id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
turkish-nlp-suite/vitamins-supplements-reviews | 2023-09-23T18:34:47.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:tr",
"license:cc-by-sa-4.0",
"region:us"
] | turkish-nlp-suite | Customer reviews dataset for Turkish. Includes reviews for vitamins and supplement products,crawled from e-commerce website Vitaminler.com. All reviews are in Turkish.[Vitamins and Supplements Customer Reviews Dataset](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews) | @inproceedings{altinok-2023-diverse,
title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
author = "Altinok, Duygu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.768",
pages = "13739--13750",
abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
} | null | 0 | 0 | ---
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Vitamins and Supplements Customer Reviews Dataset
---
# Dataset Card for turkish-nlp-suite/vitamins-supplements-reviews
<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/supplements.png" width="20%" height="20%">
### Dataset Description
- **Repository:** [Vitamins and Supplements Reviews Dataset](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews)
- **Paper:** [ACL link](https://aclanthology.org/2023.acl-long.768/)
- **Dataset:** Vitamins and Supplements Reviews Dataset
- **Domain:** E-commerce, customer reviews
### Dataset Summary
Turkish sentiment analysis dataset from customer reviews about supplement and vitamin products. The dataset is scraped from Vitaminler.com and contains
customer reviews and star rating about vitamin and supplement products.
Each customer review in the Vitamins and Supplements Reviews Dataset describes a customer’s experience with a supplement product in terms of the product’s effectiveness,
side effects, taste and smell, as well as comments on supplement usage frequency and dosage, active ingredients, brand, and similar products by other brands. The reviews
also include pointers to customers’ health history and indications how the supplements helped in resolving customers’ health problems.
Considering the characteristics of the data, our Vitamins and Supplements Reviews Dataset lies at the intersection of customer review data and healthcare NLP data. We hope
to offer a finely compiled medical NLP dataset for Turkish NLU.
### Dataset Instances
The dataset includes 1,052 products of 262 distinct brands with 244K customer reviews. During the compilation, we eliminated reviews containing person names such as customer's name and influencer names
Each dataset instance contains
- product name
- brand name
- customer review text
- star rating
Here's an example for you:
```
{
"product_name": "Microfer Şurup 250 ml",
"brand": "Ocean",
"review": "Bittikçe alıyorum harika bişey kızım tadını da seviyo",
"star": 5
}
```
If you're rather interested in JSON format where reviews are accumulated by product name, you can find the dataset as a single JSON in dataset's [Github repo](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews).
### Data Split
| name |train|validation|test|
|---------|----:|---:|---:|
|Vitamins and Supplements Reviews|200866|20000|20000|
### Citation
This work is supported by Google Developer Experts Program. Part of Duygu 2022 Fall-Winter collection, "Turkish NLP with Duygu"/ "Duygu'yla Türkçe NLP". All rights reserved. If you'd like to use this dataset in your own work, please kindly cite [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/) :
```
@inproceedings{altinok-2023-diverse,
title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
author = "Altinok, Duygu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.768",
pages = "13739--13750",
abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}
```
|
open-llm-leaderboard/details_bhenrym14__airophin-v2-13b-PI-8k-fp16 | 2023-09-22T17:43:22.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
open-llm-leaderboard/details_FelixChao__vicuna-7B-chemical | 2023-09-22T17:46:29.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of FelixChao/vicuna-7B-chemical
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [FelixChao/vicuna-7B-chemical](https://huggingface.co/FelixChao/vicuna-7B-chemical)\
\ 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_FelixChao__vicuna-7B-chemical\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:46:17.694402](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__vicuna-7B-chemical/blob/main/results_2023-09-22T17-46-17.694402.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\": 0.005557885906040268,\n\
\ \"em_stderr\": 0.0007613497667018453,\n \"f1\": 0.06261220637583904,\n\
\ \"f1_stderr\": 0.0014974766629904516,\n \"acc\": 0.3525119781135765,\n\
\ \"acc_stderr\": 0.009072291049445833\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.005557885906040268,\n \"em_stderr\": 0.0007613497667018453,\n\
\ \"f1\": 0.06261220637583904,\n \"f1_stderr\": 0.0014974766629904516\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03335860500379075,\n \
\ \"acc_stderr\": 0.004946282649173776\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6716653512233622,\n \"acc_stderr\": 0.013198299449717888\n\
\ }\n}\n```"
repo_url: https://huggingface.co/FelixChao/vicuna-7B-chemical
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_46_17.694402
path:
- '**/details_harness|drop|3_2023-09-22T17-46-17.694402.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-46-17.694402.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_46_17.694402
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-46-17.694402.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-46-17.694402.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_46_17.694402
path:
- '**/details_harness|winogrande|5_2023-09-22T17-46-17.694402.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-46-17.694402.parquet'
- config_name: results
data_files:
- split: 2023_09_22T17_46_17.694402
path:
- results_2023-09-22T17-46-17.694402.parquet
- split: latest
path:
- results_2023-09-22T17-46-17.694402.parquet
---
# Dataset Card for Evaluation run of FelixChao/vicuna-7B-chemical
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FelixChao/vicuna-7B-chemical
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [FelixChao/vicuna-7B-chemical](https://huggingface.co/FelixChao/vicuna-7B-chemical) 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_FelixChao__vicuna-7B-chemical",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:46:17.694402](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__vicuna-7B-chemical/blob/main/results_2023-09-22T17-46-17.694402.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": 0.005557885906040268,
"em_stderr": 0.0007613497667018453,
"f1": 0.06261220637583904,
"f1_stderr": 0.0014974766629904516,
"acc": 0.3525119781135765,
"acc_stderr": 0.009072291049445833
},
"harness|drop|3": {
"em": 0.005557885906040268,
"em_stderr": 0.0007613497667018453,
"f1": 0.06261220637583904,
"f1_stderr": 0.0014974766629904516
},
"harness|gsm8k|5": {
"acc": 0.03335860500379075,
"acc_stderr": 0.004946282649173776
},
"harness|winogrande|5": {
"acc": 0.6716653512233622,
"acc_stderr": 0.013198299449717888
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_baichuan-inc__Baichuan-7B | 2023-09-22T17:53:14.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
riquinho21/digovoz | 2023-09-22T18:00:21.000Z | [
"license:cc0-1.0",
"region:us"
] | riquinho21 | null | null | null | 0 | 0 | ---
license: cc0-1.0
---
|
isaiah08/estrange-ai | 2023-09-22T18:01:11.000Z | [
"region:us"
] | isaiah08 | null | null | null | 0 | 0 | Entry not found |
gollark/consciousness | 2023-09-22T18:18:51.000Z | [
"region:us"
] | gollark | null | null | null | 0 | 0 | Papers on consciousness extracted from PDF format, from Cognition and Consciousness, arXiv and JCER. |
Arthur91284/Arthur91284 | 2023-09-23T15:39:54.000Z | [
"license:openrail",
"region:us"
] | Arthur91284 | null | null | null | 0 | 0 | ---
license: openrail
---
|
quocanh34/test_result | 2023-09-22T18:28:49.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 0 | Entry not found |
Eu001/Teste | 2023-09-22T23:16:05.000Z | [
"license:openrail",
"region:us"
] | Eu001 | null | null | null | 0 | 0 | ---
license: openrail
---
|
open-llm-leaderboard/details_luffycodes__higgs-llama-vicuna-ep25-70b | 2023-09-22T18:39:04.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of luffycodes/higgs-llama-vicuna-ep25-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [luffycodes/higgs-llama-vicuna-ep25-70b](https://huggingface.co/luffycodes/higgs-llama-vicuna-ep25-70b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_luffycodes__higgs-llama-vicuna-ep25-70b\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-22T18:37:41.856857](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__higgs-llama-vicuna-ep25-70b/blob/main/results_2023-09-22T18-37-41.856857.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\": 0.6420797619490708,\n\
\ \"acc_stderr\": 0.03261955033835842,\n \"acc_norm\": 0.6458841283778145,\n\
\ \"acc_norm_stderr\": 0.0325948592191213,\n \"mc1\": 0.3684210526315789,\n\
\ \"mc1_stderr\": 0.016886551261046042,\n \"mc2\": 0.5374973802082814,\n\
\ \"mc2_stderr\": 0.015377782429548816\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5853242320819113,\n \"acc_stderr\": 0.014397070564409174,\n\
\ \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192601\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.673770165305716,\n\
\ \"acc_stderr\": 0.004678743563766661,\n \"acc_norm\": 0.8606851224855606,\n\
\ \"acc_norm_stderr\": 0.003455671196993115\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n\
\ \"acc_stderr\": 0.043192236258113303,\n \"acc_norm\": 0.4962962962962963,\n\
\ \"acc_norm_stderr\": 0.043192236258113303\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03523807393012047,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03523807393012047\n \
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6528301886792452,\n \"acc_stderr\": 0.029300101705549652,\n\
\ \"acc_norm\": 0.6528301886792452,\n \"acc_norm_stderr\": 0.029300101705549652\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\
\ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\
\ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\
\ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929775,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929775\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\
\ \"acc_stderr\": 0.04598188057816541,\n \"acc_norm\": 0.39473684210526316,\n\
\ \"acc_norm_stderr\": 0.04598188057816541\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055287,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055287\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031083,\n \"\
acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031083\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047709,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047709\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033442,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033442\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.02822644674968352,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.02822644674968352\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.02934457250063435,\n \
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.02934457250063435\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.44370860927152317,\n \"acc_stderr\": 0.04056527902281732,\n \"\
acc_norm\": 0.44370860927152317,\n \"acc_norm_stderr\": 0.04056527902281732\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700464,\n \"\
acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700464\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8725490196078431,\n \"acc_stderr\": 0.023405530480846322,\n \"\
acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.023405530480846322\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8312236286919831,\n \"acc_stderr\": 0.024381406832586234,\n \
\ \"acc_norm\": 0.8312236286919831,\n \"acc_norm_stderr\": 0.024381406832586234\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7219730941704036,\n\
\ \"acc_stderr\": 0.03006958487449405,\n \"acc_norm\": 0.7219730941704036,\n\
\ \"acc_norm_stderr\": 0.03006958487449405\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\
\ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406943,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406943\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \
\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8365261813537676,\n\
\ \"acc_stderr\": 0.013223928616741622,\n \"acc_norm\": 0.8365261813537676,\n\
\ \"acc_norm_stderr\": 0.013223928616741622\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3854748603351955,\n\
\ \"acc_stderr\": 0.01627792703963819,\n \"acc_norm\": 0.3854748603351955,\n\
\ \"acc_norm_stderr\": 0.01627792703963819\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\
\ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5078226857887875,\n\
\ \"acc_stderr\": 0.012768673076111903,\n \"acc_norm\": 0.5078226857887875,\n\
\ \"acc_norm_stderr\": 0.012768673076111903\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5845588235294118,\n \"acc_stderr\": 0.02993534270787774,\n\
\ \"acc_norm\": 0.5845588235294118,\n \"acc_norm_stderr\": 0.02993534270787774\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507205,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507205\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\
\ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\
\ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7836734693877551,\n \"acc_stderr\": 0.02635891633490403,\n\
\ \"acc_norm\": 0.7836734693877551,\n \"acc_norm_stderr\": 0.02635891633490403\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482708,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482708\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3684210526315789,\n\
\ \"mc1_stderr\": 0.016886551261046042,\n \"mc2\": 0.5374973802082814,\n\
\ \"mc2_stderr\": 0.015377782429548816\n }\n}\n```"
repo_url: https://huggingface.co/luffycodes/higgs-llama-vicuna-ep25-70b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|arc:challenge|25_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hellaswag|10_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-37-41.856857.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-37-41.856857.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T18-37-41.856857.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T18-37-41.856857.parquet'
- config_name: results
data_files:
- split: 2023_09_22T18_37_41.856857
path:
- results_2023-09-22T18-37-41.856857.parquet
- split: latest
path:
- results_2023-09-22T18-37-41.856857.parquet
---
# Dataset Card for Evaluation run of luffycodes/higgs-llama-vicuna-ep25-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/luffycodes/higgs-llama-vicuna-ep25-70b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [luffycodes/higgs-llama-vicuna-ep25-70b](https://huggingface.co/luffycodes/higgs-llama-vicuna-ep25-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_luffycodes__higgs-llama-vicuna-ep25-70b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T18:37:41.856857](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__higgs-llama-vicuna-ep25-70b/blob/main/results_2023-09-22T18-37-41.856857.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": 0.6420797619490708,
"acc_stderr": 0.03261955033835842,
"acc_norm": 0.6458841283778145,
"acc_norm_stderr": 0.0325948592191213,
"mc1": 0.3684210526315789,
"mc1_stderr": 0.016886551261046042,
"mc2": 0.5374973802082814,
"mc2_stderr": 0.015377782429548816
},
"harness|arc:challenge|25": {
"acc": 0.5853242320819113,
"acc_stderr": 0.014397070564409174,
"acc_norm": 0.6228668941979523,
"acc_norm_stderr": 0.014163366896192601
},
"harness|hellaswag|10": {
"acc": 0.673770165305716,
"acc_stderr": 0.004678743563766661,
"acc_norm": 0.8606851224855606,
"acc_norm_stderr": 0.003455671196993115
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4962962962962963,
"acc_stderr": 0.043192236258113303,
"acc_norm": 0.4962962962962963,
"acc_norm_stderr": 0.043192236258113303
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.75,
"acc_stderr": 0.03523807393012047,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03523807393012047
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6528301886792452,
"acc_stderr": 0.029300101705549652,
"acc_norm": 0.6528301886792452,
"acc_norm_stderr": 0.029300101705549652
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.29411764705882354,
"acc_stderr": 0.04533838195929775,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929775
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
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"acc_norm_stderr": 0.035123852837050475
},
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},
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"acc_norm_stderr": 0.021262719400406943
},
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},
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},
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},
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"acc_norm_stderr": 0.02591780611714716
},
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"acc_norm": 0.7009646302250804,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7253086419753086,
"acc_norm_stderr": 0.024836057868294677
},
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},
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"acc_norm_stderr": 0.012768673076111903
},
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"acc_norm_stderr": 0.02993534270787774
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
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"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.018975427920507205
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7363636363636363,
"acc_stderr": 0.04220224692971987,
"acc_norm": 0.7363636363636363,
"acc_norm_stderr": 0.04220224692971987
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7836734693877551,
"acc_stderr": 0.02635891633490403,
"acc_norm": 0.7836734693877551,
"acc_norm_stderr": 0.02635891633490403
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482708,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482708
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.038899512528272166,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3684210526315789,
"mc1_stderr": 0.016886551261046042,
"mc2": 0.5374973802082814,
"mc2_stderr": 0.015377782429548816
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_GeorgiaTechResearchInstitute__galactica-6.7b-evol-instruct-70k | 2023-09-22T18:49:20.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
Isaacsa7/Modelo_Treinado | 2023-09-22T19:14:22.000Z | [
"region:us"
] | Isaacsa7 | null | null | null | 0 | 0 | Entry not found |
NyxSlee/cool_new_dataset | 2023-09-22T19:02:38.000Z | [
"region:us"
] | NyxSlee | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: name
dtype: string
- name: description
dtype: string
- name: price
dtype: float64
- name: color
dtype: string
- name: size
sequence: string
- name: ad
dtype: string
splits:
- name: train
num_bytes: 5020
num_examples: 5
download_size: 11617
dataset_size: 5020
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cool_new_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
erenoz/your-dataset-name | 2023-09-22T19:03:05.000Z | [
"region:us"
] | erenoz | null | null | null | 0 | 0 | Entry not found |
axelprsvl/my_dataset | 2023-09-22T19:03:21.000Z | [
"region:us"
] | axelprsvl | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 40520175.0
num_examples: 5
download_size: 40474142
dataset_size: 40520175.0
---
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mmuttharasan/llmjp2 | 2023-09-22T19:19:58.000Z | [
"region:us"
] | mmuttharasan | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 592043
num_examples: 1
download_size: 0
dataset_size: 592043
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llmjp2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jgp756/m | 2023-09-22T19:32:56.000Z | [
"license:openrail",
"region:us"
] | jgp756 | null | null | null | 0 | 0 | ---
license: openrail
---
|
Raspado/Lucasdataset | 2023-09-22T19:45:04.000Z | [
"region:us"
] | Raspado | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_zarakiquemparte__zarafusionix-l2-7b | 2023-09-22T19:56:23.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
germanbava/isawa | 2023-09-22T20:15:21.000Z | [
"region:us"
] | germanbava | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_Azure99__blossom-v1-3b | 2023-09-22T20:19:18.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of Azure99/blossom-v1-3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Azure99/blossom-v1-3b](https://huggingface.co/Azure99/blossom-v1-3b) 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_Azure99__blossom-v1-3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T20:19:06.674002](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v1-3b/blob/main/results_2023-09-22T20-19-06.674002.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\": 0.035968959731543626,\n\
\ \"em_stderr\": 0.0019069930004768894,\n \"f1\": 0.08654886744966468,\n\
\ \"f1_stderr\": 0.002229945283926482,\n \"acc\": 0.2962915868075896,\n\
\ \"acc_stderr\": 0.007760914549413539\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.035968959731543626,\n \"em_stderr\": 0.0019069930004768894,\n\
\ \"f1\": 0.08654886744966468,\n \"f1_stderr\": 0.002229945283926482\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.0016927007401502012\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5887924230465666,\n \"acc_stderr\": 0.013829128358676876\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Azure99/blossom-v1-3b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T20_19_06.674002
path:
- '**/details_harness|drop|3_2023-09-22T20-19-06.674002.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T20-19-06.674002.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T20_19_06.674002
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-19-06.674002.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-19-06.674002.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T20_19_06.674002
path:
- '**/details_harness|winogrande|5_2023-09-22T20-19-06.674002.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T20-19-06.674002.parquet'
- config_name: results
data_files:
- split: 2023_09_22T20_19_06.674002
path:
- results_2023-09-22T20-19-06.674002.parquet
- split: latest
path:
- results_2023-09-22T20-19-06.674002.parquet
---
# Dataset Card for Evaluation run of Azure99/blossom-v1-3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Azure99/blossom-v1-3b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Azure99/blossom-v1-3b](https://huggingface.co/Azure99/blossom-v1-3b) 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_Azure99__blossom-v1-3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T20:19:06.674002](https://huggingface.co/datasets/open-llm-leaderboard/details_Azure99__blossom-v1-3b/blob/main/results_2023-09-22T20-19-06.674002.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": 0.035968959731543626,
"em_stderr": 0.0019069930004768894,
"f1": 0.08654886744966468,
"f1_stderr": 0.002229945283926482,
"acc": 0.2962915868075896,
"acc_stderr": 0.007760914549413539
},
"harness|drop|3": {
"em": 0.035968959731543626,
"em_stderr": 0.0019069930004768894,
"f1": 0.08654886744966468,
"f1_stderr": 0.002229945283926482
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.0016927007401502012
},
"harness|winogrande|5": {
"acc": 0.5887924230465666,
"acc_stderr": 0.013829128358676876
}
}
```
### 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] |
collabora/librilight-processed-webdataset | 2023-10-04T12:05:17.000Z | [
"license:cc0-1.0",
"region:us"
] | collabora | null | null | null | 0 | 0 | ---
license: cc0-1.0
---
|
Vaibhav9401/toxic25m | 2023-09-23T06:20:30.000Z | [
"region:us"
] | Vaibhav9401 | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: llama_finetune_text
dtype: string
splits:
- name: train
num_bytes: 20143312184
num_examples: 25159680
download_size: 3446911922
dataset_size: 20143312184
---
# Dataset Card for "toxic25m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca | 2023-09-22T20:56:21.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
thejaskp/random | 2023-09-22T21:01:46.000Z | [
"region:us"
] | thejaskp | null | null | null | 0 | 0 | Entry not found |
RadicalRendy/jakdataset | 2023-09-22T21:23:48.000Z | [
"license:openrail",
"region:us"
] | RadicalRendy | null | null | null | 0 | 0 | ---
license: openrail
---
|
open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA-OpenOrca | 2023-09-22T21:36:50.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
ShuhongZheng/3D-LLM | 2023-10-10T18:47:22.000Z | [
"arxiv:2307.12981",
"region:us"
] | ShuhongZheng | null | null | null | 0 | 0 | https://arxiv.org/abs/2307.12981 |
Viniciaao/HardLevel | 2023-09-22T22:09:51.000Z | [
"license:openrail",
"region:us"
] | Viniciaao | null | null | null | 0 | 0 | ---
license: openrail
---
|
dhenypatungka/dpXRealism | 2023-09-22T23:29:00.000Z | [
"region:us"
] | dhenypatungka | null | null | null | 0 | 0 | Entry not found |
Roscall/Alice | 2023-09-22T23:28:39.000Z | [
"region:us"
] | Roscall | null | null | null | 0 | 0 | Entry not found |
DevilCaos/lucas | 2023-09-22T23:56:41.000Z | [
"license:unknown",
"region:us"
] | DevilCaos | null | null | null | 0 | 0 | ---
license: unknown
---
|
dongyoung4091/shp-generated_flan_t5_large_flan_t5_small_zeroshot | 2023-09-23T00:45:13.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: zeroshot_helpfulness
dtype: float64
- name: zeroshot_specificity
dtype: float64
- name: zeroshot_intent
dtype: float64
- name: zeroshot_factuality
dtype: float64
- name: zeroshot_easy-to-understand
dtype: float64
- name: zeroshot_relevance
dtype: float64
- name: zeroshot_readability
dtype: float64
- name: zeroshot_enough-detail
dtype: float64
- name: 'zeroshot_biased:'
dtype: float64
- name: zeroshot_fail-to-consider-individual-preferences
dtype: float64
- name: zeroshot_repetetive
dtype: float64
- name: zeroshot_fail-to-consider-context
dtype: float64
- name: zeroshot_too-long
dtype: float64
splits:
- name: train
num_bytes: 29493865
num_examples: 25600
download_size: 1808580
dataset_size: 29493865
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "shp-generated_flan_t5_large_flan_t5_small_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dongyoung4091/shp_with_features_20k_flan_t5_large_flan_t5_small_zeroshot | 2023-09-23T00:47:12.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: post_id
dtype: string
- name: domain
dtype: string
- name: upvote_ratio
dtype: float64
- name: history
dtype: string
- name: c_root_id_A
dtype: string
- name: c_root_id_B
dtype: string
- name: created_at_utc_A
dtype: int64
- name: created_at_utc_B
dtype: int64
- name: score_A
dtype: int64
- name: score_B
dtype: int64
- name: human_ref_A
dtype: string
- name: human_ref_B
dtype: string
- name: labels
dtype: int64
- name: seconds_difference
dtype: float64
- name: score_ratio
dtype: float64
- name: helpfulness_A
dtype: float64
- name: helpfulness_B
dtype: float64
- name: specificity_A
dtype: float64
- name: specificity_B
dtype: float64
- name: intent_A
dtype: float64
- name: intent_B
dtype: float64
- name: factuality_A
dtype: float64
- name: factuality_B
dtype: float64
- name: easy-to-understand_A
dtype: float64
- name: easy-to-understand_B
dtype: float64
- name: relevance_A
dtype: float64
- name: relevance_B
dtype: float64
- name: readability_A
dtype: float64
- name: readability_B
dtype: float64
- name: enough-detail_A
dtype: float64
- name: enough-detail_B
dtype: float64
- name: biased:_A
dtype: float64
- name: biased:_B
dtype: float64
- name: fail-to-consider-individual-preferences_A
dtype: float64
- name: fail-to-consider-individual-preferences_B
dtype: float64
- name: repetetive_A
dtype: float64
- name: repetetive_B
dtype: float64
- name: fail-to-consider-context_A
dtype: float64
- name: fail-to-consider-context_B
dtype: float64
- name: too-long_A
dtype: float64
- name: too-long_B
dtype: float64
- name: __index_level_0__
dtype: int64
- name: log_score_A
dtype: float64
- name: log_score_B
dtype: float64
- name: zeroshot_helpfulness_A
dtype: float64
- name: zeroshot_helpfulness_B
dtype: float64
- name: zeroshot_specificity_A
dtype: float64
- name: zeroshot_specificity_B
dtype: float64
- name: zeroshot_intent_A
dtype: float64
- name: zeroshot_intent_B
dtype: float64
- name: zeroshot_factuality_A
dtype: float64
- name: zeroshot_factuality_B
dtype: float64
- name: zeroshot_easy-to-understand_A
dtype: float64
- name: zeroshot_easy-to-understand_B
dtype: float64
- name: zeroshot_relevance_A
dtype: float64
- name: zeroshot_relevance_B
dtype: float64
- name: zeroshot_readability_A
dtype: float64
- name: zeroshot_readability_B
dtype: float64
- name: zeroshot_enough-detail_A
dtype: float64
- name: zeroshot_enough-detail_B
dtype: float64
- name: zeroshot_biased:_A
dtype: float64
- name: zeroshot_biased:_B
dtype: float64
- name: zeroshot_fail-to-consider-individual-preferences_A
dtype: float64
- name: zeroshot_fail-to-consider-individual-preferences_B
dtype: float64
- name: zeroshot_repetetive_A
dtype: float64
- name: zeroshot_repetetive_B
dtype: float64
- name: zeroshot_fail-to-consider-context_A
dtype: float64
- name: zeroshot_fail-to-consider-context_B
dtype: float64
- name: zeroshot_too-long_A
dtype: float64
- name: zeroshot_too-long_B
dtype: float64
splits:
- name: train
num_bytes: 22674534
num_examples: 9459
- name: test
num_bytes: 22627412
num_examples: 9459
download_size: 24128568
dataset_size: 45301946
---
# Dataset Card for "shp_with_features_20k_flan_t5_large_flan_t5_small_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ocolegro/book_names_and_fields | 2023-09-23T01:44:31.000Z | [
"region:us"
] | ocolegro | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: name
dtype: string
- name: persons
list:
- name: id
dtype: string
- name: name
dtype: string
- name: year
dtype: float64
- name: field_name
dtype: string
splits:
- name: train
num_bytes: 218318565
num_examples: 1480516
download_size: 123891575
dataset_size: 218318565
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "book_names_and_fields"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TheVarunKaushik/US_History | 2023-09-23T01:46:42.000Z | [
"license:openrail",
"region:us"
] | TheVarunKaushik | null | null | null | 0 | 0 | ---
license: openrail
---
|
Ialoris/test2 | 2023-09-23T02:36:04.000Z | [
"license:mit",
"region:us"
] | Ialoris | null | null | null | 0 | 0 | ---
license: mit
---
|
proanimer/anime_face | 2023-09-23T04:26:02.000Z | [
"language:en",
"license:mit",
"region:us"
] | proanimer | null | null | null | 0 | 0 | ---
license: mit
language:
- en
--- |
OscarHenry94/oscarhenry | 2023-09-23T02:58:47.000Z | [
"region:us"
] | OscarHenry94 | null | null | null | 0 | 0 | Entry not found |
GreenDaFox/BobVelseb | 2023-09-23T04:09:25.000Z | [
"region:us"
] | GreenDaFox | null | null | null | 0 | 0 | |
benxh/libgen_titles | 2023-09-23T10:03:24.000Z | [
"size_categories:1M<n<10M",
"language:en",
"language:ru",
"language:uk",
"language:de",
"language:fr",
"region:us"
] | benxh | null | null | null | 1 | 0 | ---
language:
- en
- ru
- uk
- de
- fr
size_categories:
- 1M<n<10M
---
# All of libgen[dot]rs non-fiction
~4 Million records straight from the source. No summaries/descriptions.
Useful for synthetic textbook generation, when you run out of ideas, just sample book titles and topics from this dataset. |
ptx0/midjourney-52-showcase | 2023-09-23T04:56:48.000Z | [
"license:unlicense",
"region:us"
] | ptx0 | null | null | null | 0 | 0 | ---
license: unlicense
---
|
Ediudo/rodolfo | 2023-09-23T04:52:16.000Z | [
"region:us"
] | Ediudo | null | null | null | 0 | 0 | Entry not found |
nlplabtdtu/diem_chuan_dai_hoc | 2023-09-23T04:59:58.000Z | [
"region:us"
] | nlplabtdtu | null | null | null | 0 | 0 | Entry not found |
larryvrh/belle_filtered_2.8M_CN | 2023-09-23T05:28:23.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1M<n<10M",
"language:zh",
"license:gpl-3.0",
"region:us"
] | larryvrh | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 4151854934
num_examples: 2802515
download_size: 2513439396
dataset_size: 4151854934
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: gpl-3.0
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- 1M<n<10M
---
# Dataset Card for "belle_filtered_2.8M_CN"
通过启发式方法过滤后的[BelleGroup/train_3.5M_CN](https://huggingface.co/datasets/BelleGroup/train_3.5M_CN)，移除了语料中明显的错误和低质量内容。
## 样例
```
{
"id":"66623028",
"conversations":[
{
"from":"human",
"value":"归纳介绍三种不同类型的电影题材"
},
{
"from":"assistant",
"value":"1. 动作片：动作片通常强调动作和战斗场面，故事情节紧凑，充满了紧张和兴奋的氛围。主角一般是具有超凡实力或特殊技能的英雄人物，往往会与反派角色进行一系列激烈的格斗，追逐和爆破等酷炫场面。例如《速度与激情》和《复仇者联盟》等。\n2. 爱情片：爱情片是以爱情为主题的电影，着重呈现角色之间的感情纠葛，展现了爱情的美好和悲壮。情节与角色之间的发展令人深思，增加了观众的感性体验。这种电影的故事情节主要以人类与人类之间的情感变化为主题。例如《泰坦尼克号》和《让子弹飞》等。\n3. 科幻片：科幻片是以未来科技和未知世界为主题的电影，通常包括时间旅行、外星人、虚拟现实、未来社会等各种奇幻的元素。这种电影描绘了一种比现实更加宏伟、更加神秘和惊奇的世界。例如《星际穿越》和《发条橙》等。"
}
]
}
{
"id":"18205437",
"conversations":[
{
"from":"human",
"value":"根据给定的食材，编写一道美食菜谱。\n鸡腿肉、蘑菇、洋葱"
},
{
"from":"assistant",
"value":"鸡肉炒蘑菇洋葱\n食材：\n- 鸡腿肉 400克，去骨去皮切成小块\n- 蘑菇 200克，切片\n- 洋葱 1个，切丝\n- 生姜 适量，切丝\n- 酱油 2勺\n- 料酒 1勺\n- 盐 适量\n- 白胡椒粉 适量\n- 植物油 适量\n做法：\n1. 在一个碗里放入切好的鸡腿肉块，加入酱油、料酒和适量的白胡椒粉，搅拌均匀，腌制10分钟。\n2. 热锅凉油，加入姜丝翻炒，放入腌好的鸡肉块翻炒至变色。\n3. 加入洋葱丝和蘑菇，翻炒2-3分钟至蘑菇变软。\n4. 加入适量的盐和少量的水，盖上锅盖焖煮5分钟直至鸡肉熟透。\n5. 最后翻炒几下，使汤汁变浓稠即可。\n温馨提示：\n1. 在腌制鸡肉时，如果想让肉质更嫩滑，可以加入少量的淀粉和蛋清。\n2. 可以根据个人口味加入其他调料，如蒜末、辣椒等。\n3. 洋葱和蘑菇可以用其他蔬菜代替，如青椒、胡萝卜等。"
},
{
"from":"human",
"value":"太好了，我会试着照着你教的做一次，但是我最近买了一部新相机，想让你推荐一些好的拍照地点。"
},
{
"from":"assistant",
"value":"当地的风景区、公园、景点等等都是比较适合拍照的地点。如果你想要一些特别的地方，可以考虑一些建筑群，街道巷子、咖啡馆、书店等具有文艺气息的地方。"
},
{
"from":"human",
"value":"嗯，这些地方都很不错，但是我还想找一些可以体验当地文化和风俗的地点，你有什么好的建议吗？"
},
{
"from":"assistant",
"value":"如果你想要更深入地了解当地文化和风俗，可以考虑参加一些游学活动，比如参观当地的博物馆、参加民俗节庆、尝试当地美食等等。此外，也可以去一些小城镇，感受那里的古朴风情。"
}
]
}
```
### 字段：
```
id: 数据id
conversations: 数据内容，以对话形式给出，包括多轮和单轮对话的数据
category: 数据类别
```
过滤后的数据构成：
| Category | Count |
|----------------|---------:|
| close qa | 112,570 |
| classification | 125,623 |
| extract | 6,400 |
| open qa | 385,306 |
| harmless | 45,968 |
| role playing | 465,782 |
| rewrite | 28,146 |
| code | 180,825 |
| translation | 29,923 |
| summarization | 99,017 |
| math | 106,202 |
| generation |1,023,643 |
| brainstorming | 193,110 | |
morosCORP/governmentschemes | 2023-09-23T05:38:17.000Z | [
"license:afl-3.0",
"region:us"
] | morosCORP | null | null | null | 0 | 0 | ---
license: afl-3.0
---
|
JapanDegitalMaterial/Abandoned_places_in_Japan | 2023-09-23T12:58:42.000Z | [
"task_categories:text-to-image",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
] | JapanDegitalMaterial | null | null | null | 0 | 0 | ---
license: cc0-1.0
language:
- en
- ja
task_categories:
- text-to-image
---
# Abandoned place in Japan
This is a dataset to train text-to-image or other models without any copyright issue.
All materials used in this dataset are CC0 (Public domain /P.D.).
## Dataset Description
- **Homepage:**
- https://www.deviantart.com/japanmaterial
-
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct | 2023-09-23T05:54:45.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
ryacon/CANNABIS | 2023-09-23T05:59:08.000Z | [
"license:openrail",
"region:us"
] | ryacon | null | null | null | 0 | 0 | ---
license: openrail
---
|
marthazh/sanhuanzhonghuinewnew | 2023-09-23T06:28:29.000Z | [
"region:us"
] | marthazh | null | null | null | 0 | 0 | Entry not found |
Falah/luxurious_food_photography_prompts | 2023-09-23T06:31:09.000Z | [
"region:us"
] | Falah | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 116535
num_examples: 1000
download_size: 1927
dataset_size: 116535
---
# Dataset Card for "luxurious_food_photography_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_rinna__bilingual-gpt-neox-4b | 2023-09-23T06:40:11.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of rinna/bilingual-gpt-neox-4b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rinna/bilingual-gpt-neox-4b](https://huggingface.co/rinna/bilingual-gpt-neox-4b)\
\ 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_rinna__bilingual-gpt-neox-4b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T06:39:58.316038](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__bilingual-gpt-neox-4b/blob/main/results_2023-09-23T06-39-58.316038.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\": 0.0019494546979865776,\n \"f1_stderr\"\
: 0.0001656985868155588,\n \"acc\": 0.25927387529597473,\n \"acc_stderr\"\
: 0.007021406854444189\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 0.0019494546979865776,\n \"\
f1_stderr\": 0.0001656985868155588\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5185477505919495,\n \"acc_stderr\": 0.014042813708888378\n\
\ }\n}\n```"
repo_url: https://huggingface.co/rinna/bilingual-gpt-neox-4b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T06_39_58.316038
path:
- '**/details_harness|drop|3_2023-09-23T06-39-58.316038.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T06-39-58.316038.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T06_39_58.316038
path:
- '**/details_harness|gsm8k|5_2023-09-23T06-39-58.316038.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T06-39-58.316038.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T06_39_58.316038
path:
- '**/details_harness|winogrande|5_2023-09-23T06-39-58.316038.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T06-39-58.316038.parquet'
- config_name: results
data_files:
- split: 2023_09_23T06_39_58.316038
path:
- results_2023-09-23T06-39-58.316038.parquet
- split: latest
path:
- results_2023-09-23T06-39-58.316038.parquet
---
# Dataset Card for Evaluation run of rinna/bilingual-gpt-neox-4b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rinna/bilingual-gpt-neox-4b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [rinna/bilingual-gpt-neox-4b](https://huggingface.co/rinna/bilingual-gpt-neox-4b) 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_rinna__bilingual-gpt-neox-4b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T06:39:58.316038](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__bilingual-gpt-neox-4b/blob/main/results_2023-09-23T06-39-58.316038.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": 0.0019494546979865776,
"f1_stderr": 0.0001656985868155588,
"acc": 0.25927387529597473,
"acc_stderr": 0.007021406854444189
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0019494546979865776,
"f1_stderr": 0.0001656985868155588
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5185477505919495,
"acc_stderr": 0.014042813708888378
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_JosephusCheung__Guanaco | 2023-09-23T06:44:14.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
YiYiXu/pr5158 | 2023-09-23T07:52:15.000Z | [
"region:us"
] | YiYiXu | null | null | null | 0 | 0 | Entry not found |
Corianas/StorySalt_Concepts | 2023-09-28T09:24:44.000Z | [
"license:cdla-sharing-1.0",
"region:us"
] | Corianas | null | null | null | 0 | 0 | ---
license: cdla-sharing-1.0
---
This is the story elements extracted from the TinyStories dataset to be used as randomization data for short story generation. |
open-llm-leaderboard/details_openaccess-ai-collective__manticore-13b-chat-pyg | 2023-09-23T08:58:34.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
open-llm-leaderboard/details_sartmis1__starcoder-finetune-selfinstruct | 2023-09-23T09:06:38.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
sauravjoshi23/aws-documentation-chunked | 2023-09-23T17:41:26.000Z | [
"region:us"
] | sauravjoshi23 | null | null | null | 1 | 0 | Entry not found |
Chungfan/biomed-lay-summ | 2023-09-23T09:34:26.000Z | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"biology",
"medical",
"region:us"
] | Chungfan | null | null | null | 0 | 0 | ---
task_categories:
- summarization
language:
- en
tags:
- biology
- medical
pretty_name: f
size_categories:
- 10K<n<100K
--- |
Pulse495/your-dataset-name | 2023-09-23T10:18:47.000Z | [
"region:us"
] | Pulse495 | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_Corianas__Quokka_256m | 2023-09-23T10:44:09.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
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: clementine@hf.co
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:** clementine@hf.co
### 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] |
martinakaduc/hh-rlhf-gpt2-embedding | 2023-09-27T16:54:37.000Z | [
"language:en",
"license:mit",
"region:us"
] | martinakaduc | null | null | null | 0 | 0 | ---
license: mit
language:
- en
dataset_info:
features:
- name: chosen
sequence: float64
- name: rejected
sequence: float64
splits:
- name: train
num_bytes: 1810512224
num_examples: 147244
- name: test
num_bytes: 96179312
num_examples: 7822
download_size: 1565358947
dataset_size: 1906691536
---
|
dongyoung4091/shp-generated_flan_t5_large_flan_t5_large_zeroshot | 2023-09-23T10:59:25.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: zeroshot_helpfulness
dtype: float64
- name: zeroshot_specificity
dtype: float64
- name: zeroshot_intent
dtype: float64
- name: zeroshot_factuality
dtype: float64
- name: zeroshot_easy-to-understand
dtype: float64
- name: zeroshot_relevance
dtype: float64
- name: zeroshot_readability
dtype: float64
- name: zeroshot_enough-detail
dtype: float64
- name: 'zeroshot_biased:'
dtype: float64
- name: zeroshot_fail-to-consider-individual-preferences
dtype: float64
- name: zeroshot_repetetive
dtype: float64
- name: zeroshot_fail-to-consider-context
dtype: float64
- name: zeroshot_too-long
dtype: float64
splits:
- name: train
num_bytes: 29493865
num_examples: 25600
download_size: 1905432
dataset_size: 29493865
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "shp-generated_flan_t5_large_flan_t5_large_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ossaili/test_01 | 2023-09-23T11:03:17.000Z | [
"region:us"
] | ossaili | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 102096.0
num_examples: 1
download_size: 103703
dataset_size: 102096.0
---
# Dataset Card for "test_01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hohlederbetrug/hohlederbetrug | 2023-09-23T11:05:57.000Z | [
"region:us"
] | hohlederbetrug | null | null | null | 0 | 0 | Entry not found |
ayoubkirouane/One-Piece-anime-captions | 2023-09-23T11:20:10.000Z | [
"region:us"
] | ayoubkirouane | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 28504098.0
num_examples: 856
download_size: 28452041
dataset_size: 28504098.0
---
# Dataset Card for "One-Piece-anime-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
taldarim/ar-higher | 2023-09-23T11:23:35.000Z | [
"region:us"
] | taldarim | null | null | null | 0 | 0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: Comprehension
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Configuration
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Crashes
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Implementation
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Performance issue
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Results interpretation
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 373318
num_examples: 280
- name: test
num_bytes: 369328
num_examples: 236
download_size: 186867
dataset_size: 742646
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "ar-higher"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kanoyo/gui | 2023-09-23T11:57:27.000Z | [
"license:mit",
"region:us"
] | kanoyo | null | null | null | 0 | 0 | ---
license: mit
---
|
ixfo/test_hf | 2023-09-23T12:12:39.000Z | [
"region:us"
] | ixfo | null | null | null | 0 | 0 | Entry not found |
JapanDegitalMaterial/Texture_images | 2023-09-23T14:05:23.000Z | [
"task_categories:text-to-image",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
] | JapanDegitalMaterial | null | null | null | 0 | 0 | ---
license: cc0-1.0
task_categories:
- text-to-image
language:
- en
- ja
---
# Textuer images
This is a dataset to train text-to-image or other models without any copyright issue.
All materials used in this dataset are CC0 (Public domain /P.D.).
## Dataset Description
- **Homepage:**
- https://www.deviantart.com/japanmaterial
-
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
Valmy/Hackers_Face_Detection_Image | 2023-09-23T12:29:37.000Z | [
"license:other",
"region:us"
] | Valmy | null | null | null | 0 | 0 | ---
license: other
---
|
isno0907/AFHQv2_CAT_256 | 2023-09-23T12:29:52.000Z | [
"region:us"
] | isno0907 | null | null | null | 0 | 0 | Entry not found |
JapanDegitalMaterial/Objects_in_Japan | 2023-09-23T14:19:40.000Z | [
"license:cc0-1.0",
"region:us"
] | JapanDegitalMaterial | null | null | null | 0 | 0 | ---
license: cc0-1.0
---
# Objects in japan.
This is a dataset to train text-to-image or other models without any copyright issue.
All materials used in this dataset are CC0 (Public domain /P.D.).
## Dataset Description
- **Homepage:**
- https://www.deviantart.com/japanmaterial
-
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
lionpig/myopenia | 2023-09-30T10:03:15.000Z | [
"region:us"
] | lionpig | null | null | null | 0 | 0 | Entry not found |
JapanDegitalMaterial/Places_in_Japan | 2023-09-23T14:00:16.000Z | [
"task_categories:text-to-image",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
] | JapanDegitalMaterial | null | null | null | 0 | 0 | ---
license: cc0-1.0
task_categories:
- text-to-image
language:
- en
- ja
---
# Places in japan.
This is a dataset to train text-to-image or other models without any copyright issue.
All materials used in this dataset are CC0 (Public domain /P.D.).
## Dataset Description
- **Homepage:**
- https://www.deviantart.com/japanmaterial
-
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
BangumiBase/puellamagimadokamagica | 2023-09-29T11:24:42.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | null | 0 | 0 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Puella Magi Madoka Magica
This is the image base of bangumi Puella Magi Madoka Magica, we detected 17 characters, 2197 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 561 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 238 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 29 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 355 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 392 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 45 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 32 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 12 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 15 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 16 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 6 | [Download](10/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 11 | 58 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 150 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 64 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 13 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 13 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 198 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
rigolettofranc/regularization_data | 2023-09-23T16:53:02.000Z | [
"license:openrail",
"region:us"
] | rigolettofranc | null | null | null | 1 | 0 | ---
license: openrail
---
|
SilentAntagonist/ai-generated-music-dataset_singing-vocals-and-instrumental-accompaniments | 2023-09-23T13:45:38.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | SilentAntagonist | null | null | null | 0 | 0 | ---
license: cc-by-nc-4.0
---
|
LiChenYi/china-law | 2023-10-08T11:43:25.000Z | [
"region:us"
] | LiChenYi | null | null | null | 0 | 0 | Entry not found |
Alfred0622/HypR | 2023-09-23T13:34:24.000Z | [
"region:us"
] | Alfred0622 | null | null | null | 0 | 0 | Entry not found |
open-llm-leaderboard/details_tiiuae__falcon-40b-instruct | 2023-09-23T13:36:31.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of tiiuae/falcon-40b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-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_tiiuae__falcon-40b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T13:36:20.116121](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b-instruct/blob/main/results_2023-09-23T13-36-20.116121.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\": 0.008494127516778523,\n\
\ \"em_stderr\": 0.0009398243325411525,\n \"f1\": 0.07122378355704674,\n\
\ \"f1_stderr\": 0.0016125239917803853,\n \"acc\": 0.503219594859419,\n\
\ \"acc_stderr\": 0.011237300869919437\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008494127516778523,\n \"em_stderr\": 0.0009398243325411525,\n\
\ \"f1\": 0.07122378355704674,\n \"f1_stderr\": 0.0016125239917803853\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20849128127369218,\n \
\ \"acc_stderr\": 0.011189587985791428\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.797947908445146,\n \"acc_stderr\": 0.011285013754047448\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-40b-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|drop|3_2023-09-23T13-36-20.116121.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T13-36-20.116121.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|gsm8k|5_2023-09-23T13-36-20.116121.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T13-36-20.116121.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- '**/details_harness|winogrande|5_2023-09-23T13-36-20.116121.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T13-36-20.116121.parquet'
- config_name: results
data_files:
- split: 2023_09_23T13_36_20.116121
path:
- results_2023-09-23T13-36-20.116121.parquet
- split: latest
path:
- results_2023-09-23T13-36-20.116121.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-40b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-40b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-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_tiiuae__falcon-40b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T13:36:20.116121](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b-instruct/blob/main/results_2023-09-23T13-36-20.116121.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": 0.008494127516778523,
"em_stderr": 0.0009398243325411525,
"f1": 0.07122378355704674,
"f1_stderr": 0.0016125239917803853,
"acc": 0.503219594859419,
"acc_stderr": 0.011237300869919437
},
"harness|drop|3": {
"em": 0.008494127516778523,
"em_stderr": 0.0009398243325411525,
"f1": 0.07122378355704674,
"f1_stderr": 0.0016125239917803853
},
"harness|gsm8k|5": {
"acc": 0.20849128127369218,
"acc_stderr": 0.011189587985791428
},
"harness|winogrande|5": {
"acc": 0.797947908445146,
"acc_stderr": 0.011285013754047448
}
}
```
### 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] |
phusroyal/ViHOS | 2023-09-23T19:02:18.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:vi",
"license:mit",
"region:us"
] | phusroyal | This is a dataset of Vietnamese Hate and Offensive Spans dataset from social media texts. | null | null | 2 | 0 | ---
annotations_creators:
- crowdsourced
license: mit
multilinguality:
- monolingual
source_datasets:
- original
task_ids:
- hate-speech-detection
task_categories:
- text-classification
- token-classification
language:
- vi
pretty_name: ViHOS - Vietnamese Hate and Offensive Spans Dataset
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train_sequence_labeling
path:
- "train_sequence_labeling/syllable/train_BIO_syllable.csv"
- "train_sequence_labeling/syllable/dev_BIO_syllable.csv"
- "train_sequence_labeling/syllable/test_BIO_syllable.csv"
- "train_sequence_labeling/word/train_BIO_syllable.csv"
- "train_sequence_labeling/word/dev_BIO_syllable.csv"
- "train_sequence_labeling/word/test_BIO_syllable.csv"
- split: train_span_extraction
path:
- 'train_span_extraction/train.csv'
- 'train_span_extraction/dev.csv'
- split: test
path: "test/test.csv"
---
**Disclaimer**: This project contains real comments that could be considered profane, offensive, or abusive.
# Dataset Card for "ViHOS - Vietnamese Hate and Offensive Spans Dataset"
## Dataset Description
- **Repository:** [ViHOS](https://github.com/phusroyal/ViHOS)
- **Paper:** [EACL-ViHOS](https://aclanthology.org/2023.eacl-main.47/)
- **Total amount of disk used:** 2.6 MB
## Dataset Motivation
The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems.
To help address this issue, we present the ViHOS (**Vi**etnamese **H**ate and **O**ffensive **S**pans) dataset, the first human-annotated corpus containing 26k spans on 11k online comments.
Our goal is to create a dataset that contains comprehensive hate and offensive thoughts, meanings, or opinions within the comments rather than just a lexicon of hate and offensive terms.
We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Futhermore, our solutions to deal with *nine different online foul linguistic phenomena* are also provided in the [*paper*](https://aclanthology.org/2023.eacl-main.47/) (e.g. Teencodes; Metaphors, metonymies; Hyponyms; Puns...).
We hope that this dataset will be useful for researchers and practitioners in the field of hate speech detection in general and hate spans detection in particular.
## Dataset Summary
ViHOS contains 26,476 human-annotated spans on 11,056 comments (5,360 comments have hate and offensive spans, and 5,696 comments do not)
It is splitted into train, dev, and test set with following information:
1. Train set: 8,844 comments
2. Dev set: 1,106 comments
3. Test set: 1,106 comments
## Data Instance
An span extraction-based (see Data Structure for more details) example of 'test' looks as follows:
```
{
"content": "Thối CC chỉ không ngửi đuợc thôi",
'index_spans': "[0, 1, 2, 3, 5, 6]"
}
```
An sequence labeling-based (see Data Structure for more details) example of 'test' looks as follows:
```
{
"content": "Thối CC chỉ không ngửi đuợc thôi",
'index_spans': ["B-T", "I-T", "O", "O", "O", "O", "O"]
}
```
## Data Structure
Here is our data folder structure!
```
.
└── data/
├── train_sequence_labeling/
│ ├── syllable/
│ │ ├── dev_BIO_syllable.csv
│ │ ├── test_BIO_syllable.csv
│ │ └── train_BIO_syllable.csv
│ └── word/
│ ├── dev_BIO_Word.csv
│ ├── test_BIO_Word.csv
│ └── train_BIO_Word.csv
├── train_span_extraction/
│ ├── dev.csv
│ └── train.csv
└── test/
└── test.csv
```
### Sequence labeling-based version
#### Syllable
Description:
- This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns:
- **index**: The id of the word.
- **word**: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer followed by underscore tokenization.
The reason for this is that some words are in bad format:
e.g. "điện.thoại của tôi" is split into ["điện.thoại", "của", "tôi"] instead of ["điện", "thoại", "của", "tôi"] if we use space tokenization, which is not in the right format of Syllable.
As that, we used VnCoreNLP to tokenize first and then split words into tokens.
e.g. "điện.thoại của tôi" ---(VnCoreNLP)---> ["điện_thoại", "của", "tôi"] ---(split by "_")---> ["điện", "thoại", "của", "tôi"].
- **tag**: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word).
- The train_BIO_syllable and dev_BIO_syllable file are used for training and validation for XLMR model, respectively.
- The test_BIO_syllable file is used for reference only. It is not used for testing the model. **Please use the test.csv file in the Testdata folder for testing the model.**
#### Word
Description:
- This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns:
- **index**: The id of the word.
- **word**: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer
- **tag**: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word).
- The train_BIO_Word and dev_BIO_Word file are used for training and validation for PhoBERT model, respectively.
- The test_BIO_Word file is used for reference only. It is not used for testing the model. **Please use the test.csv file in the data/test folder for testing the model.**
### Span Extraction-based version
Description:
- This folder contains the data for the span extraction-based version of the task. The data is divided into two files: train and dev. Each file contains the following columns:
- **content**: The content of the sentence.
- **span_ids**: The index of the hate and offensive spans in the sentence. The index is in the format of [start, end] where start is the index of the first character of the hate and offensive span and end is the index of the last character of the hate and offensive span.
- The train and dev file are used for training and validation for BiLSTM-CRF model, respectively.
### Citation Information
```
@inproceedings{hoang-etal-2023-vihos,
title = "{V}i{HOS}: Hate Speech Spans Detection for {V}ietnamese",
author = "Hoang, Phu Gia and
Luu, Canh Duc and
Tran, Khanh Quoc and
Nguyen, Kiet Van and
Nguyen, Ngan Luu-Thuy",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.47",
doi = "10.18653/v1/2023.eacl-main.47",
pages = "652--669",
abstract = "The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R{\_}Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT{\_}Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Our dataset is released on GitHub.",
}
``` |
mesolitica/NER-augmentation | 2023-09-23T14:09:40.000Z | [
"region:us"
] | mesolitica | null | null | null | 0 | 0 | Entry not found |
carles-undergrad-thesis/indo-mmarco | 2023-09-23T14:34:18.000Z | [
"region:us"
] | carles-undergrad-thesis | null | null | null | 0 | 0 | Entry not found |
MrJoshua217/cagliostro-colab-ui | 2023-09-23T14:36:14.000Z | [
"region:us"
] | MrJoshua217 | null | null | null | 0 | 0 | Entry not found |
xieyizheng/elastic_zip | 2023-09-23T14:46:34.000Z | [
"region:us"
] | xieyizheng | null | null | null | 0 | 0 | Entry not found |
felixdae/length-control | 2023-09-23T15:15:50.000Z | [
"region:us"
] | felixdae | null | null | null | 0 | 0 | source https://worksheets.codalab.org/bundles/0x8b65ebfe46674fbc83fc6df60da32f1b |
LoliOverflow/DeviantArtCollection | 2023-09-23T15:07:58.000Z | [
"region:us"
] | LoliOverflow | null | null | null | 0 | 0 | Entry not found |
CyberHarem/kaname_madoka_puellamagimadokamagica | 2023-09-23T15:32:31.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Kaname Madoka
This is the dataset of Kaname Madoka, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 650 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 650 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 650 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 650 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/akemi_homura_puellamagimadokamagica | 2023-09-23T16:20:00.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Akemi Homura
This is the dataset of Akemi Homura, containing 261 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 261 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 544 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 261 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 261 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 261 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 261 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 261 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 544 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 544 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 544 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Jinyan1/PolitiFact | 2023-09-23T16:35:37.000Z | [
"region:us"
] | Jinyan1 | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: MF
path: data/MF-*
- split: HF
path: data/HF-*
- split: MR
path: data/MR-*
- split: HR
path: data/HR-*
dataset_info:
features:
- name: id
dtype: string
- name: description
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: MF
num_bytes: 164626
num_examples: 97
- name: HF
num_bytes: 266214
num_examples: 97
- name: MR
num_bytes: 641082
num_examples: 132
- name: HR
num_bytes: 3338801
num_examples: 194
download_size: 2380714
dataset_size: 4410723
---
# Dataset Card for "PolitiFact"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ERmak1581/QA_Code | 2023-09-23T16:49:57.000Z | [
"region:us"
] | ERmak1581 | null | null | null | 0 | 0 | QA code on russian language. Based on Den4ikAI/russian_code_qa |
ERmak1581/QA_sberquad | 2023-09-23T16:51:44.000Z | [
"region:us"
] | ERmak1581 | null | null | null | 0 | 0 | Russian QA datasets (small, medium, large) based on sberquad QA data |
CyberHarem/miki_sayaka_puellamagimadokamagica | 2023-09-23T17:23:49.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Miki Sayaka
This is the dataset of Miki Sayaka, containing 284 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 284 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 611 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 284 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 284 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 284 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 284 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 284 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 611 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 611 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 611 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
davanstrien/parl | 2023-09-23T17:40:28.000Z | [
"region:us"
] | davanstrien | null | null | null | 0 | 0 | Entry not found |
CyberHarem/tomoe_mami_puellamagimadokamagica | 2023-09-23T17:43:50.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 0 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Tomoe Mami
This is the dataset of Tomoe Mami, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 454 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 454 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 454 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 454 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
tinhpx2911/vietai_book_data | 2023-09-24T03:59:51.000Z | [
"region:us"
] | tinhpx2911 | null | null | null | 0 | 0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8740094801
num_examples: 15189
download_size: 4515817258
dataset_size: 8740094801
---
# Dataset Card for "vietai_book_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lololyric/Experimental | 2023-09-23T17:50:52.000Z | [
"license:openrail",
"region:us"
] | Lololyric | null | null | null | 0 | 0 | ---
license: openrail
---
|
open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k | 2023-09-23T17:52:01.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 0 | ---
pretty_name: Evaluation run of totally-not-an-llm/EverythingLM-13b-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [totally-not-an-llm/EverythingLM-13b-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-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 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_totally-not-an-llm__EverythingLM-13b-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T17:51:49.550032](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k/blob/main/results_2023-09-23T17-51-49.550032.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\": 0.0025167785234899327,\n\
\ \"em_stderr\": 0.0005131152834514911,\n \"f1\": 0.0588632550335571,\n\
\ \"f1_stderr\": 0.0013761671412880158,\n \"acc\": 0.3960729978284714,\n\
\ \"acc_stderr\": 0.009637044859971106\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834514911,\n\
\ \"f1\": 0.0588632550335571,\n \"f1_stderr\": 0.0013761671412880158\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06444275966641395,\n \
\ \"acc_stderr\": 0.0067633917284882755\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453934\n\
\ }\n}\n```"
repo_url: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|drop|3_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T17-51-49.550032.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-51-49.550032.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|winogrande|5_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T17-51-49.550032.parquet'
- config_name: results
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- results_2023-09-23T17-51-49.550032.parquet
- split: latest
path:
- results_2023-09-23T17-51-49.550032.parquet
---
# Dataset Card for Evaluation run of totally-not-an-llm/EverythingLM-13b-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [totally-not-an-llm/EverythingLM-13b-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-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 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_totally-not-an-llm__EverythingLM-13b-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T17:51:49.550032](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k/blob/main/results_2023-09-23T17-51-49.550032.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": 0.0025167785234899327,
"em_stderr": 0.0005131152834514911,
"f1": 0.0588632550335571,
"f1_stderr": 0.0013761671412880158,
"acc": 0.3960729978284714,
"acc_stderr": 0.009637044859971106
},
"harness|drop|3": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834514911,
"f1": 0.0588632550335571,
"f1_stderr": 0.0013761671412880158
},
"harness|gsm8k|5": {
"acc": 0.06444275966641395,
"acc_stderr": 0.0067633917284882755
},
"harness|winogrande|5": {
"acc": 0.7277032359905288,
"acc_stderr": 0.012510697991453934
}
}
```
### 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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