | 2,677 | 1,841 | 1,281 | 2,090 |
| test | 5,303 | 3,682 | 4,154 | 4,515 |
| total | 22,152 | 15,715 | 10,556 | 15,127 |
### How to use it
```python
from datasets import load_dataset
# you can load specific languages (e.g., Hausa). This download train, validation and test sets.
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau")
# train set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "train")
# test set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "test")
# validation set only
ds = load_dataset("HausaNLP/NaijaSenti-Twitter", "hau", split = "validation")
```
## Dataset Creation
### Curation Rationale
NaijaSenti Version 1.0.0 aimed to be used sentiment analysis and other related task in Nigerian indigenous and creole languages - Hausa, Igbo, Nigerian Pidgin and Yoruba.
### Source Data
Twitter
### Personal and Sensitive Information
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
## Considerations for Using the Data
### Social Impact of Dataset
The NaijaSenti dataset has the potential to improve sentiment analysis for Nigerian languages, which is essential for understanding and analyzing the diverse perspectives of people in Nigeria. This dataset can enable researchers and developers to create sentiment analysis models that are specific to Nigerian languages, which can be used to gain insights into the social, cultural, and political views of people in Nigerian. Furthermore, this dataset can help address the issue of underrepresentation of Nigerian languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
## Additional Information
### Dataset Curators
* Shamsuddeen Hassan Muhammad
* Idris Abdulmumin
* Ibrahim Said Ahmad
* Bello Shehu Bello
### Licensing Information
This NaijaSenti is licensed under a Creative Commons Attribution BY-NC-SA 4.0 International License
### Citation Information
```
@inproceedings{muhammad-etal-2022-naijasenti,
title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
author = "Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Ruder, Sebastian and
Ahmad, Ibrahim Sa{'}id and
Abdulmumin, Idris and
Bello, Bello Shehu and
Choudhury, Monojit and
Emezue, Chris Chinenye and
Abdullahi, Saheed Salahudeen and
Aremu, Anuoluwapo and
Jorge, Al{\'\i}pio and
Brazdil, Pavel",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.63",
pages = "590--602",
}
```
### Contributions
> This work was carried out with support from Lacuna Fund, an initiative co-founded by The Rockefeller Foundation, Google.org, and Canada’s International Development Research Centre. The views expressed herein do not necessarily represent those of Lacuna Fund, its Steering Committee, its funders, or Meridian Institute.
|
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HumanCompatibleAI/ppo-CartPole-v1
|
HumanCompatibleAI
| 2023-07-18T14:43:49Z | 79 | 0 |
[
"region:us"
] | null | 2023-07-18T14:43:44Z |
---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 2103613
num_examples: 100
download_size: 1263834
dataset_size: 2103613
---
# Dataset Card for "ppo-CartPole-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Juzzy88/science_dict_full
|
Juzzy88
| 2023-08-05T08:20:01Z | 79 | 0 |
[
"region:us"
] | null | 2023-08-01T22:52:24Z |
---
dataset_info:
features:
- name: role_1
dtype: string
- name: topic;
dtype: string
- name: sub_topic
dtype: string
- name: message_1
dtype: string
- name: message_2
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 200646759
num_examples: 38400
- name: val
num_bytes: 50121062
num_examples: 9600
- name: test
num_bytes: 62653743
num_examples: 12000
download_size: 148930334
dataset_size: 313421564
---
# Dataset Card for "science_dict_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_b
|
open-llm-leaderboard
| 2023-10-17T14:57:03Z | 79 | 0 |
[
"region:us"
] | null | 2023-08-31T14:16:14Z |
---
pretty_name: Evaluation run of KnutJaegersberg/black_goo_recipe_b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/black_goo_recipe_b](https://huggingface.co/KnutJaegersberg/black_goo_recipe_b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T14:56:50.691599](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_b/blob/main/results_2023-10-17T14-56-50.691599.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.0002773614457335715,\n \"f1\": 0.05097630033557055,\n\
\ \"f1_stderr\": 0.0013271541576312406,\n \"acc\": 0.3192425320418652,\n\
\ \"acc_stderr\": 0.007133502794987516\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.0002773614457335715,\n\
\ \"f1\": 0.05097630033557055,\n \"f1_stderr\": 0.0013271541576312406\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225241\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6377269139700079,\n \"acc_stderr\": 0.013508855476252508\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/black_goo_recipe_b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|arc:challenge|25_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T14_56_50.691599
path:
- '**/details_harness|drop|3_2023-10-17T14-56-50.691599.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T14-56-50.691599.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T14_56_50.691599
path:
- '**/details_harness|gsm8k|5_2023-10-17T14-56-50.691599.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T14-56-50.691599.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hellaswag|10_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T14:15:51.764812.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T14:15:51.764812.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T14:15:51.764812.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T14_56_50.691599
path:
- '**/details_harness|winogrande|5_2023-10-17T14-56-50.691599.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T14-56-50.691599.parquet'
- config_name: results
data_files:
- split: 2023_08_31T14_15_51.764812
path:
- results_2023-08-31T14:15:51.764812.parquet
- split: 2023_10_17T14_56_50.691599
path:
- results_2023-10-17T14-56-50.691599.parquet
- split: latest
path:
- results_2023-10-17T14-56-50.691599.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/black_goo_recipe_b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/black_goo_recipe_b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/black_goo_recipe_b](https://huggingface.co/KnutJaegersberg/black_goo_recipe_b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T14:56:50.691599](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__black_goo_recipe_b/blob/main/results_2023-10-17T14-56-50.691599.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335715,
"f1": 0.05097630033557055,
"f1_stderr": 0.0013271541576312406,
"acc": 0.3192425320418652,
"acc_stderr": 0.007133502794987516
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335715,
"f1": 0.05097630033557055,
"f1_stderr": 0.0013271541576312406
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225241
},
"harness|winogrande|5": {
"acc": 0.6377269139700079,
"acc_stderr": 0.013508855476252508
}
}
```
### 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]
|
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] |
jphme/wikitext_de_document_level_v01
|
jphme
| 2023-09-21T13:08:52Z | 79 | 0 |
[
"region:us"
] | null | 2023-09-21T13:08:48Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1860002
num_examples: 200
download_size: 1138143
dataset_size: 1860002
---
# Dataset Card for "wikitext_de_document_level_v01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_baichuan-inc__Baichuan-7B
|
open-llm-leaderboard
| 2023-09-22T17:53:14Z | 79 | 0 |
[
"region:us"
] | null | 2023-09-22T17:53:06Z |
---
pretty_name: Evaluation run of baichuan-inc/Baichuan-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [baichuan-inc/Baichuan-7B](https://huggingface.co/baichuan-inc/Baichuan-7B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_baichuan-inc__Baichuan-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:53:01.811068](https://huggingface.co/datasets/open-llm-leaderboard/details_baichuan-inc__Baichuan-7B/blob/main/results_2023-09-22T17-53-01.811068.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.00033145814652192515,\n \"f1\": 0.05030096476510072,\n\
\ \"f1_stderr\": 0.001249643333921536,\n \"acc\": 0.34788528775895733,\n\
\ \"acc_stderr\": 0.00889327304403644\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192515,\n\
\ \"f1\": 0.05030096476510072,\n \"f1_stderr\": 0.001249643333921536\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.028051554207733132,\n \
\ \"acc_stderr\": 0.004548229533836359\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6677190213101816,\n \"acc_stderr\": 0.013238316554236521\n\
\ }\n}\n```"
repo_url: https://huggingface.co/baichuan-inc/Baichuan-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_53_01.811068
path:
- '**/details_harness|drop|3_2023-09-22T17-53-01.811068.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-53-01.811068.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_53_01.811068
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-53-01.811068.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-53-01.811068.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_53_01.811068
path:
- '**/details_harness|winogrande|5_2023-09-22T17-53-01.811068.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-53-01.811068.parquet'
- config_name: results
data_files:
- split: 2023_09_22T17_53_01.811068
path:
- results_2023-09-22T17-53-01.811068.parquet
- split: latest
path:
- results_2023-09-22T17-53-01.811068.parquet
---
# Dataset Card for Evaluation run of baichuan-inc/Baichuan-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/baichuan-inc/Baichuan-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [baichuan-inc/Baichuan-7B](https://huggingface.co/baichuan-inc/Baichuan-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_baichuan-inc__Baichuan-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:53:01.811068](https://huggingface.co/datasets/open-llm-leaderboard/details_baichuan-inc__Baichuan-7B/blob/main/results_2023-09-22T17-53-01.811068.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192515,
"f1": 0.05030096476510072,
"f1_stderr": 0.001249643333921536,
"acc": 0.34788528775895733,
"acc_stderr": 0.00889327304403644
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192515,
"f1": 0.05030096476510072,
"f1_stderr": 0.001249643333921536
},
"harness|gsm8k|5": {
"acc": 0.028051554207733132,
"acc_stderr": 0.004548229533836359
},
"harness|winogrande|5": {
"acc": 0.6677190213101816,
"acc_stderr": 0.013238316554236521
}
}
```
### 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]
|
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open-llm-leaderboard/details_pe-nlp__llama-2-13b-vicuna-wizard
|
open-llm-leaderboard
| 2023-10-12T18:46:52Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-12T18:46:43Z |
---
pretty_name: Evaluation run of pe-nlp/llama-2-13b-vicuna-wizard
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pe-nlp/llama-2-13b-vicuna-wizard](https://huggingface.co/pe-nlp/llama-2-13b-vicuna-wizard)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pe-nlp__llama-2-13b-vicuna-wizard\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T18:46:39.910816](https://huggingface.co/datasets/open-llm-leaderboard/details_pe-nlp__llama-2-13b-vicuna-wizard/blob/main/results_2023-10-12T18-46-39.910816.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3976510067114094,\n\
\ \"em_stderr\": 0.0050120430065395205,\n \"f1\": 0.43937709731543745,\n\
\ \"f1_stderr\": 0.004888666829236633,\n \"acc\": 0.3794502424345056,\n\
\ \"acc_stderr\": 0.007394168076612409\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3976510067114094,\n \"em_stderr\": 0.0050120430065395205,\n\
\ \"f1\": 0.43937709731543745,\n \"f1_stderr\": 0.004888666829236633\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \
\ \"acc_stderr\": 0.0026153265107756725\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.749802683504341,\n \"acc_stderr\": 0.012173009642449144\n\
\ }\n}\n```"
repo_url: https://huggingface.co/pe-nlp/llama-2-13b-vicuna-wizard
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T18_46_39.910816
path:
- '**/details_harness|drop|3_2023-10-12T18-46-39.910816.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T18-46-39.910816.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T18_46_39.910816
path:
- '**/details_harness|gsm8k|5_2023-10-12T18-46-39.910816.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T18-46-39.910816.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T18_46_39.910816
path:
- '**/details_harness|winogrande|5_2023-10-12T18-46-39.910816.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T18-46-39.910816.parquet'
- config_name: results
data_files:
- split: 2023_10_12T18_46_39.910816
path:
- results_2023-10-12T18-46-39.910816.parquet
- split: latest
path:
- results_2023-10-12T18-46-39.910816.parquet
---
# Dataset Card for Evaluation run of pe-nlp/llama-2-13b-vicuna-wizard
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pe-nlp/llama-2-13b-vicuna-wizard
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [pe-nlp/llama-2-13b-vicuna-wizard](https://huggingface.co/pe-nlp/llama-2-13b-vicuna-wizard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_pe-nlp__llama-2-13b-vicuna-wizard",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T18:46:39.910816](https://huggingface.co/datasets/open-llm-leaderboard/details_pe-nlp__llama-2-13b-vicuna-wizard/blob/main/results_2023-10-12T18-46-39.910816.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3976510067114094,
"em_stderr": 0.0050120430065395205,
"f1": 0.43937709731543745,
"f1_stderr": 0.004888666829236633,
"acc": 0.3794502424345056,
"acc_stderr": 0.007394168076612409
},
"harness|drop|3": {
"em": 0.3976510067114094,
"em_stderr": 0.0050120430065395205,
"f1": 0.43937709731543745,
"f1_stderr": 0.004888666829236633
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.0026153265107756725
},
"harness|winogrande|5": {
"acc": 0.749802683504341,
"acc_stderr": 0.012173009642449144
}
}
```
### 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]
|
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open-llm-leaderboard/details_WizardLM__WizardMath-13B-V1.0
|
open-llm-leaderboard
| 2023-10-12T22:46:05Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-12T22:45:57Z |
---
pretty_name: Evaluation run of WizardLM/WizardMath-13B-V1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [WizardLM/WizardMath-13B-V1.0](https://huggingface.co/WizardLM/WizardMath-13B-V1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_WizardLM__WizardMath-13B-V1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T22:45:52.861079](https://huggingface.co/datasets/open-llm-leaderboard/details_WizardLM__WizardMath-13B-V1.0/blob/main/results_2023-10-12T22-45-52.861079.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0024119127516778523,\n\
\ \"em_stderr\": 0.0005023380498893313,\n \"f1\": 0.07075817953020154,\n\
\ \"f1_stderr\": 0.0015254513833319102,\n \"acc\": 0.4212998893591507,\n\
\ \"acc_stderr\": 0.010848795701326375\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893313,\n\
\ \"f1\": 0.07075817953020154,\n \"f1_stderr\": 0.0015254513833319102\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12357846853677028,\n \
\ \"acc_stderr\": 0.009065050306776925\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7190213101815311,\n \"acc_stderr\": 0.012632541095875825\n\
\ }\n}\n```"
repo_url: https://huggingface.co/WizardLM/WizardMath-13B-V1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T22_45_52.861079
path:
- '**/details_harness|drop|3_2023-10-12T22-45-52.861079.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T22-45-52.861079.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T22_45_52.861079
path:
- '**/details_harness|gsm8k|5_2023-10-12T22-45-52.861079.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T22-45-52.861079.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T22_45_52.861079
path:
- '**/details_harness|winogrande|5_2023-10-12T22-45-52.861079.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T22-45-52.861079.parquet'
- config_name: results
data_files:
- split: 2023_10_12T22_45_52.861079
path:
- results_2023-10-12T22-45-52.861079.parquet
- split: latest
path:
- results_2023-10-12T22-45-52.861079.parquet
---
# Dataset Card for Evaluation run of WizardLM/WizardMath-13B-V1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/WizardLM/WizardMath-13B-V1.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [WizardLM/WizardMath-13B-V1.0](https://huggingface.co/WizardLM/WizardMath-13B-V1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_WizardLM__WizardMath-13B-V1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T22:45:52.861079](https://huggingface.co/datasets/open-llm-leaderboard/details_WizardLM__WizardMath-13B-V1.0/blob/main/results_2023-10-12T22-45-52.861079.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0024119127516778523,
"em_stderr": 0.0005023380498893313,
"f1": 0.07075817953020154,
"f1_stderr": 0.0015254513833319102,
"acc": 0.4212998893591507,
"acc_stderr": 0.010848795701326375
},
"harness|drop|3": {
"em": 0.0024119127516778523,
"em_stderr": 0.0005023380498893313,
"f1": 0.07075817953020154,
"f1_stderr": 0.0015254513833319102
},
"harness|gsm8k|5": {
"acc": 0.12357846853677028,
"acc_stderr": 0.009065050306776925
},
"harness|winogrande|5": {
"acc": 0.7190213101815311,
"acc_stderr": 0.012632541095875825
}
}
```
### 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]
|
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open-llm-leaderboard/details_bigscience__bloomz-560m
|
open-llm-leaderboard
| 2023-12-04T12:37:21Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-13T02:59:41Z |
---
pretty_name: Evaluation run of bigscience/bloomz-560m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 8 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_bigscience__bloomz-560m\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.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/bigscience/bloomz-560m
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|gsm8k|5_2023-10-13T02-59-38.387630.parquet'
- split: 2023_12_03T14_34_05.520160
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-34-05.520160.parquet'
- split: 2023_12_03T14_34_17.552843
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-34-17.552843.parquet'
- split: 2023_12_03T15_36_24.223775
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-36-24.223775.parquet'
- split: 2023_12_03T15_36_26.532570
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-36-26.532570.parquet'
- split: 2023_12_04T09_27_25.322225
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-27-25.322225.parquet'
- split: 2023_12_04T12_37_10.556639
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-10.556639.parquet'
- split: 2023_12_04T12_37_15.813527
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- '**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet'
- config_name: results
data_files:
- split: 2023_10_13T02_59_38.387630
path:
- results_2023-10-13T02-59-38.387630.parquet
- split: 2023_12_03T14_34_05.520160
path:
- results_2023-12-03T14-34-05.520160.parquet
- split: 2023_12_03T14_34_17.552843
path:
- results_2023-12-03T14-34-17.552843.parquet
- split: 2023_12_03T15_36_24.223775
path:
- results_2023-12-03T15-36-24.223775.parquet
- split: 2023_12_03T15_36_26.532570
path:
- results_2023-12-03T15-36-26.532570.parquet
- split: 2023_12_04T09_27_25.322225
path:
- results_2023-12-04T09-27-25.322225.parquet
- split: 2023_12_04T12_37_10.556639
path:
- results_2023-12-04T12-37-10.556639.parquet
- split: 2023_12_04T12_37_15.813527
path:
- results_2023-12-04T12-37-15.813527.parquet
- split: latest
path:
- results_2023-12-04T12-37-15.813527.parquet
---
# Dataset Card for Evaluation run of bigscience/bloomz-560m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-560m
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 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_bigscience__bloomz-560m",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.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]
|
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open-llm-leaderboard/details_meta-llama__Llama-2-7b-chat-hf
|
open-llm-leaderboard
| 2023-10-15T02:34:27Z | 79 | 1 |
[
"region:us"
] | null | 2023-10-15T02:34:19Z |
---
pretty_name: Evaluation run of meta-llama/Llama-2-7b-chat-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-llama__Llama-2-7b-chat-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T02:34:15.484281](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-7b-chat-hf/blob/main/results_2023-10-15T02-34-15.484281.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.06763842281879194,\n\
\ \"em_stderr\": 0.0025717489509556085,\n \"f1\": 0.13085570469798627,\n\
\ \"f1_stderr\": 0.0028825856446422905,\n \"acc\": 0.39549166962367155,\n\
\ \"acc_stderr\": 0.009921949302668327\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.06763842281879194,\n \"em_stderr\": 0.0025717489509556085,\n\
\ \"f1\": 0.13085570469798627,\n \"f1_stderr\": 0.0028825856446422905\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07354056103108415,\n \
\ \"acc_stderr\": 0.0071898357543652685\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7174427782162589,\n \"acc_stderr\": 0.012654062850971384\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T02_34_15.484281
path:
- '**/details_harness|drop|3_2023-10-15T02-34-15.484281.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T02-34-15.484281.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T02_34_15.484281
path:
- '**/details_harness|gsm8k|5_2023-10-15T02-34-15.484281.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T02-34-15.484281.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T02_34_15.484281
path:
- '**/details_harness|winogrande|5_2023-10-15T02-34-15.484281.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T02-34-15.484281.parquet'
- config_name: results
data_files:
- split: 2023_10_15T02_34_15.484281
path:
- results_2023-10-15T02-34-15.484281.parquet
- split: latest
path:
- results_2023-10-15T02-34-15.484281.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-7b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_meta-llama__Llama-2-7b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T02:34:15.484281](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-7b-chat-hf/blob/main/results_2023-10-15T02-34-15.484281.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.06763842281879194,
"em_stderr": 0.0025717489509556085,
"f1": 0.13085570469798627,
"f1_stderr": 0.0028825856446422905,
"acc": 0.39549166962367155,
"acc_stderr": 0.009921949302668327
},
"harness|drop|3": {
"em": 0.06763842281879194,
"em_stderr": 0.0025717489509556085,
"f1": 0.13085570469798627,
"f1_stderr": 0.0028825856446422905
},
"harness|gsm8k|5": {
"acc": 0.07354056103108415,
"acc_stderr": 0.0071898357543652685
},
"harness|winogrande|5": {
"acc": 0.7174427782162589,
"acc_stderr": 0.012654062850971384
}
}
```
### 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]
|
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atom92/medical_healthwa_2.0
|
atom92
| 2023-10-15T12:41:21Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-15T12:41:19Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1544703
num_examples: 7360
download_size: 501374
dataset_size: 1544703
---
# Dataset Card for "medical_healthwa_2.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_The-Face-Of-Goonery__Huginn-v3-13b
|
open-llm-leaderboard
| 2023-10-16T05:28:21Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-16T05:28:13Z |
---
pretty_name: Evaluation run of The-Face-Of-Goonery/Huginn-v3-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [The-Face-Of-Goonery/Huginn-v3-13b](https://huggingface.co/The-Face-Of-Goonery/Huginn-v3-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_The-Face-Of-Goonery__Huginn-v3-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T05:28:09.073903](https://huggingface.co/datasets/open-llm-leaderboard/details_The-Face-Of-Goonery__Huginn-v3-13b/blob/main/results_2023-10-16T05-28-09.073903.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.06354865771812081,\n\
\ \"em_stderr\": 0.002498247436471722,\n \"f1\": 0.14479865771812028,\n\
\ \"f1_stderr\": 0.002890194024794147,\n \"acc\": 0.3913161593683,\n\
\ \"acc_stderr\": 0.009083920481175163\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.06354865771812081,\n \"em_stderr\": 0.002498247436471722,\n\
\ \"f1\": 0.14479865771812028,\n \"f1_stderr\": 0.002890194024794147\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \
\ \"acc_stderr\": 0.005784991662691864\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7363851617995264,\n \"acc_stderr\": 0.01238284929965846\n\
\ }\n}\n```"
repo_url: https://huggingface.co/The-Face-Of-Goonery/Huginn-v3-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T05_28_09.073903
path:
- '**/details_harness|drop|3_2023-10-16T05-28-09.073903.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T05-28-09.073903.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T05_28_09.073903
path:
- '**/details_harness|gsm8k|5_2023-10-16T05-28-09.073903.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T05-28-09.073903.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T05_28_09.073903
path:
- '**/details_harness|winogrande|5_2023-10-16T05-28-09.073903.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T05-28-09.073903.parquet'
- config_name: results
data_files:
- split: 2023_10_16T05_28_09.073903
path:
- results_2023-10-16T05-28-09.073903.parquet
- split: latest
path:
- results_2023-10-16T05-28-09.073903.parquet
---
# Dataset Card for Evaluation run of The-Face-Of-Goonery/Huginn-v3-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/The-Face-Of-Goonery/Huginn-v3-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [The-Face-Of-Goonery/Huginn-v3-13b](https://huggingface.co/The-Face-Of-Goonery/Huginn-v3-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_The-Face-Of-Goonery__Huginn-v3-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T05:28:09.073903](https://huggingface.co/datasets/open-llm-leaderboard/details_The-Face-Of-Goonery__Huginn-v3-13b/blob/main/results_2023-10-16T05-28-09.073903.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.06354865771812081,
"em_stderr": 0.002498247436471722,
"f1": 0.14479865771812028,
"f1_stderr": 0.002890194024794147,
"acc": 0.3913161593683,
"acc_stderr": 0.009083920481175163
},
"harness|drop|3": {
"em": 0.06354865771812081,
"em_stderr": 0.002498247436471722,
"f1": 0.14479865771812028,
"f1_stderr": 0.002890194024794147
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.005784991662691864
},
"harness|winogrande|5": {
"acc": 0.7363851617995264,
"acc_stderr": 0.01238284929965846
}
}
```
### 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]
|
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open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf
|
open-llm-leaderboard
| 2023-10-17T05:07:55Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-17T05:07:46Z |
---
pretty_name: Evaluation run of meta-llama/Llama-2-70b-chat-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T05:07:42.486452](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf/blob/main/results_2023-10-17T05-07-42.486452.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.040373322147651006,\n\
\ \"em_stderr\": 0.0020157564185176837,\n \"f1\": 0.1050272651006715,\n\
\ \"f1_stderr\": 0.0023756238577676155,\n \"acc\": 0.5359600711595986,\n\
\ \"acc_stderr\": 0.011658939983913113\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.040373322147651006,\n \"em_stderr\": 0.0020157564185176837,\n\
\ \"f1\": 0.1050272651006715,\n \"f1_stderr\": 0.0023756238577676155\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.266868840030326,\n \
\ \"acc_stderr\": 0.012183780551887957\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938268\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- '**/details_harness|drop|3_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T05-07-42.486452.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- '**/details_harness|gsm8k|5_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T05-07-42.486452.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- '**/details_harness|winogrande|5_2023-10-17T05-07-42.486452.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T05-07-42.486452.parquet'
- config_name: results
data_files:
- split: 2023_10_17T05_07_42.486452
path:
- results_2023-10-17T05-07-42.486452.parquet
- split: latest
path:
- results_2023-10-17T05-07-42.486452.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-70b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T05:07:42.486452](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-70b-chat-hf/blob/main/results_2023-10-17T05-07-42.486452.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.040373322147651006,
"em_stderr": 0.0020157564185176837,
"f1": 0.1050272651006715,
"f1_stderr": 0.0023756238577676155,
"acc": 0.5359600711595986,
"acc_stderr": 0.011658939983913113
},
"harness|drop|3": {
"em": 0.040373322147651006,
"em_stderr": 0.0020157564185176837,
"f1": 0.1050272651006715,
"f1_stderr": 0.0023756238577676155
},
"harness|gsm8k|5": {
"acc": 0.266868840030326,
"acc_stderr": 0.012183780551887957
},
"harness|winogrande|5": {
"acc": 0.8050513022888713,
"acc_stderr": 0.011134099415938268
}
}
```
### 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]
|
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open-llm-leaderboard/details_Aeala__VicUnlocked-alpaca-30b
|
open-llm-leaderboard
| 2023-10-17T18:02:33Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-17T18:02:24Z |
---
pretty_name: Evaluation run of Aeala/VicUnlocked-alpaca-30b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aeala/VicUnlocked-alpaca-30b](https://huggingface.co/Aeala/VicUnlocked-alpaca-30b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aeala__VicUnlocked-alpaca-30b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T18:02:20.593503](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__VicUnlocked-alpaca-30b/blob/main/results_2023-10-17T18-02-20.593503.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.011849832214765101,\n\
\ \"em_stderr\": 0.0011081721365098474,\n \"f1\": 0.07360528523489944,\n\
\ \"f1_stderr\": 0.0016918412800750494,\n \"acc\": 0.4642427803704344,\n\
\ \"acc_stderr\": 0.010668138318862291\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.011849832214765101,\n \"em_stderr\": 0.0011081721365098474,\n\
\ \"f1\": 0.07360528523489944,\n \"f1_stderr\": 0.0016918412800750494\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1463229719484458,\n \
\ \"acc_stderr\": 0.00973521055778526\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aeala/VicUnlocked-alpaca-30b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T18_02_20.593503
path:
- '**/details_harness|drop|3_2023-10-17T18-02-20.593503.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T18-02-20.593503.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T18_02_20.593503
path:
- '**/details_harness|gsm8k|5_2023-10-17T18-02-20.593503.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T18-02-20.593503.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T18_02_20.593503
path:
- '**/details_harness|winogrande|5_2023-10-17T18-02-20.593503.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T18-02-20.593503.parquet'
- config_name: results
data_files:
- split: 2023_10_17T18_02_20.593503
path:
- results_2023-10-17T18-02-20.593503.parquet
- split: latest
path:
- results_2023-10-17T18-02-20.593503.parquet
---
# Dataset Card for Evaluation run of Aeala/VicUnlocked-alpaca-30b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aeala/VicUnlocked-alpaca-30b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aeala/VicUnlocked-alpaca-30b](https://huggingface.co/Aeala/VicUnlocked-alpaca-30b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aeala__VicUnlocked-alpaca-30b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T18:02:20.593503](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__VicUnlocked-alpaca-30b/blob/main/results_2023-10-17T18-02-20.593503.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.011849832214765101,
"em_stderr": 0.0011081721365098474,
"f1": 0.07360528523489944,
"f1_stderr": 0.0016918412800750494,
"acc": 0.4642427803704344,
"acc_stderr": 0.010668138318862291
},
"harness|drop|3": {
"em": 0.011849832214765101,
"em_stderr": 0.0011081721365098474,
"f1": 0.07360528523489944,
"f1_stderr": 0.0016918412800750494
},
"harness|gsm8k|5": {
"acc": 0.1463229719484458,
"acc_stderr": 0.00973521055778526
},
"harness|winogrande|5": {
"acc": 0.7821625887924231,
"acc_stderr": 0.011601066079939324
}
}
```
### 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]
|
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open-llm-leaderboard/details_quantumaikr__llama-2-7b-hf-guanaco-1k
|
open-llm-leaderboard
| 2023-10-17T19:26:46Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-17T19:26:38Z |
---
pretty_name: Evaluation run of quantumaikr/llama-2-7b-hf-guanaco-1k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [quantumaikr/llama-2-7b-hf-guanaco-1k](https://huggingface.co/quantumaikr/llama-2-7b-hf-guanaco-1k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_quantumaikr__llama-2-7b-hf-guanaco-1k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T19:26:34.289625](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-7b-hf-guanaco-1k/blob/main/results_2023-10-17T19-26-34.289625.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002726510067114094,\n\
\ \"em_stderr\": 0.0005340111700415914,\n \"f1\": 0.056623322147651096,\n\
\ \"f1_stderr\": 0.0013885957029727636,\n \"acc\": 0.40100097356766773,\n\
\ \"acc_stderr\": 0.009867271082149756\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415914,\n\
\ \"f1\": 0.056623322147651096,\n \"f1_stderr\": 0.0013885957029727636\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07429871114480667,\n \
\ \"acc_stderr\": 0.007223844172845574\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453937\n\
\ }\n}\n```"
repo_url: https://huggingface.co/quantumaikr/llama-2-7b-hf-guanaco-1k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T19_26_34.289625
path:
- '**/details_harness|drop|3_2023-10-17T19-26-34.289625.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T19-26-34.289625.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T19_26_34.289625
path:
- '**/details_harness|gsm8k|5_2023-10-17T19-26-34.289625.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T19-26-34.289625.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T19_26_34.289625
path:
- '**/details_harness|winogrande|5_2023-10-17T19-26-34.289625.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T19-26-34.289625.parquet'
- config_name: results
data_files:
- split: 2023_10_17T19_26_34.289625
path:
- results_2023-10-17T19-26-34.289625.parquet
- split: latest
path:
- results_2023-10-17T19-26-34.289625.parquet
---
# Dataset Card for Evaluation run of quantumaikr/llama-2-7b-hf-guanaco-1k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/quantumaikr/llama-2-7b-hf-guanaco-1k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [quantumaikr/llama-2-7b-hf-guanaco-1k](https://huggingface.co/quantumaikr/llama-2-7b-hf-guanaco-1k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_quantumaikr__llama-2-7b-hf-guanaco-1k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T19:26:34.289625](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-7b-hf-guanaco-1k/blob/main/results_2023-10-17T19-26-34.289625.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415914,
"f1": 0.056623322147651096,
"f1_stderr": 0.0013885957029727636,
"acc": 0.40100097356766773,
"acc_stderr": 0.009867271082149756
},
"harness|drop|3": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415914,
"f1": 0.056623322147651096,
"f1_stderr": 0.0013885957029727636
},
"harness|gsm8k|5": {
"acc": 0.07429871114480667,
"acc_stderr": 0.007223844172845574
},
"harness|winogrande|5": {
"acc": 0.7277032359905288,
"acc_stderr": 0.012510697991453937
}
}
```
### 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]
|
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open-llm-leaderboard/details_quantumaikr__QuantumLM-7B
|
open-llm-leaderboard
| 2023-10-18T01:13:20Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-18T01:13:11Z |
---
pretty_name: Evaluation run of quantumaikr/QuantumLM-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [quantumaikr/QuantumLM-7B](https://huggingface.co/quantumaikr/QuantumLM-7B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_quantumaikr__QuantumLM-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T01:13:07.754865](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__QuantumLM-7B/blob/main/results_2023-10-18T01-13-07.754865.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003355704697986577,\n\
\ \"em_stderr\": 0.0005922452850005221,\n \"f1\": 0.05992030201342302,\n\
\ \"f1_stderr\": 0.001439479348001652,\n \"acc\": 0.39582407087716237,\n\
\ \"acc_stderr\": 0.0100052755032964\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003355704697986577,\n \"em_stderr\": 0.0005922452850005221,\n\
\ \"f1\": 0.05992030201342302,\n \"f1_stderr\": 0.001439479348001652\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07657316148597422,\n \
\ \"acc_stderr\": 0.007324564881451574\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7150749802683505,\n \"acc_stderr\": 0.012685986125141227\n\
\ }\n}\n```"
repo_url: https://huggingface.co/quantumaikr/QuantumLM-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T01_13_07.754865
path:
- '**/details_harness|drop|3_2023-10-18T01-13-07.754865.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T01-13-07.754865.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T01_13_07.754865
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-13-07.754865.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-13-07.754865.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T01_13_07.754865
path:
- '**/details_harness|winogrande|5_2023-10-18T01-13-07.754865.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T01-13-07.754865.parquet'
- config_name: results
data_files:
- split: 2023_10_18T01_13_07.754865
path:
- results_2023-10-18T01-13-07.754865.parquet
- split: latest
path:
- results_2023-10-18T01-13-07.754865.parquet
---
# Dataset Card for Evaluation run of quantumaikr/QuantumLM-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/quantumaikr/QuantumLM-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [quantumaikr/QuantumLM-7B](https://huggingface.co/quantumaikr/QuantumLM-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_quantumaikr__QuantumLM-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T01:13:07.754865](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__QuantumLM-7B/blob/main/results_2023-10-18T01-13-07.754865.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003355704697986577,
"em_stderr": 0.0005922452850005221,
"f1": 0.05992030201342302,
"f1_stderr": 0.001439479348001652,
"acc": 0.39582407087716237,
"acc_stderr": 0.0100052755032964
},
"harness|drop|3": {
"em": 0.003355704697986577,
"em_stderr": 0.0005922452850005221,
"f1": 0.05992030201342302,
"f1_stderr": 0.001439479348001652
},
"harness|gsm8k|5": {
"acc": 0.07657316148597422,
"acc_stderr": 0.007324564881451574
},
"harness|winogrande|5": {
"acc": 0.7150749802683505,
"acc_stderr": 0.012685986125141227
}
}
```
### 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]
|
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open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k
|
open-llm-leaderboard
| 2023-10-18T04:31:36Z | 79 | 0 |
[
"region:us"
] | null | 2023-10-18T04:31:27Z |
---
pretty_name: Evaluation run of Open-Orca/LlongOrca-7B-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Open-Orca/LlongOrca-7B-16k](https://huggingface.co/Open-Orca/LlongOrca-7B-16k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T04:31:23.491817](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k/blob/main/results_2023-10-18T04-31-23.491817.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.016988255033557047,\n\
\ \"em_stderr\": 0.0013234068882109723,\n \"f1\": 0.08061136744966452,\n\
\ \"f1_stderr\": 0.001896831507875326,\n \"acc\": 0.4100619744335266,\n\
\ \"acc_stderr\": 0.009753220057431532\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.016988255033557047,\n \"em_stderr\": 0.0013234068882109723,\n\
\ \"f1\": 0.08061136744966452,\n \"f1_stderr\": 0.001896831507875326\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07505686125852919,\n \
\ \"acc_stderr\": 0.007257633145486642\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Open-Orca/LlongOrca-7B-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|drop|3_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T04-31-23.491817.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|gsm8k|5_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T04-31-23.491817.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|winogrande|5_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T04-31-23.491817.parquet'
- config_name: results
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- results_2023-10-18T04-31-23.491817.parquet
- split: latest
path:
- results_2023-10-18T04-31-23.491817.parquet
---
# Dataset Card for Evaluation run of Open-Orca/LlongOrca-7B-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Open-Orca/LlongOrca-7B-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Open-Orca/LlongOrca-7B-16k](https://huggingface.co/Open-Orca/LlongOrca-7B-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T04:31:23.491817](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k/blob/main/results_2023-10-18T04-31-23.491817.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.016988255033557047,
"em_stderr": 0.0013234068882109723,
"f1": 0.08061136744966452,
"f1_stderr": 0.001896831507875326,
"acc": 0.4100619744335266,
"acc_stderr": 0.009753220057431532
},
"harness|drop|3": {
"em": 0.016988255033557047,
"em_stderr": 0.0013234068882109723,
"f1": 0.08061136744966452,
"f1_stderr": 0.001896831507875326
},
"harness|gsm8k|5": {
"acc": 0.07505686125852919,
"acc_stderr": 0.007257633145486642
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### 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]
|
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] |
sabilmakbar/indo_wiki
|
sabilmakbar
| 2023-11-03T07:59:24Z | 79 | 0 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:Wikipedia-HF",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:gor",
"language:id",
"language:jv",
"language:mis",
"language:min",
"language:ms",
"language:nia",
"language:su",
"language:tet",
"license:cc-by-sa-3.0",
"license:gfdl",
"Wikipedia",
"Indonesian",
"Sundanese",
"Javanese",
"Malay",
"Dialect",
"Javanese Dialect (Banyumase/Ngapak)",
"Indonesian Language",
"Malay Language",
"Indonesia-related Languages",
"Indonesian Local Languages",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-11-03T06:49:33Z |
---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- ace
- ban
- bjn
- bug
- gor
- id
- jv
- mis
- min
- ms
- nia
- su
- tet
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
source_datasets:
- Wikipedia-HF
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: Wikipedia Archive for Indonesian Languages & Local Languages
tags:
- Wikipedia
- Indonesian
- Sundanese
- Javanese
- Malay
- Dialect
- Javanese Dialect (Banyumase/Ngapak)
- Indonesian Language
- Malay Language
- Indonesia-related Languages
- Indonesian Local Languages
dataset_info:
- config_name: indowiki_all
features:
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: ace
num_bytes: 4875688
num_examples: 12932
- name: ban
num_bytes: 17561379
num_examples: 20243
- name: bjn
num_bytes: 6669628
num_examples: 10460
- name: bug
num_bytes: 3297641
num_examples: 15877
- name: gor
num_bytes: 6007726
num_examples: 14572
- name: id
num_bytes: 1103106307
num_examples: 657990
- name: jv
num_bytes: 70335030
num_examples: 73150
- name: map_bms
num_bytes: 5215803
num_examples: 13574
- name: min
num_bytes: 116481049
num_examples: 227024
- name: ms
num_bytes: 416001194
num_examples: 367463
- name: nia
num_bytes: 1938378
num_examples: 1651
- name: su
num_bytes: 47489084
num_examples: 61557
- name: tet
num_bytes: 1452716
num_examples: 1465
download_size: 1803193334
dataset_size: 1800431623
- config_name: indowiki_dedup_all
features:
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: ace
num_bytes: 4867838
num_examples: 12904
- name: ban
num_bytes: 17366080
num_examples: 19837
- name: bjn
num_bytes: 6655378
num_examples: 10437
- name: bug
num_bytes: 2072609
num_examples: 9793
- name: gor
num_bytes: 5989252
num_examples: 14514
- name: id
num_bytes: 1100932403
num_examples: 654287
- name: jv
num_bytes: 69774853
num_examples: 72667
- name: map_bms
num_bytes: 5060989
num_examples: 11832
- name: min
num_bytes: 116376870
num_examples: 225858
- name: ms
num_bytes: 410443550
num_examples: 346186
- name: nia
num_bytes: 1938121
num_examples: 1650
- name: su
num_bytes: 47410439
num_examples: 61494
- name: tet
num_bytes: 1447926
num_examples: 1460
download_size: 1793103024
dataset_size: 1790336308
- config_name: indowiki_dedup_id_only
features:
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1100932403
num_examples: 654287
download_size: 1103131493
dataset_size: 1100932403
---
# **Indonesian Wikipedia Data Repository**
---
license: cc-by-sa-3.0
---
Welcome to Indonesian Wikipedia Data Repository. The datasets are extracted from [Wikipedia HF](https://huggingface.co/datasets/wikipedia) and processed using the scripts available in this repository for reproducibility purpose.
# **FAQS**
### What are the available languages provided in dataset?
Please check the following table.
| Lang Code | Lang Desc | Wiki Info | Total Data | Total Size (bytes) |
| :---: | :----: | :--- | ---: | ---: |
| ace | Acehnese | [Wiki Link](https://en.wikipedia.org/wiki/Acehnese_language) | 12904 | 4867838 |
| ban | Balinese | [Wiki Link](https://en.wikipedia.org/wiki/Balinese_language) | 19837 | 17366080 |
| bjn | Acehnese | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 10437 | 6655378 |
| bug | Buginese | [Wiki Link](https://en.wikipedia.org/wiki/Buginese_language) | 9793 | 2072609 |
| gor | Gorontalo | [Wiki Link](https://en.wikipedia.org/wiki/Gorontalo_language) | 14514 | 5989252 |
| id | Indonesian | [Wiki Link](https://en.wikipedia.org/wiki/Indonesian_language) | 654287 | 1100932403 |
| jv | Javanese | [Wiki Link](https://en.wikipedia.org/wiki/Javanese_language) | 72667 | 69774853 |
| map_bms | Banyumasan <br />(Dialect of Javanese) | [Wiki Link](https://en.wikipedia.org/wiki/Banyumasan_dialect) | 11832 | 5060989 |
| min | Minangkabau | [Wiki Link](https://en.wikipedia.org/wiki/Minangkabau_language) | 225858 | 116376870 |
| ms | Malay | [Wiki Link](https://en.wikipedia.org/wiki/Malay_language) | 346186 | 410443550 |
| nia | Nias | [Wiki Link](https://en.wikipedia.org/wiki/Nias_language) | 1650 | 1938121 |
| su | Sundanese | [Wiki Link](https://en.wikipedia.org/wiki/Sundanese_language) | 61494 | 47410439 |
| tet | Tetum | [Wiki Link](https://en.wikipedia.org/wiki/Tetum_language) | 1465 | 1452716 |
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may check to the script [_```extract_raw_wiki_data.py```_](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/extract_raw_wiki_data.py) to understand its implementations, or you can adjust the bash provided in [_```extract_raw_wiki_data_indo.sh```_](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/extract_raw_wiki_data_indo.sh) to extract it on your own. Please note that this dataset is extensible to any languages of your choice.
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may visit this [Wikipedia Dump Index](https://dumps.wikimedia.org/backup-index.html) to check any latest available data and this link [Wikipedia Language Coverage](https://meta.wikimedia.org/wiki/List_of_Wikipedias#All_Wikipedias_ordered_by_number_of_articles) to map into any languages that you're wanting to extract.
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on ```title``` and ```text``` (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for no-available informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars validated). You may check this [ ```cleanse_wiki_data.py```](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/cleanse_wiki_data.py) script to understand its implementation.
# Getting Started #
### To read the datasets directly ###
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of ```config name``` using the following script
```
dataset = load_dataset(
"sabilmakbar/indo_wiki",
"indo_wiki_dedup_data" # a config name, can be "indo_wiki_raw_data" or "indowiki_dedup_id_only", defaults to "indo_wiki_dedup_data"
)
```
Or you can provide both ```lang``` and ```date_stamp``` (providing only one will thrown an error)
```
dataset = load_dataset(
"sabilmakbar/indo_wiki",
lang = "id", # see the splits for complete lang choices
date_stamp="20230901"
)
```
### To replicate the whole dataset generation process ###
1. Set-up a new Python/Conda Environment (recommended Python version: 3.9.6 to 3.9.18 or 3.10.0 to 3.10.13) and install the requirements on ```requirements.txt``` use this codebase via ```pip install -r requirements.txt```.
2. Activate the chosen Python/Conda environment which the requirements are being installed.
3. Run this ```sh``` script for extractions from Wikimedia Dump:
```sh extract_raw_wiki_data_indo.sh```.
4. Run this ```sh``` script of deduplication:
```sh dedup_raw_wiki_data_indo.sh```.
## Citation Info:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"}
@ONLINE{wikipedia-hf,
title = "Huggingface Wikipedia Dataset",
url = "https://huggingface.co/datasets/wikipedia"}
```
|
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Flyfer/CBDataSetBeta1
|
Flyfer
| 2023-11-22T12:39:56Z | 79 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-03T13:00:48Z |
---
license: apache-2.0
---
|
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atmallen/qm_alice_grader_first_1.0e
|
atmallen
| 2023-11-16T18:27:00Z | 79 | 0 |
[
"region:us"
] | null | 2023-11-16T03:18:57Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 17970044.0
num_examples: 200000
- name: validation
num_bytes: 1801418.0
num_examples: 20000
- name: test
num_bytes: 1802170.0
num_examples: 20000
download_size: 0
dataset_size: 21573632.0
---
# Dataset Card for "qm_alice__grader_first_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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SonishMaharjan/male-female
|
SonishMaharjan
| 2023-11-22T06:01:51Z | 79 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-22T04:40:32Z |
---
license: unknown
---
|
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] |
Tidrael/tsl_news
|
Tidrael
| 2022-10-10T14:23:36Z | 78 | 1 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2022-10-06T04:47:14Z |
---
annotations_creators: []
language:
- en
language_creators:
- machine-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: bussiness-news
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for [Dataset Name]
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Top news headline in finance from bbc-news
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Sentiment label: Using threshold below 0 is negative (0) and above 0 is positive (1)
[More Information Needed]
### Data Splits
Train/Split Ratio is 0.9/0.1
## 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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MBZUAI/LaMini-Hallucination
|
MBZUAI
| 2023-05-23T18:39:09Z | 78 | 3 |
[
"region:us"
] | null | 2023-05-23T18:39:01Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: category
dtype: string
splits:
- name: test
num_bytes: 2785
num_examples: 40
download_size: 3220
dataset_size: 2785
---
# Dataset Card for "LaMini-Hallucination"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
|
open-llm-leaderboard
| 2023-10-18T01:14:20Z | 78 | 0 |
[
"region:us"
] | null | 2023-08-28T22:57:04Z |
---
pretty_name: Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08420721476510067,\n\
\ \"em_stderr\": 0.0028438907694585103,\n \"f1\": 0.20487206375838948,\n\
\ \"f1_stderr\": 0.0032246591490556827,\n \"acc\": 0.35556432517758485,\n\
\ \"acc_stderr\": 0.006369120635509223\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08420721476510067,\n \"em_stderr\": 0.0028438907694585103,\n\
\ \"f1\": 0.20487206375838948,\n \"f1_stderr\": 0.0032246591490556827\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7111286503551697,\n\
\ \"acc_stderr\": 0.012738241271018446\n }\n}\n```"
repo_url: https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T01_14_07.335372
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet'
- config_name: results
data_files:
- split: 2023_08_28T22_56_04.264879
path:
- results_2023-08-28T22:56:04.264879.parquet
- split: 2023_10_18T01_14_07.335372
path:
- results_2023-10-18T01-14-07.335372.parquet
- split: latest
path:
- results_2023-10-18T01-14-07.335372.parquet
---
# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827,
"acc": 0.35556432517758485,
"acc_stderr": 0.006369120635509223
},
"harness|drop|3": {
"em": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018446
}
}
```
### 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
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## Additional Information
### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
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|
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ds4sd/SynthTabNet_OTSL
|
ds4sd
| 2023-08-31T17:14:02Z | 78 | 1 |
[
"task_categories:object-detection",
"task_categories:table-to-text",
"size_categories:10K<n<100K",
"license:other",
"table-structure-recognition",
"table-understanding",
"PDF",
"arxiv:2305.03393",
"region:us"
] |
[
"object-detection",
"table-to-text"
] | 2023-08-31T16:07:02Z |
---
license: other
pretty_name: SynthTabNet-OTSL
size_categories:
- 10K<n<100K
tags:
- table-structure-recognition
- table-understanding
- PDF
task_categories:
- object-detection
- table-to-text
---
# Dataset Card for SynthTabNet_OTSL
## Dataset Description
- **Homepage:** https://ds4sd.github.io
- **Paper:** https://arxiv.org/pdf/2305.03393
### Dataset Summary
This dataset is a conversion of the original [SynthTabNet](https://github.com/IBM/SynthTabNet) into the OTSL format presented in our paper "Optimized Table Tokenization for Table Structure Recognition". The dataset includes the original annotations amongst new additions.
SynthTabNet is organized into 4 parts of 150k tables (600k in total). Each part contains tables with different appearances in regard to their size, structure, style and content. All parts are divided into Train, Test and Val splits.
| Appearance style | Records |
|------------------|---------|
| Fintabnet | 150k |
| Marketing | 150k |
| PubTabNet | 150k |
| Sparse | 150k |
### Dataset Structure
* cells: origunal dataset cell groundtruth (content).
* otsl: new reduced table structure token format
* html: original dataset groundtruth HTML (structure).
* html_restored: generated HTML from OTSL.
* cols: grid column length.
* rows: grid row length.
* image: PIL image
### OTSL Vocabulary:
**OTSL**: new reduced table structure token format
More information on the OTSL table structure format and its concepts can be read from our paper.
Format of this dataset extends work presented in a paper, and introduces slight modifications:
* "fcel" - cell that has content in it
* "ecel" - cell that is empty
* "lcel" - left-looking cell (to handle horizontally merged cells)
* "ucel" - up-looking cell (to handle vertically merged cells)
* "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell
* "nl" - new line token
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Additional Information
### Dataset Curators
The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Citation Information
```bib
@misc{lysak2023optimized,
title={Optimized Table Tokenization for Table Structure Recognition},
author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar},
year={2023},
eprint={2305.03393},
archivePrefix={arXiv},
primaryClass={cs.CV}
}```
|
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] |
DanFosing/public-domain-poetry
|
DanFosing
| 2023-09-24T11:48:44Z | 78 | 3 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"region:us"
] |
[
"text-generation"
] | 2023-09-16T20:46:31Z |
---
task_categories:
- text-generation
pretty_name: public-domain-poetry
language:
- en
size_categories:
- 10K<n<100K
license: cc0-1.0
---
# Overview
This dataset is a collection of approximately 38,500 poems from https://www.public-domain-poetry.com/.
## Language
The language of this dataset is English.
## License
All data in this dataset is public domain, which means you should be able to use it for anything you want, as long as you aren't breaking any law in the process of doing so.
|
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tomaarsen/conll2002
|
tomaarsen
| 2023-09-23T10:53:11Z | 78 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"language:nl",
"license:unknown",
"region:us"
] |
[
"token-classification"
] | 2023-09-23T10:04:25Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- es
- nl
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2002
pretty_name: CoNLL-2002
config_names:
- es
- nl
dataset_info:
- config_name: es
features:
- name: id
dtype: string
- name: document_id
dtype: int32
- name: sentence_id
dtype: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': AO
'1': AQ
'2': CC
'3': CS
'4': DA
'5': DE
'6': DD
'7': DI
'8': DN
'9': DP
'10': DT
'11': Faa
'12': Fat
'13': Fc
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'19': Fit
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'23': Fs
'24': Ft
'25': Fx
'26': Fz
'27': I
'28': NC
'29': NP
'30': P0
'31': PD
'32': PI
'33': PN
'34': PP
'35': PR
'36': PT
'37': PX
'38': RG
'39': RN
'40': SP
'41': VAI
'42': VAM
'43': VAN
'44': VAP
'45': VAS
'46': VMG
'47': VMI
'48': VMM
'49': VMN
'50': VMP
'51': VMS
'52': VSG
'53': VSI
'54': VSM
'55': VSN
'56': VSP
'57': VSS
'58': Y
'59': Z
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 6738717
num_examples: 8323
- name: validation
num_bytes: 1349064
num_examples: 1915
- name: test
num_bytes: 1306252
num_examples: 1517
download_size: 4140690
dataset_size: 9394033
- config_name: nl
features:
- name: id
dtype: string
- name: document_id
dtype: int32
- name: sentence_id
dtype: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': Adj
'1': Adv
'2': Art
'3': Conj
'4': Int
'5': Misc
'6': N
'7': Num
'8': Prep
'9': Pron
'10': Punc
'11': V
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 5435346
num_examples: 15806
- name: validation
num_bytes: 1017418
num_examples: 2895
- name: test
num_bytes: 1850382
num_examples: 5195
download_size: 3642241
dataset_size: 8303146
---
# Dataset Card for CoNLL-2002
## 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:** [homepage](https://www.clips.uantwerpen.be/conll2002/ner/)
- **Repository:** [github](https://github.com/teropa/nlp/tree/master/resources/corpora/conll2002)
- **Paper:** [paper](https://www.aclweb.org/anthology/W02-2024/)
- **Point of Contact:** [Erik Tjong Kim Sang]([email protected])
### Dataset Summary
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.
After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
- `parsing`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A part-of-speech tag is correct only if it is equal to the corresponding tag in the data.
### Languages
There are two languages available : Spanish (es) and Dutch (nl).
## Dataset Structure
### Data Instances
The examples look like this :
```
{
'id': '0',
'document_id': 0,
'sentence_id': 0,
'tokens': ['Melbourne', '(', 'Australia', ')', ',', '25', 'may', '(', 'EFE', ')', '.'],
'pos_tags': [29, 21, 29, 22, 13, 59, 28, 21, 28, 22, 20],
'ner_tags': [5, 0, 5, 0, 0, 0, 0, 0, 3, 0, 0]
}
```
The original data files within the Dutch sub-dataset have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
- `id`: id of the sample
- `document_id`: an `int32` feature tracking which document the sample is from.
- `sentence_id`: an `int32` feature tracking which sentence in this document the sample is from.
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
- `pos_tags`: the POS tags of each token
The POS tags correspond to this list for Spanish:
```
'AO', 'AQ', 'CC', 'CS', 'DA', 'DE', 'DD', 'DI', 'DN', 'DP', 'DT', 'Faa', 'Fat', 'Fc', 'Fd', 'Fe', 'Fg', 'Fh', 'Fia', 'Fit', 'Fp', 'Fpa', 'Fpt', 'Fs', 'Ft', 'Fx', 'Fz', 'I', 'NC', 'NP', 'P0', 'PD', 'PI', 'PN', 'PP', 'PR', 'PT', 'PX', 'RG', 'RN', 'SP', 'VAI', 'VAM', 'VAN', 'VAP', 'VAS', 'VMG', 'VMI', 'VMM', 'VMN', 'VMP', 'VMS', 'VSG', 'VSI', 'VSM', 'VSN', 'VSP', 'VSS', 'Y', 'Z'
```
And this list for Dutch:
```
'Adj', 'Adv', 'Art', 'Conj', 'Int', 'Misc', 'N', 'Num', 'Prep', 'Pron', 'Punc', 'V'
```
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the chunking task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC).
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
### Data Splits
For both configurations (Spanish and Dutch), there are three splits.
The original splits were named `train`, `testa` and `testb` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Examples (Spanish) | 8324 | 1916 | 1518 |
| N. Examples (Dutch) | 15807 | 2896 | 5196 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to two languages that were under-served for statistical machine learning at the time, Dutch and Spanish.
[More Information Needed]
### Source Data
The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.
The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1).
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
For the Dutch data, the annotator has followed the MITRE and SAIC guidelines for named entity recognition (Chinchor et al., 1999) as well as possible.
#### Who are the annotators?
The Spanish data annotation was carried out by the TALP Research Center of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC) of the University of Barcelona (UB).
The Dutch data was annotated as a part of the Atranos project at the University of Antwerp.
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
Named Entity Recognition systems can be used to efficiently index news text, allowing to easily gather all information pertaining to an organization or individual. Making such resources widely available in languages other than English can support better research and user experience for a larger part of the world's population. At the same time, better indexing and discoverability can also enable surveillance by state actors.
### Discussion of Biases
News text reproduces the biases of society, and any system trained on news data should be cognizant of these limitations and the risk for models to learn spurious correlations in this context, for example between a person's gender and their occupation.
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
The annotation of the Spanish data was funded by the European Commission through the NAMIC project (IST-1999-12392).
### Licensing Information
The licensing status of the data, especially the news source text, is unknown.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://www.aclweb.org/anthology/W02-2024",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
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yirenlu/heroicons
|
yirenlu
| 2023-09-26T23:11:38Z | 78 | 0 |
[
"region:us"
] | null | 2023-09-25T19:55:57Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4277197.0
num_examples: 292
download_size: 4220955
dataset_size: 4277197.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "heroicons"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_Yhyu13__chimera-inst-chat-13b-hf
|
open-llm-leaderboard
| 2023-10-15T10:30:44Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-15T10:30:36Z |
---
pretty_name: Evaluation run of Yhyu13/chimera-inst-chat-13b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Yhyu13/chimera-inst-chat-13b-hf](https://huggingface.co/Yhyu13/chimera-inst-chat-13b-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Yhyu13__chimera-inst-chat-13b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T10:30:32.183057](https://huggingface.co/datasets/open-llm-leaderboard/details_Yhyu13__chimera-inst-chat-13b-hf/blob/main/results_2023-10-15T10-30-32.183057.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.006606543624161074,\n\
\ \"em_stderr\": 0.0008296357389921881,\n \"f1\": 0.08297609060402691,\n\
\ \"f1_stderr\": 0.0018006483858768888,\n \"acc\": 0.4107112190060514,\n\
\ \"acc_stderr\": 0.009943586099857618\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006606543624161074,\n \"em_stderr\": 0.0008296357389921881,\n\
\ \"f1\": 0.08297609060402691,\n \"f1_stderr\": 0.0018006483858768888\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08188021228203184,\n \
\ \"acc_stderr\": 0.00755233852771695\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998287\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Yhyu13/chimera-inst-chat-13b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T10_30_32.183057
path:
- '**/details_harness|drop|3_2023-10-15T10-30-32.183057.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T10-30-32.183057.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T10_30_32.183057
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-30-32.183057.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T10-30-32.183057.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T10_30_32.183057
path:
- '**/details_harness|winogrande|5_2023-10-15T10-30-32.183057.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T10-30-32.183057.parquet'
- config_name: results
data_files:
- split: 2023_10_15T10_30_32.183057
path:
- results_2023-10-15T10-30-32.183057.parquet
- split: latest
path:
- results_2023-10-15T10-30-32.183057.parquet
---
# Dataset Card for Evaluation run of Yhyu13/chimera-inst-chat-13b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Yhyu13/chimera-inst-chat-13b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Yhyu13/chimera-inst-chat-13b-hf](https://huggingface.co/Yhyu13/chimera-inst-chat-13b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Yhyu13__chimera-inst-chat-13b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T10:30:32.183057](https://huggingface.co/datasets/open-llm-leaderboard/details_Yhyu13__chimera-inst-chat-13b-hf/blob/main/results_2023-10-15T10-30-32.183057.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.006606543624161074,
"em_stderr": 0.0008296357389921881,
"f1": 0.08297609060402691,
"f1_stderr": 0.0018006483858768888,
"acc": 0.4107112190060514,
"acc_stderr": 0.009943586099857618
},
"harness|drop|3": {
"em": 0.006606543624161074,
"em_stderr": 0.0008296357389921881,
"f1": 0.08297609060402691,
"f1_stderr": 0.0018006483858768888
},
"harness|gsm8k|5": {
"acc": 0.08188021228203184,
"acc_stderr": 0.00755233852771695
},
"harness|winogrande|5": {
"acc": 0.739542225730071,
"acc_stderr": 0.012334833671998287
}
}
```
### 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]
|
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open-llm-leaderboard/details_TehVenom__Dolly_Shygmalion-6b
|
open-llm-leaderboard
| 2023-10-15T16:26:47Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-15T16:26:39Z |
---
pretty_name: Evaluation run of TehVenom/Dolly_Shygmalion-6b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TehVenom/Dolly_Shygmalion-6b](https://huggingface.co/TehVenom/Dolly_Shygmalion-6b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TehVenom__Dolly_Shygmalion-6b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T16:26:35.787063](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__Dolly_Shygmalion-6b/blob/main/results_2023-10-15T16-26-35.787063.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0008389261744966443,\n\
\ \"em_stderr\": 0.0002964962989801232,\n \"f1\": 0.049329907718121055,\n\
\ \"f1_stderr\": 0.001207499751606471,\n \"acc\": 0.33737021840348064,\n\
\ \"acc_stderr\": 0.008672111270767138\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0008389261744966443,\n \"em_stderr\": 0.0002964962989801232,\n\
\ \"f1\": 0.049329907718121055,\n \"f1_stderr\": 0.001207499751606471\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02122820318423048,\n \
\ \"acc_stderr\": 0.003970449129848635\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6535122336227308,\n \"acc_stderr\": 0.01337377341168564\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TehVenom/Dolly_Shygmalion-6b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T16_26_35.787063
path:
- '**/details_harness|drop|3_2023-10-15T16-26-35.787063.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T16-26-35.787063.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T16_26_35.787063
path:
- '**/details_harness|gsm8k|5_2023-10-15T16-26-35.787063.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T16-26-35.787063.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T16_26_35.787063
path:
- '**/details_harness|winogrande|5_2023-10-15T16-26-35.787063.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T16-26-35.787063.parquet'
- config_name: results
data_files:
- split: 2023_10_15T16_26_35.787063
path:
- results_2023-10-15T16-26-35.787063.parquet
- split: latest
path:
- results_2023-10-15T16-26-35.787063.parquet
---
# Dataset Card for Evaluation run of TehVenom/Dolly_Shygmalion-6b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TehVenom/Dolly_Shygmalion-6b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TehVenom/Dolly_Shygmalion-6b](https://huggingface.co/TehVenom/Dolly_Shygmalion-6b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TehVenom__Dolly_Shygmalion-6b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T16:26:35.787063](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__Dolly_Shygmalion-6b/blob/main/results_2023-10-15T16-26-35.787063.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0008389261744966443,
"em_stderr": 0.0002964962989801232,
"f1": 0.049329907718121055,
"f1_stderr": 0.001207499751606471,
"acc": 0.33737021840348064,
"acc_stderr": 0.008672111270767138
},
"harness|drop|3": {
"em": 0.0008389261744966443,
"em_stderr": 0.0002964962989801232,
"f1": 0.049329907718121055,
"f1_stderr": 0.001207499751606471
},
"harness|gsm8k|5": {
"acc": 0.02122820318423048,
"acc_stderr": 0.003970449129848635
},
"harness|winogrande|5": {
"acc": 0.6535122336227308,
"acc_stderr": 0.01337377341168564
}
}
```
### 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]
|
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open-llm-leaderboard/details_ziqingyang__chinese-alpaca-2-13b
|
open-llm-leaderboard
| 2023-10-15T20:22:39Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-15T20:22:31Z |
---
pretty_name: Evaluation run of ziqingyang/chinese-alpaca-2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ziqingyang/chinese-alpaca-2-13b](https://huggingface.co/ziqingyang/chinese-alpaca-2-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ziqingyang__chinese-alpaca-2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T20:22:27.142442](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-alpaca-2-13b/blob/main/results_2023-10-15T20-22-27.142442.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.32728607382550334,\n\
\ \"em_stderr\": 0.004805279168508311,\n \"f1\": 0.4106134647651026,\n\
\ \"f1_stderr\": 0.004650726360819101,\n \"acc\": 0.4307653965208868,\n\
\ \"acc_stderr\": 0.010243166856230161\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.32728607382550334,\n \"em_stderr\": 0.004805279168508311,\n\
\ \"f1\": 0.4106134647651026,\n \"f1_stderr\": 0.004650726360819101\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10462471569370735,\n \
\ \"acc_stderr\": 0.008430668082029278\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7569060773480663,\n \"acc_stderr\": 0.012055665630431043\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ziqingyang/chinese-alpaca-2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T20_22_27.142442
path:
- '**/details_harness|drop|3_2023-10-15T20-22-27.142442.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T20-22-27.142442.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T20_22_27.142442
path:
- '**/details_harness|gsm8k|5_2023-10-15T20-22-27.142442.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T20-22-27.142442.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T20_22_27.142442
path:
- '**/details_harness|winogrande|5_2023-10-15T20-22-27.142442.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T20-22-27.142442.parquet'
- config_name: results
data_files:
- split: 2023_10_15T20_22_27.142442
path:
- results_2023-10-15T20-22-27.142442.parquet
- split: latest
path:
- results_2023-10-15T20-22-27.142442.parquet
---
# Dataset Card for Evaluation run of ziqingyang/chinese-alpaca-2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ziqingyang/chinese-alpaca-2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ziqingyang/chinese-alpaca-2-13b](https://huggingface.co/ziqingyang/chinese-alpaca-2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ziqingyang__chinese-alpaca-2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T20:22:27.142442](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-alpaca-2-13b/blob/main/results_2023-10-15T20-22-27.142442.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.32728607382550334,
"em_stderr": 0.004805279168508311,
"f1": 0.4106134647651026,
"f1_stderr": 0.004650726360819101,
"acc": 0.4307653965208868,
"acc_stderr": 0.010243166856230161
},
"harness|drop|3": {
"em": 0.32728607382550334,
"em_stderr": 0.004805279168508311,
"f1": 0.4106134647651026,
"f1_stderr": 0.004650726360819101
},
"harness|gsm8k|5": {
"acc": 0.10462471569370735,
"acc_stderr": 0.008430668082029278
},
"harness|winogrande|5": {
"acc": 0.7569060773480663,
"acc_stderr": 0.012055665630431043
}
}
```
### 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]
|
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open-llm-leaderboard/details_TaylorAI__Flash-Llama-13B
|
open-llm-leaderboard
| 2023-10-16T02:07:33Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-16T02:07:25Z |
---
pretty_name: Evaluation run of TaylorAI/Flash-Llama-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TaylorAI/Flash-Llama-13B](https://huggingface.co/TaylorAI/Flash-Llama-13B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TaylorAI__Flash-Llama-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T02:07:21.607373](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__Flash-Llama-13B/blob/main/results_2023-10-16T02-07-21.607373.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.00039210421902982666,\n \"f1\": 0.0607822986577181,\n\
\ \"f1_stderr\": 0.0013583957676382913,\n \"acc\": 0.43739636770101,\n\
\ \"acc_stderr\": 0.010228023491905505\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902982666,\n\
\ \"f1\": 0.0607822986577181,\n \"f1_stderr\": 0.0013583957676382913\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \
\ \"acc_stderr\": 0.008563852506627487\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183524\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TaylorAI/Flash-Llama-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T02_07_21.607373
path:
- '**/details_harness|drop|3_2023-10-16T02-07-21.607373.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T02-07-21.607373.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T02_07_21.607373
path:
- '**/details_harness|gsm8k|5_2023-10-16T02-07-21.607373.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T02-07-21.607373.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T02_07_21.607373
path:
- '**/details_harness|winogrande|5_2023-10-16T02-07-21.607373.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T02-07-21.607373.parquet'
- config_name: results
data_files:
- split: 2023_10_16T02_07_21.607373
path:
- results_2023-10-16T02-07-21.607373.parquet
- split: latest
path:
- results_2023-10-16T02-07-21.607373.parquet
---
# Dataset Card for Evaluation run of TaylorAI/Flash-Llama-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TaylorAI/Flash-Llama-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TaylorAI/Flash-Llama-13B](https://huggingface.co/TaylorAI/Flash-Llama-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TaylorAI__Flash-Llama-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T02:07:21.607373](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__Flash-Llama-13B/blob/main/results_2023-10-16T02-07-21.607373.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902982666,
"f1": 0.0607822986577181,
"f1_stderr": 0.0013583957676382913,
"acc": 0.43739636770101,
"acc_stderr": 0.010228023491905505
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902982666,
"f1": 0.0607822986577181,
"f1_stderr": 0.0013583957676382913
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627487
},
"harness|winogrande|5": {
"acc": 0.7663772691397001,
"acc_stderr": 0.011892194477183524
}
}
```
### 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]
|
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open-llm-leaderboard/details_Aeala__Enterredaas-33b
|
open-llm-leaderboard
| 2023-10-17T04:24:29Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T04:24:21Z |
---
pretty_name: Evaluation run of Aeala/Enterredaas-33b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aeala/Enterredaas-33b](https://huggingface.co/Aeala/Enterredaas-33b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aeala__Enterredaas-33b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T04:24:16.762833](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Enterredaas-33b/blob/main/results_2023-10-17T04-24-16.762833.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.00040584511324177344,\n \"f1\": 0.06232487416107388,\n\
\ \"f1_stderr\": 0.0013590473373823627,\n \"acc\": 0.47496578750374735,\n\
\ \"acc_stderr\": 0.010824257783821654\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.00040584511324177344,\n\
\ \"f1\": 0.06232487416107388,\n \"f1_stderr\": 0.0013590473373823627\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.16224412433661864,\n \
\ \"acc_stderr\": 0.010155130880393526\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249784\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aeala/Enterredaas-33b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- '**/details_harness|drop|3_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T04-24-16.762833.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- '**/details_harness|gsm8k|5_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T04-24-16.762833.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- '**/details_harness|winogrande|5_2023-10-17T04-24-16.762833.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T04-24-16.762833.parquet'
- config_name: results
data_files:
- split: 2023_10_17T04_24_16.762833
path:
- results_2023-10-17T04-24-16.762833.parquet
- split: latest
path:
- results_2023-10-17T04-24-16.762833.parquet
---
# Dataset Card for Evaluation run of Aeala/Enterredaas-33b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aeala/Enterredaas-33b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aeala/Enterredaas-33b](https://huggingface.co/Aeala/Enterredaas-33b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aeala__Enterredaas-33b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T04:24:16.762833](https://huggingface.co/datasets/open-llm-leaderboard/details_Aeala__Enterredaas-33b/blob/main/results_2023-10-17T04-24-16.762833.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001572986577181208,
"em_stderr": 0.00040584511324177344,
"f1": 0.06232487416107388,
"f1_stderr": 0.0013590473373823627,
"acc": 0.47496578750374735,
"acc_stderr": 0.010824257783821654
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.00040584511324177344,
"f1": 0.06232487416107388,
"f1_stderr": 0.0013590473373823627
},
"harness|gsm8k|5": {
"acc": 0.16224412433661864,
"acc_stderr": 0.010155130880393526
},
"harness|winogrande|5": {
"acc": 0.7876874506708761,
"acc_stderr": 0.011493384687249784
}
}
```
### 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]
|
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open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean
|
open-llm-leaderboard
| 2023-10-17T08:56:36Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T08:56:28Z |
---
pretty_name: Evaluation run of quantumaikr/llama-2-70b-fb16-korean
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [quantumaikr/llama-2-70b-fb16-korean](https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T08:56:24.573395](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean/blob/main/results_2023-10-17T08-56-24.573395.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0041946308724832215,\n\
\ \"em_stderr\": 0.0006618716168266237,\n \"f1\": 0.07418729026845645,\n\
\ \"f1_stderr\": 0.0015820737575191846,\n \"acc\": 0.5583664886878857,\n\
\ \"acc_stderr\": 0.011574854481074981\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0041946308724832215,\n \"em_stderr\": 0.0006618716168266237,\n\
\ \"f1\": 0.07418729026845645,\n \"f1_stderr\": 0.0015820737575191846\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.29037149355572406,\n \
\ \"acc_stderr\": 0.012503592481818962\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8263614838200474,\n \"acc_stderr\": 0.010646116480331\n\
\ }\n}\n```"
repo_url: https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- '**/details_harness|drop|3_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T08-56-24.573395.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T08-56-24.573395.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- '**/details_harness|winogrande|5_2023-10-17T08-56-24.573395.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T08-56-24.573395.parquet'
- config_name: results
data_files:
- split: 2023_10_17T08_56_24.573395
path:
- results_2023-10-17T08-56-24.573395.parquet
- split: latest
path:
- results_2023-10-17T08-56-24.573395.parquet
---
# Dataset Card for Evaluation run of quantumaikr/llama-2-70b-fb16-korean
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [quantumaikr/llama-2-70b-fb16-korean](https://huggingface.co/quantumaikr/llama-2-70b-fb16-korean) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T08:56:24.573395](https://huggingface.co/datasets/open-llm-leaderboard/details_quantumaikr__llama-2-70b-fb16-korean/blob/main/results_2023-10-17T08-56-24.573395.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0041946308724832215,
"em_stderr": 0.0006618716168266237,
"f1": 0.07418729026845645,
"f1_stderr": 0.0015820737575191846,
"acc": 0.5583664886878857,
"acc_stderr": 0.011574854481074981
},
"harness|drop|3": {
"em": 0.0041946308724832215,
"em_stderr": 0.0006618716168266237,
"f1": 0.07418729026845645,
"f1_stderr": 0.0015820737575191846
},
"harness|gsm8k|5": {
"acc": 0.29037149355572406,
"acc_stderr": 0.012503592481818962
},
"harness|winogrande|5": {
"acc": 0.8263614838200474,
"acc_stderr": 0.010646116480331
}
}
```
### 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]
|
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open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora
|
open-llm-leaderboard
| 2023-10-17T09:49:05Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T09:48:57Z |
---
pretty_name: Evaluation run of chargoddard/platypus-2-22b-relora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/platypus-2-22b-relora](https://huggingface.co/chargoddard/platypus-2-22b-relora)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T09:48:53.081759](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora/blob/main/results_2023-10-17T09-48-53.081759.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.38443791946308725,\n\
\ \"em_stderr\": 0.004981827548218364,\n \"f1\": 0.42459836409396046,\n\
\ \"f1_stderr\": 0.004867799120548586,\n \"acc\": 0.4197198614386422,\n\
\ \"acc_stderr\": 0.009300550123366277\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.38443791946308725,\n \"em_stderr\": 0.004981827548218364,\n\
\ \"f1\": 0.42459836409396046,\n \"f1_stderr\": 0.004867799120548586\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06595905989385899,\n \
\ \"acc_stderr\": 0.006836951192034225\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698329\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/platypus-2-22b-relora
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- '**/details_harness|drop|3_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T09-48-53.081759.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-48-53.081759.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- '**/details_harness|winogrande|5_2023-10-17T09-48-53.081759.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T09-48-53.081759.parquet'
- config_name: results
data_files:
- split: 2023_10_17T09_48_53.081759
path:
- results_2023-10-17T09-48-53.081759.parquet
- split: latest
path:
- results_2023-10-17T09-48-53.081759.parquet
---
# Dataset Card for Evaluation run of chargoddard/platypus-2-22b-relora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/platypus-2-22b-relora
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/platypus-2-22b-relora](https://huggingface.co/chargoddard/platypus-2-22b-relora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T09:48:53.081759](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus-2-22b-relora/blob/main/results_2023-10-17T09-48-53.081759.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.38443791946308725,
"em_stderr": 0.004981827548218364,
"f1": 0.42459836409396046,
"f1_stderr": 0.004867799120548586,
"acc": 0.4197198614386422,
"acc_stderr": 0.009300550123366277
},
"harness|drop|3": {
"em": 0.38443791946308725,
"em_stderr": 0.004981827548218364,
"f1": 0.42459836409396046,
"f1_stderr": 0.004867799120548586
},
"harness|gsm8k|5": {
"acc": 0.06595905989385899,
"acc_stderr": 0.006836951192034225
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.011764149054698329
}
}
```
### 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
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open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b
|
open-llm-leaderboard
| 2023-10-17T10:27:46Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T10:27:37Z |
---
pretty_name: Evaluation run of chargoddard/Chronorctypus-Limarobormes-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/Chronorctypus-Limarobormes-13b](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T10:27:33.460587](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b/blob/main/results_2023-10-17T10-27-33.460587.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.05169882550335571,\n\
\ \"em_stderr\": 0.0022675304823078276,\n \"f1\": 0.17888317953020105,\n\
\ \"f1_stderr\": 0.0028882183973903902,\n \"acc\": 0.39147173871286817,\n\
\ \"acc_stderr\": 0.008785918503769254\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.05169882550335571,\n \"em_stderr\": 0.0022675304823078276,\n\
\ \"f1\": 0.17888317953020105,\n \"f1_stderr\": 0.0028882183973903902\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03866565579984837,\n \
\ \"acc_stderr\": 0.005310583162098035\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440473\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- '**/details_harness|drop|3_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T10-27-33.460587.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- '**/details_harness|gsm8k|5_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T10-27-33.460587.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- '**/details_harness|winogrande|5_2023-10-17T10-27-33.460587.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T10-27-33.460587.parquet'
- config_name: results
data_files:
- split: 2023_10_17T10_27_33.460587
path:
- results_2023-10-17T10-27-33.460587.parquet
- split: latest
path:
- results_2023-10-17T10-27-33.460587.parquet
---
# Dataset Card for Evaluation run of chargoddard/Chronorctypus-Limarobormes-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/Chronorctypus-Limarobormes-13b](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T10:27:33.460587](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b/blob/main/results_2023-10-17T10-27-33.460587.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.05169882550335571,
"em_stderr": 0.0022675304823078276,
"f1": 0.17888317953020105,
"f1_stderr": 0.0028882183973903902,
"acc": 0.39147173871286817,
"acc_stderr": 0.008785918503769254
},
"harness|drop|3": {
"em": 0.05169882550335571,
"em_stderr": 0.0022675304823078276,
"f1": 0.17888317953020105,
"f1_stderr": 0.0028882183973903902
},
"harness|gsm8k|5": {
"acc": 0.03866565579984837,
"acc_stderr": 0.005310583162098035
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440473
}
}
```
### 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]
|
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open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload
|
open-llm-leaderboard
| 2023-12-02T16:47:50Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T19:08:46Z |
---
pretty_name: Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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/Aspik101/tulu-7b-instruct-pl-lora_unload
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T19_08_42.181138
path:
- '**/details_harness|drop|3_2023-10-17T19-08-42.181138.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T19-08-42.181138.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T19_08_42.181138
path:
- '**/details_harness|gsm8k|5_2023-10-17T19-08-42.181138.parquet'
- split: 2023_12_02T16_47_29.026992
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-47-29.026992.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T16-47-29.026992.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T19_08_42.181138
path:
- '**/details_harness|winogrande|5_2023-10-17T19-08-42.181138.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T19-08-42.181138.parquet'
- config_name: results
data_files:
- split: 2023_10_17T19_08_42.181138
path:
- results_2023-10-17T19-08-42.181138.parquet
- split: 2023_12_02T16_47_29.026992
path:
- results_2023-12-02T16-47-29.026992.parquet
- split: latest
path:
- results_2023-12-02T16-47-29.026992.parquet
---
# Dataset Card for Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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]
|
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open-llm-leaderboard/details_augtoma__qCammel-70
|
open-llm-leaderboard
| 2023-10-17T22:35:47Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-17T22:35:39Z |
---
pretty_name: Evaluation run of augtoma/qCammel-70
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [augtoma/qCammel-70](https://huggingface.co/augtoma/qCammel-70) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_augtoma__qCammel-70\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T22:35:35.594396](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70/blob/main/results_2023-10-17T22-35-35.594396.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.033766778523489936,\n\
\ \"em_stderr\": 0.001849802869119515,\n \"f1\": 0.10340918624161041,\n\
\ \"f1_stderr\": 0.0022106009828094797,\n \"acc\": 0.5700654570173166,\n\
\ \"acc_stderr\": 0.011407494958111332\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.033766778523489936,\n \"em_stderr\": 0.001849802869119515,\n\
\ \"f1\": 0.10340918624161041,\n \"f1_stderr\": 0.0022106009828094797\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2971948445792267,\n \
\ \"acc_stderr\": 0.012588685966624186\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\
\ }\n}\n```"
repo_url: https://huggingface.co/augtoma/qCammel-70
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T22_35_35.594396
path:
- '**/details_harness|drop|3_2023-10-17T22-35-35.594396.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T22-35-35.594396.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T22_35_35.594396
path:
- '**/details_harness|gsm8k|5_2023-10-17T22-35-35.594396.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T22-35-35.594396.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T22_35_35.594396
path:
- '**/details_harness|winogrande|5_2023-10-17T22-35-35.594396.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T22-35-35.594396.parquet'
- config_name: results
data_files:
- split: 2023_10_17T22_35_35.594396
path:
- results_2023-10-17T22-35-35.594396.parquet
- split: latest
path:
- results_2023-10-17T22-35-35.594396.parquet
---
# Dataset Card for Evaluation run of augtoma/qCammel-70
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/augtoma/qCammel-70
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [augtoma/qCammel-70](https://huggingface.co/augtoma/qCammel-70) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_augtoma__qCammel-70",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T22:35:35.594396](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70/blob/main/results_2023-10-17T22-35-35.594396.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797,
"acc": 0.5700654570173166,
"acc_stderr": 0.011407494958111332
},
"harness|drop|3": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797
},
"harness|gsm8k|5": {
"acc": 0.2971948445792267,
"acc_stderr": 0.012588685966624186
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598479
}
}
```
### 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]
|
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-OpenOrca_20w
|
open-llm-leaderboard
| 2023-10-18T01:15:07Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-18T01:14:59Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-OpenOrca_20w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-OpenOrca_20w](https://huggingface.co/CHIH-HUNG/llama-2-13b-OpenOrca_20w)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-OpenOrca_20w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T01:14:55.229555](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-OpenOrca_20w/blob/main/results_2023-10-18T01-14-55.229555.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.14953859060402686,\n\
\ \"em_stderr\": 0.0036521078888639676,\n \"f1\": 0.20982382550335602,\n\
\ \"f1_stderr\": 0.003706029190176112,\n \"acc\": 0.44925660000490675,\n\
\ \"acc_stderr\": 0.010476365550372343\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.14953859060402686,\n \"em_stderr\": 0.0036521078888639676,\n\
\ \"f1\": 0.20982382550335602,\n \"f1_stderr\": 0.003706029190176112\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12661106899166036,\n \
\ \"acc_stderr\": 0.009159715283081094\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663592\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-OpenOrca_20w
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T01_14_55.229555
path:
- '**/details_harness|drop|3_2023-10-18T01-14-55.229555.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T01-14-55.229555.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T01_14_55.229555
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-55.229555.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T01-14-55.229555.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T01_14_55.229555
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-55.229555.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T01-14-55.229555.parquet'
- config_name: results
data_files:
- split: 2023_10_18T01_14_55.229555
path:
- results_2023-10-18T01-14-55.229555.parquet
- split: latest
path:
- results_2023-10-18T01-14-55.229555.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-OpenOrca_20w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-OpenOrca_20w
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-OpenOrca_20w](https://huggingface.co/CHIH-HUNG/llama-2-13b-OpenOrca_20w) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-OpenOrca_20w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T01:14:55.229555](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-OpenOrca_20w/blob/main/results_2023-10-18T01-14-55.229555.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.14953859060402686,
"em_stderr": 0.0036521078888639676,
"f1": 0.20982382550335602,
"f1_stderr": 0.003706029190176112,
"acc": 0.44925660000490675,
"acc_stderr": 0.010476365550372343
},
"harness|drop|3": {
"em": 0.14953859060402686,
"em_stderr": 0.0036521078888639676,
"f1": 0.20982382550335602,
"f1_stderr": 0.003706029190176112
},
"harness|gsm8k|5": {
"acc": 0.12661106899166036,
"acc_stderr": 0.009159715283081094
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.011793015817663592
}
}
```
### 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
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### Contributions
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|
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open-llm-leaderboard/details_golaxy__gogpt2-13b
|
open-llm-leaderboard
| 2023-10-23T09:09:11Z | 78 | 0 |
[
"region:us"
] | null | 2023-10-23T09:09:03Z |
---
pretty_name: Evaluation run of golaxy/gogpt2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [golaxy/gogpt2-13b](https://huggingface.co/golaxy/gogpt2-13b) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_golaxy__gogpt2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T09:08:59.644904](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt2-13b/blob/main/results_2023-10-23T09-08-59.644904.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.25041946308724833,\n\
\ \"em_stderr\": 0.004436932707454965,\n \"f1\": 0.319959102348994,\n\
\ \"f1_stderr\": 0.004400567822301105,\n \"acc\": 0.3496193990687978,\n\
\ \"acc_stderr\": 0.00851200635523702\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.25041946308724833,\n \"em_stderr\": 0.004436932707454965,\n\
\ \"f1\": 0.319959102348994,\n \"f1_stderr\": 0.004400567822301105\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02047005307050796,\n \
\ \"acc_stderr\": 0.003900413385915719\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6787687450670876,\n \"acc_stderr\": 0.013123599324558321\n\
\ }\n}\n```"
repo_url: https://huggingface.co/golaxy/gogpt2-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|drop|3_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T09-08-59.644904.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|gsm8k|5_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T09-08-59.644904.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- '**/details_harness|winogrande|5_2023-10-23T09-08-59.644904.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T09-08-59.644904.parquet'
- config_name: results
data_files:
- split: 2023_10_23T09_08_59.644904
path:
- results_2023-10-23T09-08-59.644904.parquet
- split: latest
path:
- results_2023-10-23T09-08-59.644904.parquet
---
# Dataset Card for Evaluation run of golaxy/gogpt2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/golaxy/gogpt2-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [golaxy/gogpt2-13b](https://huggingface.co/golaxy/gogpt2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_golaxy__gogpt2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T09:08:59.644904](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt2-13b/blob/main/results_2023-10-23T09-08-59.644904.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.25041946308724833,
"em_stderr": 0.004436932707454965,
"f1": 0.319959102348994,
"f1_stderr": 0.004400567822301105,
"acc": 0.3496193990687978,
"acc_stderr": 0.00851200635523702
},
"harness|drop|3": {
"em": 0.25041946308724833,
"em_stderr": 0.004436932707454965,
"f1": 0.319959102348994,
"f1_stderr": 0.004400567822301105
},
"harness|gsm8k|5": {
"acc": 0.02047005307050796,
"acc_stderr": 0.003900413385915719
},
"harness|winogrande|5": {
"acc": 0.6787687450670876,
"acc_stderr": 0.013123599324558321
}
}
```
### 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]
|
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multi-train/agnews_1107
|
multi-train
| 2023-11-10T18:36:46Z | 78 | 0 |
[
"region:us"
] | null | 2023-11-10T18:36:37Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 98773974
num_examples: 200000
download_size: 50174968
dataset_size: 98773974
---
# Dataset Card for "agnews_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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atmallen/qm_bob_mixture_1.0e
|
atmallen
| 2023-11-16T18:18:21Z | 78 | 0 |
[
"region:us"
] | null | 2023-11-16T03:33:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 22366655.5
num_examples: 200000
- name: validation
num_bytes: 2254431.5
num_examples: 20000
- name: test
num_bytes: 2248382.5
num_examples: 20000
download_size: 0
dataset_size: 26869469.5
---
# Dataset Card for "qm_bob__mixture_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
dapooni/sorsolingo_en_bsl
|
dapooni
| 2023-12-03T13:13:55Z | 78 | 0 |
[
"region:us"
] | null | 2023-11-26T16:20:01Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: bsl
dtype: string
splits:
- name: train
num_bytes: 163845
num_examples: 3303
download_size: 117966
dataset_size: 163845
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
nateraw/english-to-hinglish
|
nateraw
| 2023-11-28T21:16:58Z | 78 | 0 |
[
"region:us"
] | null | 2023-11-28T10:01:50Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: hi_ng
dtype: string
- name: source
dtype: int64
splits:
- name: train
num_bytes: 18814411
num_examples: 178701
- name: test
num_bytes: 1098000
num_examples: 10401
download_size: 11924718
dataset_size: 19912411
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
Fork of [findnitai/english-to-hinglish](https://huggingface.co/datasets/findnitai/english-to-hinglish) that splits the training set into train/test.
|
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codeparrot/github-jupyter-parsed
|
codeparrot
| 2022-10-25T09:30:23Z | 77 | 5 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:muonolingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] |
[
"text-generation"
] | 2022-07-06T13:09:04Z |
---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- muonolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# GitHub Jupyter Dataset
## Dataset Description
This is a parsed and preprocessed version of [GitHub-Jupyter Dataset](https://huggingface.co/datasets/codeparrot/github-jupyter), a dataset extracted from Jupyter Notebooks on BigQuery. We only keep markdown and python cells and convert the markdown to text. Some heuristics are also applied to filter notebooks with little data and very long or very short cells.
## Licenses
Each example has the license of its associated repository. There are in total 15 licenses:
```python
[
'mit',
'apache-2.0',
'gpl-3.0',
'gpl-2.0',
'bsd-3-clause',
'agpl-3.0',
'lgpl-3.0',
'lgpl-2.1',
'bsd-2-clause',
'cc0-1.0',
'epl-1.0',
'mpl-2.0',
'unlicense',
'isc',
'artistic-2.0'
]
```
|
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Bingsu/zeroth-korean
|
Bingsu
| 2022-08-15T10:30:30Z | 77 | 11 |
[
"task_categories:automatic-speech-recognition",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|kresnik/zeroth_korean",
"language:ko",
"license:cc-by-4.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-08-14T08:50:33Z |
---
language:
- ko
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: zeroth-korean
source_datasets:
- extended|kresnik/zeroth_korean
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
---
# Zeroth-Korean
## Dataset Description
- **Homepage:** [OpenSLR](https://www.openslr.org/40/)
- **Repository:** [goodatlas/zeroth](https://github.com/goodatlas/zeroth)
- **Download Size** 2.68 GiB
- **Generated Size** 2.85 GiB
- **Total Size** 5.52 GiB
## Zeroth-Korean
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor).
Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone.
This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.).
Contact: Lucas Jo([email protected]), Wonkyum Lee([email protected])
### License
CC BY 4.0
## Dataset Structure
### Data Instance
```pycon
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/zeroth-korean")
>>> dataset
DatasetDict({
train: Dataset({
features: ['audio', 'text'],
num_rows: 22263
})
test: Dataset({
features: ['text', 'audio'],
num_rows: 457
})
})
```
### Data Size
download: 2.68 GiB<br>
generated: 2.85 GiB<br>
total: 5.52 GiB
### Data Fields
- audio: `audio`, sampling rate = 16000
- A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`.
- text: `string`
```pycon
>>> dataset["train"][0]
{'audio': {'path': None,
'array': array([-3.0517578e-05, 0.0000000e+00, -3.0517578e-05, ...,
0.0000000e+00, 0.0000000e+00, -6.1035156e-05], dtype=float32),
'sampling_rate': 16000},
'text': '인사를 결정하는 과정에서 당 지도부가 우 원내대표 및 원내지도부와 충분한 상의를 거치지 않은 채 일방적으로 인사를 했다는 불만도 원내지도부를 중심으로 흘러나왔다'}
```
### Data Splits
| | train | test |
| ---------- | -------- | ----- |
| # of data | 22263 | 457 |
|
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] |
bigbio/biorelex
|
bigbio
| 2022-12-22T15:44:10Z | 77 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:07:24Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BioRelEx
homepage: https://github.com/YerevaNN/BioRelEx
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
- RELATION_EXTRACTION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioRelEx
## Dataset Description
- **Homepage:** https://github.com/YerevaNN/BioRelEx
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED,RE,COREF
BioRelEx is a biological relation extraction dataset. Version 1.0 contains 2010
annotated sentences that describe binding interactions between various
biological entities (proteins, chemicals, etc.). 1405 sentences are for
training, another 201 sentences are for validation. They are publicly available
at https://github.com/YerevaNN/BioRelEx/releases. Another 404 sentences are for
testing which are kept private for at this Codalab competition
https://competitions.codalab.org/competitions/20468. All sentences contain words
"bind", "bound" or "binding". For every sentence we provide: 1) Complete
annotations of all biological entities that appear in the sentence 2) Entity
types (32 types) and grounding information for most of the proteins and families
(links to uniprot, interpro and other databases) 3) Coreference between entities
in the same sentence (e.g. abbreviations and synonyms) 4) Binding interactions
between the annotated entities 5) Binding interaction types: positive, negative
(A does not bind B) and neutral (A may bind to B)
## Citation Information
```
@inproceedings{khachatrian2019biorelex,
title = "{B}io{R}el{E}x 1.0: Biological Relation Extraction Benchmark",
author = "Khachatrian, Hrant and
Nersisyan, Lilit and
Hambardzumyan, Karen and
Galstyan, Tigran and
Hakobyan, Anna and
Arakelyan, Arsen and
Rzhetsky, Andrey and
Galstyan, Aram",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5019",
doi = "10.18653/v1/W19-5019",
pages = "176--190"
}
```
|
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] |
MedHALT/Med-HALT
|
MedHALT
| 2023-08-10T15:27:31Z | 77 | 6 |
[
"license:apache-2.0",
"arxiv:2307.15343",
"region:us"
] | null | 2023-08-10T07:30:11Z |
---
license: apache-2.0
configs:
- config_name: IR_abstract2pubmedlink
data_files: "IR_abstract2pubmedlink/IR_abstract2pubmedlink.csv"
- config_name: IR_pubmedlink2title
data_files: "IR_pubmedlink2title/IR_pubmedlink2title.csv"
- config_name: IR_pmid2title
data_files: "IR_pmid2title/IR_pmid2title.csv"
- config_name: IR_title2pubmedlink
data_files: "IR_title2pubmedlink/IR_title2pubmedlink.csv"
- config_name: reasoning_fake
data_files: "reasoning_fake/reasoning_fake.csv"
- config_name: reasoning_nota
data_files: "reasoning_nota/reasoning_nota.csv"
- config_name: reasoning_FCT
data_files: "reasoning_FCT/reasoning_FCT.csv"
---
# Med-HALT: Medical Domain Hallucination Test for Large Language Models
This is a dataset used in the [Med-HALT](https://arxiv.org/abs/2307.15343) research paper. This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. We propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate hallucinations.
Med-HALT provides a diverse multinational dataset derived from medical examinations across various countries and includes multiple innovative testing modalities. Med-HALT includes two categories of tests reasoning and memory-based hallucination tests, designed to assess LLMs' problem-solving and information retrieval abilities. Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa and Falcon, revealing significant differences in their performance. The paper provides detailed insights into the dataset, promoting transparency and reproducibility. Through this work, we aim to contribute to the development of safer and more reliable language models in healthcare. Our benchmark can be found at https://github.com/medhalt/medhalt
## Benchmark
The Med-HALT framework proposes a two-tiered approach to evaluate the presence and impact of hallucinations in generated outputs.
#### Reasoning Hallucination Tests (RHTs)
<details>
<summary>False Confidence Test (FCT)</summary>
The False Confidence Test (FCT) involves presenting a multiple-choice medical question and a randomly suggested correct answer to the language model, tasking it with evaluating the validity of the proposed answer and providing detailed explanations for its correctness or incorrectness, in addition to explaining why the other options are wrong.
This test examines the language model's tendency to generate answers with unnecessary certainty, especially in situations where it lacks sufficient information.
</details>
<details>
<summary>None of the Above Test (Nota)</summary>
In the None of the Above (Nota) Test, the model is presented with a multiple-choice medical question where the correct answer is replaced by 'None of the above', requiring the model to identify this and justify its selection.
It tests the model's ability to distinguish irrelevant or incorrect information.
</details>
<details>
<summary>Fake Questions Test (FQT)</summary>
This test involves presenting the model with fake or nonsensical medical questions to examine whether it can correctly identify and handle such queries.
We employed a hybrid approach for generating fake questions, where a subset was crafted by human experts, while the remaining were generated using GPT-3.5.
</details>
#### Memory Hallucination Tests (MHTs)
<details>
<summary>Abstract-to-Link Test</summary>
Given the abstract of a PubMed article, the LLM is asked to generate the corresponding link to the article. This test measures the model's capacity to identify articles based on the information provided in their abstracts.
</details>
<details>
<summary>PMID-to-Title Test</summary>
In this test, the LLM is given the PubMed ID (PMID) of an article and is asked to generate the title of the article. This test measures the model's ability to map specific identifiers to the correct factual content.
</details>
<details>
<summary>Title-to-Link Test</summary>
Given the title of a PubMed article, the LLM is prompted to provide the PubMed link of the article. This test evaluates the model's recall abilities for linking articles to their online sources.
</details>
<details>
<summary>Link-to-Title Test</summary>
Similar to the previous one, in this test, we give the PubMed link of an article as input and ask the language model to provide the title as output. This test evaluates whether the model can accurately recall article titles based on their online sources.
</details>
## Citation
```
@article{Medhalt,
title={Med-HALT: Medical Domain Hallucination Test for Large Language Models},
author={Umapathi, Logesh Kumar and Pal, Ankit and Sankarasubbu, Malaikannan},
journal={arXiv preprint},
year={2023}
}
```
|
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open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch
|
open-llm-leaderboard
| 2023-10-15T17:45:59Z | 77 | 0 |
[
"region:us"
] | null | 2023-08-28T22:58:19Z |
---
pretty_name: Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T17:45:47.583077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-15T17-45-47.583077.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.4115981543624161,\n\
\ \"em_stderr\": 0.005039800830998692,\n \"f1\": 0.4835130033557058,\n\
\ \"f1_stderr\": 0.004781616038785324,\n \"acc\": 0.4006105292510111,\n\
\ \"acc_stderr\": 0.008506118979991088\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4115981543624161,\n \"em_stderr\": 0.005039800830998692,\n\
\ \"f1\": 0.4835130033557058,\n \"f1_stderr\": 0.004781616038785324\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0356330553449583,\n \
\ \"acc_stderr\": 0.005106107853744191\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T17_45_47.583077
path:
- '**/details_harness|drop|3_2023-10-15T17-45-47.583077.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T17-45-47.583077.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T17_45_47.583077
path:
- '**/details_harness|gsm8k|5_2023-10-15T17-45-47.583077.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T17-45-47.583077.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T17_45_47.583077
path:
- '**/details_harness|winogrande|5_2023-10-15T17-45-47.583077.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T17-45-47.583077.parquet'
- config_name: results
data_files:
- split: 2023_08_28T22_57_17.888972
path:
- results_2023-08-28T22:57:17.888972.parquet
- split: 2023_10_15T17_45_47.583077
path:
- results_2023-10-15T17-45-47.583077.parquet
- split: latest
path:
- results_2023-10-15T17-45-47.583077.parquet
---
# Dataset Card for Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T17:45:47.583077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-15T17-45-47.583077.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.4115981543624161,
"em_stderr": 0.005039800830998692,
"f1": 0.4835130033557058,
"f1_stderr": 0.004781616038785324,
"acc": 0.4006105292510111,
"acc_stderr": 0.008506118979991088
},
"harness|drop|3": {
"em": 0.4115981543624161,
"em_stderr": 0.005039800830998692,
"f1": 0.4835130033557058,
"f1_stderr": 0.004781616038785324
},
"harness|gsm8k|5": {
"acc": 0.0356330553449583,
"acc_stderr": 0.005106107853744191
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237985
}
}
```
### 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]
|
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