datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
one-sec-cv12/chunk_28 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 16231343616.0
num_examples: 168992
download_size: 14338516609
dataset_size: 16231343616.0
---
# Dataset Card for "chunk_28"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
clarin-knext/arguana-pl | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl |
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 981
num_examples: 32
download_size: 2055
dataset_size: 981
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
berkeley-nest/Nectar | ---
license: apache-2.0
language:
- en
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/rlaif.parquet
tags:
- RLHF
- RLAIF
- reward model
---
# Dataset Card for Nectar
- **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
- **License:** Apache-2.0 license under the condition that the dataset is not used to compete with OpenAI
Nectar is the first high-quality 7-wise comparison dataset, generated through GPT-4-based ranking. Nectar contains diverse chat prompts, high-quality and diverse responses, and accurate ranking labels. Nectar's prompts are an amalgamation of diverse sources, including [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m), [ShareGPT](https://sharegpt.com/), [Antropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf), [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback), [Evol-Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), and [Flan](https://huggingface.co/datasets/SirNeural/flan_v2). Nectar's 7 responses per prompt are primarily derived from a variety of models, namely GPT-4, GPT-3.5-turbo, GPT-3.5-turbo-instruct, [LLama-2-7B-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), and [Mistral-7B-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), alongside other existing datasets and models. Each prompt's responses are sorted into a 7-wise ranking labeled by GPT-4, resulting in a total of 3.8M pairwise comparisons. Nectar was used to train the reward model [Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) which propelled [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) to an MT-Bench score of 8.09, the current highest for any 7B model.
#### Disclaimer: This dataset contains conversations and responses that are possibly unsafe, offensive, and/or disturbing. These are included only for the purpose of training safer models. Viewer discretion is advised.
## Schema:
```
{
prompt: str, // in format "\n\nHuman: ... \n\nAssistant: "
answers: [
{
answer: str, // the model's response
model: str, // name of the model that generated the response
rank: int // the rank this response recieved
},
...
{
answer: str, // the model's response
model: str, // name of the model that generated the response
rank: int // the rank this response recieved
},
]
turns: int, // number of turns in the conversation
num_response: int, // number of responses for the prompt
source: list[str], // list of the dataset sources for the prompt and answers
good_natured: bool // if the prompt is good natured
}
```
Note: The ```good_natured``` label is derived as a by-product of generating GPT-4 rankings. Since we always first ask GPT-4 if the prompt is good natured before ranking, we were able to parse GPT-4's classification of the prompt's nature to create this label. It is important to note that this label is an approximation generated by GPT-4, and not a representation of the authors' personal beliefs or views.
## Process
### Collecting Prompts
1. For each dataset, generate prompt and answer pairs.
2. For each dataset, group by prompt.
3. Concatenate datasets from (2), down sample according to the following.
a. Take all ShareGPT prompts.
b. Randomly sample without replacement 75,000 Anthropic HH prompts.
c. Take all Ultrafeedback prompts.
d. Randomly sample without replacement 45,000 lmsys prompts with more than 1 answer
4. Group the concatenated dataset by prompt, concatenate the answer lists.
5. Take all prompts that are longer than 40 characters and have more than 1 answer
Notes:
1. When we process the raw datasets, we identify the top 3 languages, if English is not present, we do not take the datapoint.
2. When we group prompts, we use a sanitized version where all non-alphanumeric characters are removed.
### Collecting Responses
To achieve 7 responses for every prompt, we distilled responses from Llama-2-7B-chat, Mistral-7B-instruct, GPT-4, GPT-4-0613, GPT-3.5-turbo, and GPT-3.5-turbo-instruct. GPT-4-0613 was given a system prompt to help set the
chatting context yielding better responses.
From the k responses for each prompt, we pick 7 with the following priority from highest to lowest:
1. GPT-4-0613, GPT-4, Anthropic HH, GPT-3.5-Turbo-Instruct
2. Other models we did not distill and Mistral-7B-Instruct
3. GPT-3.5-Turbo
4. Llama-2-7B-chat
5. Any repeat model (not Llama-2-7B-chat or GPT-3.5-Turbo)
6. Repeated GPT-3.5-Turbo
7. Repeated Llama-2-7B-chat response
8. Default*
*Note that the default response is: "I apologize, but I can’t assist with that request." (regardless of
the user prompt)
As result, we get a dataset with 182,954 prompts and 7 responses per prompt.
### Collecting Rankings
To ranking the 7 responses for each prompt, we ask GPT-4 to rank the responses from best to worse in accordance to our rubric. We implemented several strategies to mitigate positional bias, which is convered in detailed in our paper (coming soon).
Nectar's response rankings are based off both helpfulness and harmlessess. The rubric expects, first and foremost, that all responses should be harmless. Helpfulness should be maximized when responding to good natured prompts.
An excerpt from the GPT-4 judging prompt defines the ranking rubric as follow:
>We have 2 rubrics, one is the helpfulness rubric, one is the harmlessness rubric.
>
>
>The helpfulness rubric is as follows:
> 1. Is the completion relevant? The completion should be the best possible assistant answer.
> 2. Is the completion accurate? The completion should accurately fulfill the prompt’s request.
> 3. If a creative answer is expected, is the completion creative? If an analytical answer is expected, is the completion factual/objectively correct?
> 4. Is the completion detailed? The completion should at minimum satisfy the full level of detail required by the prompt.
>
>The harmlessness rubric is as follows:
> 1. Does the completion adhere to moral and ethical standards?
> 2. Is the completion truthful?
> 3. Is the response polite and respectful?
> 4. If the request given is not good natured, does the completion politely decline to reply while giving a short explanation why they cannot comply with the request?
>
>Completions should always maintain harmlessness in the face of adversarial requests. We may see conversations that are good natured and some that are not good natured. All conversations should be completed in a manner that aligns with the harmlessness rubric. Good natured conversations additionally should align with the helpfulness rubric.
<!-- ## Stats
<img src="images/sources.png" alt="alt text" width="600"/>
<img src="images/model_responses.png" alt="alt text" width="600"/> -->
### Dataset Sources
<!-- Provide the basic links for the model. -->
- **Blog:** https://starling.cs.berkeley.edu/
- **Paper:** Coming soon!
- **Code:** Coming soon!
## License
The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## Acknowledgment
We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.
**✉ Correspondence to:** Banghua Zhu (banghua@berkeley.edu).
## Citation
```
@misc{starling2023,
title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
url = {},
author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao},
month = {November},
year = {2023}
}
``` |
Ali-C137/Arabic-Guanaco-oasst1_Extended | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 38713258
num_examples: 20728
download_size: 20111815
dataset_size: 38713258
---
# Dataset Card for "Arabic-Guanaco-oasst1_Extended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
UWV/Leesplank_NL_wikipedia_simplifications | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
- text-classification
- translation
- text2text-generation
language:
- nl
pretty_name: Leesplank wikipedia vereenvoudigingen
size_categories:
- 1M<n<10M
---
The set contains 2.87M pragraphs of prompt/result combinations, where the prompt is a paragraph from Dutch Wikipedia and the result is a simplified text, which could include more than one paragraph.
This dataset was created by UWV, as a part of project "Leesplank", an effort to generate datasets that are ethically and legally sound.
The basis of this dataset was the wikipedia extract as a part of Gigacorpus (http://gigacorpus.nl/). The lines were fed one by one into GPT 4 1106 preview, where the filtering level was the strictest.
This means that this dataset is cleaned by the [OpenAI filtering standards](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter).
The prompt used was:
"Simplify a Dutch paragraph directly into a single, clear, and engaging text suitable for adult readers that speak Dutch as a second language, using words from the 'basiswoordenlijst Amsterdamse kleuters.' Maintain direct quotes, simplify dialogue, explain cultural references, idioms, and technical terms naturally within the text. Adjust the order of information for improved simplicity, engagement, and readability. Attempt to not use any commas or diminutives."
The prompt was used as a system prompt.
Hyperparameters were: Temperature 0.5, top_P: 0.2
After generation, the resulting prompt/result combinations were filtered where items that had a ratio prompt/result length greater than 4 or smaller than 0.25 were deemed "bad" and are not included in the set.
Another round of processing may be required to filter out items that originated as map descriptions.
|
shibing624/CSC | ---
license: apache-2.0
language:
- zh
tags:
- text-correction
pretty_name: CSC
task_categories:
- text-generation
---
# Dataset Card for CSC
中文拼写纠错数据集
- **Repository:** https://github.com/shibing624/pycorrector
## Dataset Description
Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings.
中文拼写纠错数据集，共27万条，是通过原始SIGHAN13、14、15年数据集和Wang271k数据集合并整理后得到，json格式，带错误字符位置信息。
### Original Dataset Summary
- test.json 和 dev.json 为 **SIGHAN数据集**， 包括SIGHAN13 14 15，来自 [官方csc.html](http://nlp.ee.ncu.edu.tw/resource/csc.html) ，文件大小：339kb，4千条。
- train.json 为 **Wang271k数据集**，包括 Wang271k ，来自 [Automatic-Corpus-Generation dimmywang提供](https://github.com/wdimmy/Automatic-Corpus-Generation/blob/master/corpus/train.sgml) ，文件大小：93MB，27万条。
如果只想用SIGHAN数据集，可以这样取数据：
```python
from datasets import load_dataset
dev_ds = load_dataset('shibing624/CSC', split='validation')
print(dev_ds)
print(dev_ds[0])
test_ds = load_dataset('shibing624/CSC', split='test')
print(test_ds)
print(test_ds[0])
```
### Supported Tasks and Leaderboards
中文拼写纠错任务
The dataset designed for csc task training pretrained language models.
### Languages
The data in CSC are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"id": "B2-4029-3",
"original_text": "晚间会听到嗓音，白天的时候大家都不会太在意，但是在睡觉的时候这嗓音成为大家的恶梦。",
"wrong_ids": [
5,
31
],
"correct_text": "晚间会听到噪音，白天的时候大家都不会太在意，但是在睡觉的时候这噪音成为大家的恶梦。"
}
```
### Data Fields
字段解释：
- id：唯一标识符，无意义
- original_text: 原始错误文本
- wrong_ids： 错误字的位置，从0开始
- correct_text: 纠正后的文本
### Data Splits
| | train | dev | test |
|---------------|------:|--:|--:|
| CSC | 251835条 | 27981条 | 1100条 |
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
```latex
@misc{Xu_Pycorrector_Text_error,
title={Pycorrector: Text error correction tool},
author={Xu Ming},
year={2021},
howpublished={\url{https://github.com/shibing624/pycorrector}},
}
```
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传 |
DGurgurov/nepali_conceptnet | ---
license: mit
---
## ConceptNet Data for the Nepali Language
**Dataset Description:**
This dataset contains data extracted from ConceptNet using the dedicated module for fetching knowledge from the graph, available on [GitHub](https://github.com/d-gurgurov/Conceptnet-Embeddings).
**Data Structure:**
The data is converted from triplets into natural text using a pre-defined relationship mapping and split into training and validation sets. It was used for training language adapters for the project aimed at [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs).
|
joshwe/storiesnew | ---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: validation
num_bytes: 78052
num_examples: 19
- name: train
num_bytes: 82160
num_examples: 20
download_size: 39005
dataset_size: 160212
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
---
|
AnonymousSub/MedQuAD_47441_Context_Question_Answer_Triples | ---
dataset_info:
features:
- name: Contexts
dtype: string
- name: Questions
dtype: string
- name: Answers
dtype: string
splits:
- name: train
num_bytes: 190797665
num_examples: 47441
download_size: 21780319
dataset_size: 190797665
---
# Dataset Card for "MedQuAD_47441_Context_Question_Answer_Triples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4 | ---
pretty_name: Evaluation run of NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NobodyExistsOnTheInternet/PuffedConvo13bLoraE4](https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T05:37:42.901960](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4/blob/main/results_2023-10-16T05-37-42.901960.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\": 0.02506291946308725,\n\
\ \"em_stderr\": 0.0016008246934367607,\n \"f1\": 0.08258703859060393,\n\
\ \"f1_stderr\": 0.0020041497370696586,\n \"acc\": 0.4200579473220307,\n\
\ \"acc_stderr\": 0.009967774108676528\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.02506291946308725,\n \"em_stderr\": 0.0016008246934367607,\n\
\ \"f1\": 0.08258703859060393,\n \"f1_stderr\": 0.0020041497370696586\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08794541319181198,\n \
\ \"acc_stderr\": 0.007801162197487711\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|arc:challenge|25_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|arc:challenge|25_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T05_37_42.901960
path:
- '**/details_harness|drop|3_2023-10-16T05-37-42.901960.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T05-37-42.901960.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T05_37_42.901960
path:
- '**/details_harness|gsm8k|5_2023-10-16T05-37-42.901960.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T05-37-42.901960.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hellaswag|10_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hellaswag|10_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T23:15:40.572782.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T23:15:40.572782.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T23:15:40.572782.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T00-01-07.493301.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T00-01-07.493301.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-28T23:15:40.572782.parquet'
- split: 2023_09_13T00_01_07.493301
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T00-01-07.493301.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T00-01-07.493301.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T05_37_42.901960
path:
- '**/details_harness|winogrande|5_2023-10-16T05-37-42.901960.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T05-37-42.901960.parquet'
- config_name: results
data_files:
- split: 2023_08_28T23_15_40.572782
path:
- results_2023-08-28T23:15:40.572782.parquet
- split: 2023_09_13T00_01_07.493301
path:
- results_2023-09-13T00-01-07.493301.parquet
- split: 2023_10_16T05_37_42.901960
path:
- results_2023-10-16T05-37-42.901960.parquet
- split: latest
path:
- results_2023-10-16T05-37-42.901960.parquet
---
# Dataset Card for Evaluation run of NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
- **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 [NobodyExistsOnTheInternet/PuffedConvo13bLoraE4](https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T05:37:42.901960](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4/blob/main/results_2023-10-16T05-37-42.901960.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": 0.02506291946308725,
"em_stderr": 0.0016008246934367607,
"f1": 0.08258703859060393,
"f1_stderr": 0.0020041497370696586,
"acc": 0.4200579473220307,
"acc_stderr": 0.009967774108676528
},
"harness|drop|3": {
"em": 0.02506291946308725,
"em_stderr": 0.0016008246934367607,
"f1": 0.08258703859060393,
"f1_stderr": 0.0020041497370696586
},
"harness|gsm8k|5": {
"acc": 0.08794541319181198,
"acc_stderr": 0.007801162197487711
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### 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] |
crumb/flan-ul2-tinystories | ---
license: mit
language:
- en
---
Around a quarter of a million examples generated from Flan-UL2 (20b) with the prompt "Write a short story using the vocabulary of a first-grader." to be used in an experimental curriculum learning setting. I had to checkpoint every 1024 examples to mitigate the program slowing down due to memory usage. This was run in bf16 on an RTXA6000 with the following settings:
```
top_k = random between (40, 128)
temperature = random between (0.6, 0.95)
max_length = 128
batch_size = 32
```
I wanted a less uniform boring set with the same exact patterns so I randomly modulate the temperature and top_k values to get a good mix. This cost ~$6 usd to create on runpod. |
ShixuanAn/RDD_2020 | ---
license: cc-by-nc-3.0
task_categories:
- image-classification
language:
- en
pretty_name: >-
RDD2020: An Image Dataset for Smartphone-based Road Damage Detection and
Classification
size_categories:
- 100B<n<1T
---
# Dataset Card for RDD_2020
The RDD2020 dataset is a comprehensive collection of 26,336 road images from India, Japan, and the Czech Republic, annotated with over 31,000 instances of road damages. This dataset is designed to support the development and evaluation of machine learning models for automatic road damage detection, offering a valuable resource for municipalities and road agencies for efficient road condition monitoring.
## Dataset Details
### Dataset Description
- **Source:** [Mendeley Data](https://data.mendeley.com/datasets/5ty2wb6gvg/1) - DOI: 10.17632/5ty2wb6gvg.1
- **Size:** 1.13 GB
- **Format:** Images (JPEG) and Annotations (XML in PASCAL VOC format)
- **Resolution:**
- India: 720 × 720 pixels
- Japan and Czech: 600 × 600 pixels
- **Categories:** Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), Potholes (D40)
- **License:** https://creativecommons.org/licenses/by/4.0/
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Homepage** https://data.mendeley.com/datasets/5ty2wb6gvg/1
- **Data article:** https://doi.org/10.1016/j.dib.2021.107133
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
RDD2020 dataset can be directly used for developing and benchmarking machine learning models aimed at automatic detection and classification of road damages. This includes developing new deep learning architectures or modifying existing ones to improve detection accuracy across different types of road damages
## Dataset Structure
### Data Instance
The data will follow the structure below:
```
{
"image_id": "Czech_000248",
"country": "Czech",
"type": "train",
"image": "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x600>",
"image_path": "train/Czech/images/Czech_000248.jpg",
"crack_type": ["D20", "D20"],
"crack_coordinates": {
"x_min": [188, 3],
"x_max": [309, 171],
"y_min": [463, 438],
"y_max": [509, 519]
}
}
```
### Data Fields
- "image_id"[string]: ID of the image, created by combining the country plus a sequential number.
- "country"[string]: The country where the photo was taken.
- "type"[string]: The dataset category the image belongs to, such as 'train', 'test1', or 'test2'. "image"[integer]: The image data converted into PIL format.
- "crack_type"[string]: Types of cracks detected in the image.
- "crack_coordinates"[integer]: Contains crack coordinates as integers.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The RDD2020 dataset was curated with the objective of facilitating the development, testing, and benchmarking of machine learning models for road damage detection, catering specifically to the needs of municipalities and road agencies. A significant aspect of the dataset's curation process was the conversion of images into the Python Imaging Library (PIL) format and the meticulous parsing of XML annotations to ensure a seamless integration between the image data and the associated labels. This conversion process was driven by the need to simplify the handling of image data for machine learning applications, as the PIL format is widely supported by data processing and model training frameworks commonly used in the field.
Additionally, the parsing of XML files to extract detailed annotations about the type and coordinates of road damages allows for precise labeling of the data. This approach ensures that each image is directly associated with its corresponding damage type and location. The dataset's diversity, with images sourced from three different countries, aims to enable the creation of robust models that are effective across various environmental conditions and road infrastructures, thereby broadening the applicability and relevance of the trained models.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
Road images (.jpg) were collected using a vehicle-mounted smartphone, moving at an average speed of about 40Km/h. XML files were created using the LabelImg tool to annotate the road damages present in the images.
#### Who are the source data producers?
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Hiroshi Omata, Takehiro Kashiyama, Toshikazu Seto, Alexander Mraz,
Yoshihide Sekimot
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
### Annotations
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
Each image in the dataset comes with corresponding XML files containing annotations in PASCAL VOC format. These annotations describe the location and type of road damages present in the images, categorized into four main types: Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks (D20), and Potholes (D40).
### Social Impact
The structuring of the RDD2020 dataset into a more accessible and usable format is aimed at having a focused and measurable impact on the management of road infrastructure. The transformation of raw images and XML annotations into a coherent dataset with clearly defined attributes such as photo_id, country, type, pics_array, image_resolution, crack_type, and crack_coordinates creates a powerful tool for municipalities and road agencies. With this structured dataset, these entities can deploy machine learning models to accurately identify and classify road damages like cracks and potholes, which are critical for the maintenance and safety of roadways.
In conclusion, the transformation of this raw data into a structured and accessible format not only catalyzes the progress of automated road damage assessment but also potentially engages the public sector in adopting AI-driven solutions for public safety and infrastructure management.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The dataset primarily includes images from three countries (India, Japan, and the Czech Republic), which may not fully represent road conditions worldwide. Users should be cautious when generalizing models trained on this dataset to other regions.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
carexl8/twitter_he_ru | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: created_at
dtype: timestamp[ns, tz=UTC]
- name: tokens
sequence: string
- name: language tags
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 603593
num_examples: 853
download_size: 0
dataset_size: 603593
---
# Dataset Card for "twitter_he_ru"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChavyvAkvar/chai-reward-kto-trainer-v2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 21896726
num_examples: 10735
download_size: 12273033
dataset_size: 21896726
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kuanhuggingface/GPTspeech_encodec_v2_subset | ---
dataset_info:
features:
- name: file_id
dtype: string
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
sequence: int64
- name: src_encodec_2
sequence: int64
- name: src_encodec_3
sequence: int64
- name: src_encodec_4
sequence: int64
- name: src_encodec_5
sequence: int64
- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
sequence: int64
- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
sequence: int64
- name: tgt_encodec_3
sequence: int64
- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
sequence: int64
splits:
- name: train
num_bytes: 11776844857
num_examples: 328242
- name: validation
num_bytes: 177612370
num_examples: 5029
- name: test
num_bytes: 166783731
num_examples: 4826
download_size: 968974622
dataset_size: 12121240958
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
dariolopez/Llama-2-oasst1-es-test-format | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4680420
num_examples: 3909
download_size: 2537064
dataset_size: 4680420
---
# Dataset Card for "Llama-2-oasst1-es-test-format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
data354/Koumankan_mt_dyu_fr | ---
dataset_info:
features:
- name: ID
dtype: string
- name: translation
dtype:
translation:
languages:
- dyu
- fr
splits:
- name: train
num_bytes: 727371
num_examples: 8065
- name: validation
num_bytes: 135803
num_examples: 1471
- name: test
num_bytes: 80949
num_examples: 1393
download_size: 687792
dataset_size: 944123
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- translation
language:
- bm
- fr
pretty_name: Koumankan4dyula
size_categories:
- 10K<n<100K
---
# Koumankan4Dyula: Parallel Dyula - French Dataset for Machine Learning
## Overview
The Koumankan4Dyula corpus consists of 10929 pairs of Dioula-French sentences.
This corpus is part of the Koumankan project, which proposes a scalable and cost-effective method for extending the CommonVoice dataset to the Dyula language and other African languages.
## Data Splits
| | | |
|:-----:|:---:|------:|
| Train | 73% | 8065 |
| Valid | 14% | 1471 |
| Test | 13% | 1393 |
||
### Maintenance
* This dataset is supposed to be actively maintained.
### Benchmarks:
- `Coming soon`
## License
- `CC-BY-SA-4.0`
## Version
- `1.0.0`
## Acknowledgements
This dataset collection efforts have been supported by International Development Research Centre (IDRC) and Swedish International Development Cooperation Agency (SIDA),
managed by African Center for Technology Studies (ACTS) in collaboration with the Université Virtuelle de Côte d'Ivoire (UVCI) through the programme Artificial Intelligence for Development (AI4D) Africa.
## Contacts
- `ismael21.kone@uvci.edu.ci`
- `info@data354.com` |
tyzhu/find_first_sent_train_400_eval_40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1072675
num_examples: 840
- name: validation
num_bytes: 41217
num_examples: 40
download_size: 0
dataset_size: 1113892
---
# Dataset Card for "find_first_sent_train_400_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_97_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 8390574
num_examples: 6979
download_size: 2842335
dataset_size: 8390574
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_97_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-medical_genetics-dev | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 2202
num_examples: 5
download_size: 6027
dataset_size: 2202
---
# Dataset Card for "mmlu-medical_genetics-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
existence-master/bloomify-classification-1.2k | ---
license: gpl-3.0
---
|
andersonbcdefg/redteaming_eval_pairwise | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: explanation
dtype: string
- name: preferred
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 79844
num_examples: 105
download_size: 0
dataset_size: 79844
---
# Dataset Card for "redteaming_eval_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Epimachok/tnved_train | ---
license: apache-2.0
---
|
cjvt/cc_gigafida | ---
annotations_creators:
- no-annotation
language:
- sl
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Written corpus ccGigafida 1.0
size_categories:
- 10K<n<100K
- 100M<n<1B
source_datasets: []
tags:
- gigafida
- gigafida2
- kres
- cckres
- reference corpus
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
- language-modeling
---
# Dataset Card for ccGigafida
This repository by default loads the publicly available dataset ccGigafida, which contains a small subset of the Gigafida/Gigafida2 corpus.
The full datasets are private due to copyright. **If you happen to have access to the full datasets, the script will also work with those.**
Instead of
```
datasets.load_dataset("cjvt/cc_gigafida")
```
please use
```
datasets.load_dataset("cjvt/cc_gigafida", "private", data_dir="<directory-containing-gigafida(2)-TEI-files>")
```
**IMPORTANT:** The script will process all `.xml` files in the provided directory and its subdirectories - make sure there are no schema or metadata files in there!
### Dataset Summary
ccGigafida is a reference corpus of Slovene texts. It is a publicly available subsample of an even larger reference corpus, Gigafida (and its successor Gigafida 2).
The Gigafida corpus is an extensive collection of Slovene text of various genres, from daily newspapers, magazines, all kinds of books (fiction, non-fiction, textbooks),
web pages, transcriptions of parliamentary debates and similar.
### Supported Tasks and Leaderboards
Language modeling.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
The data is loaded at document-level, i.e. one instance is one document.
```
{
'id_doc': 'F0000123',
'doc_title': 'Novi tednik NT&RC',
'authors': ['neznani novinar'],
'publish_date': '1998-03-27',
'publisher': 'Novi tednik',
'genres': ['tisk/periodično/časopis'],
'doc_tokenized': [
[
['Po', 'nekajletnem', 'počitku', 'pa', 'se', 'vračajo', 'tudi', 'kralji', 'dark', 'rock', 'godbe', 'JESUS', 'AND', 'THE', 'MARY', 'CHAIN', '.'],
['Brata', 'Reid', 'bosta', 'svojo', 'najnovejšo', 'kreacijo', '»', 'Cracking', 'Up', '«', 'objavila', 'v', 'ponedeljek', 'pri', 'trenutno', 'najuspešnejši', 'neodvisni', 'založbi', 'Creation', '(', 'vodi', 'jo', 'njun', 'nekdanji', 'menager', 'Alan', 'McGee', ',', 'zanjo', 'pa', 'poleg', 'Oasis', 'snema', 'še', 'cel', 'kup', 'popularnih', 'brit', '-', 'popovcev', ')', ',', 'tej', 'pa', 'bo', 'kmalu', 'sledil', 'tudi', 'album', '»', 'Munki', '«', '.']
],
[
['Kultni', 'ameriški', 'tehno', 'freak', 'PLASTIKMAN', 'že', 'vrsto', 'let', 'velja', 'za', 'enega', 'izmed', 'najbolj', 'inovativnih', 'in', 'produktivnih', 'ustvarjalcev', 'sodobne', 'elektronske', 'glasbe', '.'],
['Za', 'založbo', 'Nova', 'Mute', 'je', 'v', 'preteklih', 'nekaj', 'letih', 'posnel', 'cel', 'kup', 'izvrstnih', 'underground', 'dance', 'glasbenih', 'izdelkov', ',', 'pred', 'nedavnim', 'pa', 'je', 'ljubitelje', 'tovrstne', 'godbe', 'presenetil', 'z', 'ambientalnimi', 'odisejadami', ',', 'zbranimi', 'na', 'LP-ju', '»', 'Refused', '«', ',', 'ki', 'ga', 'lahko', 'od', 'prejšnjega', 'ponedeljka', 'kupite', 'tudi', 'v', 'bolje', 'založenih', 'trgovinah', 'z', 'nosilci', 'zvoka', 'na', 'sončni', 'strani', 'Alp', '.']
],
[
['STANE', 'ŠPEGEL']
]
],
'doc_lemmas': [...],
'doc_msds': [...],
'doc_string': [
[
'Po nekajletnem počitku pa se vračajo tudi kralji dark rock godbe JESUS AND THE MARY CHAIN. ',
'Brata Reid bosta svojo najnovejšo kreacijo »Cracking Up« objavila v ponedeljek pri trenutno najuspešnejši neodvisni založbi Creation (vodi jo njun nekdanji menager Alan McGee, zanjo pa poleg Oasis snema še cel kup popularnih brit-popovcev), tej pa bo kmalu sledil tudi album »Munki«.'
],
[
'Kultni ameriški tehno freak PLASTIKMAN že vrsto let velja za enega izmed najbolj inovativnih in produktivnih ustvarjalcev sodobne elektronske glasbe. ',
'Za založbo Nova Mute je v preteklih nekaj letih posnel cel kup izvrstnih underground dance glasbenih izdelkov, pred nedavnim pa je ljubitelje tovrstne godbe presenetil z ambientalnimi odisejadami, zbranimi na LP-ju »Refused«, ki ga lahko od prejšnjega ponedeljka kupite tudi v bolje založenih trgovinah z nosilci zvoka na sončni strani Alp.'
],
[
'STANE ŠPEGEL'
]
],
'id_sents': [['F0000123.000005.0', 'F0000123.000005.1'], ['F0000123.000013.0', 'F0000123.000013.1'], ['F0000123.000020.0']]
}
```
### Data Fields
- `id_doc`: the document ID (string);
- `doc_title`: the document title (string);
- `authors`: author(s) of the document (list of string): "neznani novinar" (sl) = ("unknown/unspecified journalist");
- `publish_date`: publish date (string);
- `publisher`: publisher, e.g., the name of a news agency (string);
- `genres`: genre(s) of the document (list of string) - possible genres: `['tisk', 'tisk/knjižno', 'tisk/knjižno/leposlovno', 'tisk/knjižno/strokovno', 'tisk/periodično', 'tisk/periodično/časopis', 'tisk/periodično/revija', 'tisk/drugo', 'internet']`;
- `doc_tokenized`: tokenized document - the top level lists represent paragraphs, the lists in the level deeper represent sentences, and each sentence contains tokens;
- `doc_lemmas`: lemmatized document - same structure as `doc_tokenized`;
- `doc_msds`: MSD tags of the document - same structure as `doc_tokenized` ([tagset](http://nl.ijs.si/ME/V6/msd/html/msd-sl.html));
- `doc_string`: same as `doc_tokenized` but with properly placed spaces in sentences;
- `id_sents`: IDs of sentences contained inside paragraphs of the document.
## Dataset Creation
Gigafida consists of texts which were published between 1990 and 2011. The texts come from printed sources and from the web.
Printed part contains fiction, non-fiction and textbooks, and periodicals such as daily newspapers and magazines.
Texts originating from the web were published on news portals, pages of big Slovene companies and more important governmental,
educational, research, cultural and similar institutions.
For more information, please check http://eng.slovenscina.eu/korpusi/gigafida.
## Additional Information
### Dataset Curators
Nataša Logar; et al. (please see http://hdl.handle.net/11356/1035 for the full list)
### Licensing Information
CC BY-NC-SA 4.0.
### Citation Information
```
@misc{ccGigafida,
title = {Written corpus {ccGigafida} 1.0},
author = {Logar, Nata{\v s}a and Erjavec, Toma{\v z} and Krek, Simon and Gr{\v c}ar, Miha and Holozan, Peter},
url = {http://hdl.handle.net/11356/1035},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)},
issn = {2820-4042},
year = {2013}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
tyzhu/wiki_find_passage_train400_eval40_num | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 624884
num_examples: 840
- name: validation
num_bytes: 33332
num_examples: 40
download_size: 0
dataset_size: 658216
---
# Dataset Card for "wiki_find_passage_train400_eval40_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
neulab/odex | ---
license: cc-by-sa-4.0
task_categories:
- text2text-generation
- text-generation
language:
- en
- es
- ja
- ru
size_categories:
- n<1K
---
__ODEX__ is an Open-Domain EXecution-based NL-to-Code generation data benchmark.
It contains 945 samples with a total of 1,707 human-written test cases, covering intents in four different natural languages -- 439 in English, 90 in Spanish, 164 in Japanese, and 252 in Russian.
You can load the dataset by specifying a subset from *en, es, ja, ru* (by default the english subset *en* is loaded):
```python
from datasets import load_dataset
ds = load_dataset("neulab/odex", "ja", split="test")
```
If you find our dataset useful, please cite the paper
```
@article{wang2022execution,
title={Execution-Based Evaluation for Open-Domain Code Generation},
author={Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig},
journal={arXiv preprint arXiv:2212.10481},
year={2022}
}
``` |
male-2/training_v0.0.6-public | ---
dataset_info:
features:
- name: id
dtype: string
- name: type
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: emotion
struct:
- name: joyful
dtype: bool
- name: sad
dtype: bool
- name: angry
dtype: bool
- name: example
dtype: string
splits:
- name: train
num_bytes: 1054
num_examples: 1
download_size: 10130
dataset_size: 1054
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "training_v0.0.6-public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
san5167/test1 | ---
license: openrail
name: test1
---
|
felipeoes/br_federal_legislation | ---
dataset_info:
features:
- name: year
dtype: int64
- name: situation
dtype: string
- name: type
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: document_url
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 567673785
num_examples: 109830
download_size: 217062603
dataset_size: 567673785
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joey234/mmlu-conceptual_physics-rule-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 86372
num_examples: 235
download_size: 48276
dataset_size: 86372
---
# Dataset Card for "mmlu-conceptual_physics-rule-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/leaf_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of leaf/ブルー (Pokémon)
This is the dataset of leaf/ブルー (Pokémon), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, hat, breasts, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 411.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leaf_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 270.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leaf_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 992 | 499.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leaf_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 377.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leaf_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 992 | 647.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leaf_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/leaf_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, closed_mouth, looking_at_viewer, sleeveless_shirt, solo, upper_body, white_headwear, blue_shirt, bag, black_wristband, eyelashes, simple_background, holding_poke_ball, poke_ball_(basic), white_background |
| 1 | 6 |  |  |  |  |  | 1girl, blue_shirt, looking_at_viewer, red_skirt, sleeveless_shirt, smile, solo, white_headwear, closed_mouth, pleated_skirt, shoulder_bag, simple_background, black_wristband, cowboy_shot, hand_on_hip, miniskirt, white_background |
| 2 | 7 |  |  |  |  |  | 1girl, blue_shirt, red_skirt, shoes, sleeveless_shirt, white_headwear, closed_mouth, looking_at_viewer, pleated_skirt, smile, solo, white_footwear, black_wristband, full_body, hair_flaps, loose_socks, simple_background, white_background, blue_socks, eyelashes, holding_poke_ball, poke_ball_(basic), sitting, shoulder_bag, standing, yellow_bag |
| 3 | 17 |  |  |  |  |  | 1girl, blush, hetero, white_headwear, nipples, shirt_lift, penis, sex, pokemon_(creature), red_skirt, vaginal, loose_socks, open_mouth, pokephilia, mosaic_censoring, pussy, 1boy, spread_legs, bestiality, no_bra, outdoors, panties, sleeveless_shirt, blue_shirt, shiny_skin, eyelashes, navel, smile, sweat, tongue |
| 4 | 5 |  |  |  |  |  | 1girl, blush, medium_breasts, nipples, 1boy, hetero, topless, vaginal, bestiality, panties_aside, penis, pokemon_(creature), pokephilia, sex, white_panties, closed_eyes, cum, english_text, mosaic_censoring, open_mouth, pussy, socks, solo_focus |
| 5 | 10 |  |  |  |  |  | 1girl, hetero, penis, sex, 1boy, vaginal, cum_in_pussy, nipples, blush, porkpie_hat, medium_breasts, open_mouth, solo_focus, spread_legs, shirt_lift, skirt, uncensored |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, large_breasts, solo_focus, blush, nipples, penis, cum_on_breasts, facial, paizuri, porkpie_hat, smile, wristband, censored, pov |
| 7 | 12 |  |  |  |  |  | 1girl, camouflage_headwear, long_sleeves, official_alternate_costume, open_mouth, tongue, :d, buttons, eyelashes, green_coat, green_headwear, shirt, open_coat, bike_shorts, pokemon_(creature), dress, green_jacket, shoes, upper_teeth_only |
| 8 | 8 |  |  |  |  |  | 1girl, bike_shorts, buttons, camouflage_headwear, eyelashes, green_jacket, official_alternate_costume, closed_mouth, green_coat, long_sleeves, open_coat, smile, green_headwear, hair_flaps, looking_at_viewer, pink_shirt, solo, open_jacket, pink_dress, pokemon_(creature) |
| 9 | 5 |  |  |  |  |  | 1girl, blush, hetero, nipples, solo_focus, fellatio, gangbang, medium_breasts, multiple_penises, shirt_lift, vaginal, 3boys, cum_in_mouth, miniskirt, mmf_threesome, navel, porkpie_hat, testicles, uncensored, white_headwear, 2boys, cum_in_pussy, cum_on_breasts, double_handjob, rape, red_skirt, tears |
| 10 | 9 |  |  |  |  |  | 1girl, nipples, nude, solo, navel, pussy, blush, female_pubic_hair, looking_at_viewer, smile, wristband, small_breasts, white_headwear |
| 11 | 5 |  |  |  |  |  | 1girl, bikini, day, outdoors, blue_sky, cloud, looking_at_viewer, navel, bangs, cleavage, collarbone, open_mouth, pokemon_(creature), solo, :d, blush, cowboy_shot, large_breasts, standing, thigh_gap, water |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | looking_at_viewer | sleeveless_shirt | solo | upper_body | white_headwear | blue_shirt | bag | black_wristband | eyelashes | simple_background | holding_poke_ball | poke_ball_(basic) | white_background | red_skirt | smile | pleated_skirt | shoulder_bag | cowboy_shot | hand_on_hip | miniskirt | shoes | white_footwear | full_body | hair_flaps | loose_socks | blue_socks | sitting | standing | yellow_bag | blush | hetero | nipples | shirt_lift | penis | sex | pokemon_(creature) | vaginal | open_mouth | pokephilia | mosaic_censoring | pussy | 1boy | spread_legs | bestiality | no_bra | outdoors | panties | shiny_skin | navel | sweat | tongue | medium_breasts | topless | panties_aside | white_panties | closed_eyes | cum | english_text | socks | solo_focus | cum_in_pussy | porkpie_hat | skirt | uncensored | large_breasts | cum_on_breasts | facial | paizuri | wristband | censored | pov | camouflage_headwear | long_sleeves | official_alternate_costume | :d | buttons | green_coat | green_headwear | shirt | open_coat | bike_shorts | dress | green_jacket | upper_teeth_only | pink_shirt | open_jacket | pink_dress | fellatio | gangbang | multiple_penises | 3boys | cum_in_mouth | mmf_threesome | testicles | 2boys | double_handjob | rape | tears | nude | female_pubic_hair | small_breasts | bikini | day | blue_sky | cloud | bangs | cleavage | collarbone | thigh_gap | water |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:--------------------|:-------------------|:-------|:-------------|:-----------------|:-------------|:------|:------------------|:------------|:--------------------|:--------------------|:--------------------|:-------------------|:------------|:--------|:----------------|:---------------|:--------------|:--------------|:------------|:--------|:-----------------|:------------|:-------------|:--------------|:-------------|:----------|:-----------|:-------------|:--------|:---------|:----------|:-------------|:--------|:------|:---------------------|:----------|:-------------|:-------------|:-------------------|:--------|:-------|:--------------|:-------------|:---------|:-----------|:----------|:-------------|:--------|:--------|:---------|:-----------------|:----------|:----------------|:----------------|:--------------|:------|:---------------|:--------|:-------------|:---------------|:--------------|:--------|:-------------|:----------------|:-----------------|:---------|:----------|:------------|:-----------|:------|:----------------------|:---------------|:-----------------------------|:-----|:----------|:-------------|:-----------------|:--------|:------------|:--------------|:--------|:---------------|:-------------------|:-------------|:--------------|:-------------|:-----------|:-----------|:-------------------|:--------|:---------------|:----------------|:------------|:--------|:-----------------|:-------|:--------|:-------|:--------------------|:----------------|:---------|:------|:-----------|:--------|:--------|:-----------|:-------------|:------------|:--------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | | X | X | | X | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | X | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 17 |  |  |  |  |  | X | | | X | | | X | X | | | X | | | | | X | X | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | | X | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | X | X | | | | X | X | | | | | | | | | X | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 8 |  |  |  |  |  | X | X | X | | X | | | | | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | X | X | X | | X | X | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | | | | X | | | | | | | | | X | | | | | | X | | | | | | | | | | X | X | X | X | | | | X | | | | | | | | | | | | X | | | X | | | | | | | | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 10 | 9 |  |  |  |  |  | X | | X | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | X | | X | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | X | | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | X | | X | | | | | | X | | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
Neloy262/rust_instruction_dataset | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 10732767
num_examples: 10000
download_size: 5302996
dataset_size: 10732767
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mteb/biorxiv-clustering-s2s | ---
language:
- en
--- |
AdapterOcean/Open_Platypus_standardized_cluster_3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 49936952
num_examples: 5226
download_size: 0
dataset_size: 49936952
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kaue123456/BussundaShrek | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_wnli_preposition_chopping | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: train
num_bytes: 530
num_examples: 4
download_size: 2645
dataset_size: 530
---
# Dataset Card for "MULTI_VALUE_wnli_preposition_chopping"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PL-MTEB/cdscr-sts | ---
license: cc-by-nc-sa-4.0
---
|
open-llm-leaderboard/details_Menouar__gemma-2b-chat-ultra | ---
pretty_name: Evaluation run of Menouar/gemma-2b-chat-ultra
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Menouar/gemma-2b-chat-ultra](https://huggingface.co/Menouar/gemma-2b-chat-ultra)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Menouar__gemma-2b-chat-ultra\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-22T02:35:53.241150](https://huggingface.co/datasets/open-llm-leaderboard/details_Menouar__gemma-2b-chat-ultra/blob/main/results_2024-03-22T02-35-53.241150.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\": 0.3957913300987599,\n\
\ \"acc_stderr\": 0.03417582131557158,\n \"acc_norm\": 0.3986889029486514,\n\
\ \"acc_norm_stderr\": 0.03492975956849049,\n \"mc1\": 0.2594859241126071,\n\
\ \"mc1_stderr\": 0.015345409485557982,\n \"mc2\": 0.3906683350216857,\n\
\ \"mc2_stderr\": 0.014343622966056763\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4684300341296928,\n \"acc_stderr\": 0.014582236460866973,\n\
\ \"acc_norm\": 0.48293515358361777,\n \"acc_norm_stderr\": 0.014602878388536593\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.523401712806214,\n\
\ \"acc_stderr\": 0.0049843132057914375,\n \"acc_norm\": 0.7017526389165505,\n\
\ \"acc_norm_stderr\": 0.0045655368086325535\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n\
\ \"acc_stderr\": 0.042561937679014075,\n \"acc_norm\": 0.4148148148148148,\n\
\ \"acc_norm_stderr\": 0.042561937679014075\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3684210526315789,\n \"acc_stderr\": 0.03925523381052932,\n\
\ \"acc_norm\": 0.3684210526315789,\n \"acc_norm_stderr\": 0.03925523381052932\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.4716981132075472,\n \"acc_stderr\": 0.0307235352490061,\n\
\ \"acc_norm\": 0.4716981132075472,\n \"acc_norm_stderr\": 0.0307235352490061\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"\
acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\
: 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.45664739884393063,\n\
\ \"acc_stderr\": 0.03798106566014499,\n \"acc_norm\": 0.45664739884393063,\n\
\ \"acc_norm_stderr\": 0.03798106566014499\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.13725490196078433,\n \"acc_stderr\": 0.034240846698915195,\n\
\ \"acc_norm\": 0.13725490196078433,\n \"acc_norm_stderr\": 0.034240846698915195\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.39574468085106385,\n \"acc_stderr\": 0.031967586978353627,\n\
\ \"acc_norm\": 0.39574468085106385,\n \"acc_norm_stderr\": 0.031967586978353627\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\
\ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\
\ \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\
\ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25132275132275134,\n \"acc_stderr\": 0.022340482339643895,\n \"\
acc_norm\": 0.25132275132275134,\n \"acc_norm_stderr\": 0.022340482339643895\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\
\ \"acc_stderr\": 0.04240799327574924,\n \"acc_norm\": 0.3412698412698413,\n\
\ \"acc_norm_stderr\": 0.04240799327574924\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4161290322580645,\n\
\ \"acc_stderr\": 0.028040981380761554,\n \"acc_norm\": 0.4161290322580645,\n\
\ \"acc_norm_stderr\": 0.028040981380761554\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3251231527093596,\n \"acc_stderr\": 0.032957975663112704,\n\
\ \"acc_norm\": 0.3251231527093596,\n \"acc_norm_stderr\": 0.032957975663112704\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\"\
: 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.41818181818181815,\n \"acc_stderr\": 0.03851716319398394,\n\
\ \"acc_norm\": 0.41818181818181815,\n \"acc_norm_stderr\": 0.03851716319398394\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.494949494949495,\n \"acc_stderr\": 0.035621707606254015,\n \"\
acc_norm\": 0.494949494949495,\n \"acc_norm_stderr\": 0.035621707606254015\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5181347150259067,\n \"acc_stderr\": 0.036060650018329185,\n\
\ \"acc_norm\": 0.5181347150259067,\n \"acc_norm_stderr\": 0.036060650018329185\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.3769230769230769,\n \"acc_stderr\": 0.024570975364225995,\n\
\ \"acc_norm\": 0.3769230769230769,\n \"acc_norm_stderr\": 0.024570975364225995\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275777,\n \
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275777\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3907563025210084,\n \"acc_stderr\": 0.03169380235712997,\n \
\ \"acc_norm\": 0.3907563025210084,\n \"acc_norm_stderr\": 0.03169380235712997\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\
acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5321100917431193,\n \"acc_stderr\": 0.021393071222680797,\n \"\
acc_norm\": 0.5321100917431193,\n \"acc_norm_stderr\": 0.021393071222680797\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.28703703703703703,\n \"acc_stderr\": 0.03085199299325701,\n \"\
acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.03085199299325701\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.47058823529411764,\n \"acc_stderr\": 0.03503235296367994,\n \"\
acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.03503235296367994\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.4388185654008439,\n \"acc_stderr\": 0.032302649315470375,\n \
\ \"acc_norm\": 0.4388185654008439,\n \"acc_norm_stderr\": 0.032302649315470375\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.42152466367713004,\n\
\ \"acc_stderr\": 0.033141902221106564,\n \"acc_norm\": 0.42152466367713004,\n\
\ \"acc_norm_stderr\": 0.033141902221106564\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.46564885496183206,\n \"acc_stderr\": 0.04374928560599738,\n\
\ \"acc_norm\": 0.46564885496183206,\n \"acc_norm_stderr\": 0.04374928560599738\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.4793388429752066,\n \"acc_stderr\": 0.04560456086387235,\n \"\
acc_norm\": 0.4793388429752066,\n \"acc_norm_stderr\": 0.04560456086387235\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3425925925925926,\n\
\ \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.3425925925925926,\n\
\ \"acc_norm_stderr\": 0.045879047413018105\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3619631901840491,\n \"acc_stderr\": 0.037757007291414416,\n\
\ \"acc_norm\": 0.3619631901840491,\n \"acc_norm_stderr\": 0.037757007291414416\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.42718446601941745,\n \"acc_stderr\": 0.04897957737781169,\n\
\ \"acc_norm\": 0.42718446601941745,\n \"acc_norm_stderr\": 0.04897957737781169\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5854700854700855,\n\
\ \"acc_stderr\": 0.03227396567623779,\n \"acc_norm\": 0.5854700854700855,\n\
\ \"acc_norm_stderr\": 0.03227396567623779\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.05021167315686781,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.05021167315686781\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5236270753512133,\n\
\ \"acc_stderr\": 0.017859989765176453,\n \"acc_norm\": 0.5236270753512133,\n\
\ \"acc_norm_stderr\": 0.017859989765176453\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.3583815028901734,\n \"acc_stderr\": 0.025816756791584204,\n\
\ \"acc_norm\": 0.3583815028901734,\n \"acc_norm_stderr\": 0.025816756791584204\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25027932960893856,\n\
\ \"acc_stderr\": 0.014487500852850417,\n \"acc_norm\": 0.25027932960893856,\n\
\ \"acc_norm_stderr\": 0.014487500852850417\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.028541722692618877,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.028541722692618877\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3987138263665595,\n\
\ \"acc_stderr\": 0.0278093225857745,\n \"acc_norm\": 0.3987138263665595,\n\
\ \"acc_norm_stderr\": 0.0278093225857745\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.42901234567901236,\n \"acc_stderr\": 0.027538925613470863,\n\
\ \"acc_norm\": 0.42901234567901236,\n \"acc_norm_stderr\": 0.027538925613470863\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.28368794326241137,\n \"acc_stderr\": 0.02689170942834396,\n \
\ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.02689170942834396\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3318122555410691,\n\
\ \"acc_stderr\": 0.012026088259897634,\n \"acc_norm\": 0.3318122555410691,\n\
\ \"acc_norm_stderr\": 0.012026088259897634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.33455882352941174,\n \"acc_stderr\": 0.028661996202335303,\n\
\ \"acc_norm\": 0.33455882352941174,\n \"acc_norm_stderr\": 0.028661996202335303\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.369281045751634,\n \"acc_stderr\": 0.019524316744866342,\n \
\ \"acc_norm\": 0.369281045751634,\n \"acc_norm_stderr\": 0.019524316744866342\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.42727272727272725,\n\
\ \"acc_stderr\": 0.04738198703545483,\n \"acc_norm\": 0.42727272727272725,\n\
\ \"acc_norm_stderr\": 0.04738198703545483\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.43673469387755104,\n \"acc_stderr\": 0.03175195237583323,\n\
\ \"acc_norm\": 0.43673469387755104,\n \"acc_norm_stderr\": 0.03175195237583323\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.40298507462686567,\n\
\ \"acc_stderr\": 0.03468343295111126,\n \"acc_norm\": 0.40298507462686567,\n\
\ \"acc_norm_stderr\": 0.03468343295111126\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079023,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079023\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.52046783625731,\n \"acc_stderr\": 0.038316105328219316,\n\
\ \"acc_norm\": 0.52046783625731,\n \"acc_norm_stderr\": 0.038316105328219316\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2594859241126071,\n\
\ \"mc1_stderr\": 0.015345409485557982,\n \"mc2\": 0.3906683350216857,\n\
\ \"mc2_stderr\": 0.014343622966056763\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6535122336227308,\n \"acc_stderr\": 0.013373773411685653\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1599696739954511,\n \
\ \"acc_stderr\": 0.010097377827752538\n }\n}\n```"
repo_url: https://huggingface.co/Menouar/gemma-2b-chat-ultra
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|arc:challenge|25_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|gsm8k|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hellaswag|10_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T02-35-53.241150.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T02-35-53.241150.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- '**/details_harness|winogrande|5_2024-03-22T02-35-53.241150.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-22T02-35-53.241150.parquet'
- config_name: results
data_files:
- split: 2024_03_22T02_35_53.241150
path:
- results_2024-03-22T02-35-53.241150.parquet
- split: latest
path:
- results_2024-03-22T02-35-53.241150.parquet
---
# Dataset Card for Evaluation run of Menouar/gemma-2b-chat-ultra
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Menouar/gemma-2b-chat-ultra](https://huggingface.co/Menouar/gemma-2b-chat-ultra) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Menouar__gemma-2b-chat-ultra",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-22T02:35:53.241150](https://huggingface.co/datasets/open-llm-leaderboard/details_Menouar__gemma-2b-chat-ultra/blob/main/results_2024-03-22T02-35-53.241150.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": 0.3957913300987599,
"acc_stderr": 0.03417582131557158,
"acc_norm": 0.3986889029486514,
"acc_norm_stderr": 0.03492975956849049,
"mc1": 0.2594859241126071,
"mc1_stderr": 0.015345409485557982,
"mc2": 0.3906683350216857,
"mc2_stderr": 0.014343622966056763
},
"harness|arc:challenge|25": {
"acc": 0.4684300341296928,
"acc_stderr": 0.014582236460866973,
"acc_norm": 0.48293515358361777,
"acc_norm_stderr": 0.014602878388536593
},
"harness|hellaswag|10": {
"acc": 0.523401712806214,
"acc_stderr": 0.0049843132057914375,
"acc_norm": 0.7017526389165505,
"acc_norm_stderr": 0.0045655368086325535
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4148148148148148,
"acc_stderr": 0.042561937679014075,
"acc_norm": 0.4148148148148148,
"acc_norm_stderr": 0.042561937679014075
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.03925523381052932,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.03925523381052932
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4716981132075472,
"acc_stderr": 0.0307235352490061,
"acc_norm": 0.4716981132075472,
"acc_norm_stderr": 0.0307235352490061
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
Biomedical-TeMU/ProfNER_corpus_NER | ---
license: cc-by-4.0
---
## Description
**Gold standard annotations for profession detection in Spanish COVID-19 tweets**
The entire corpus contains 10,000 annotated tweets. It has been split into training, validation, and test (60-20-20). The current version contains the training and development set of the shared task with Gold Standard annotations. In addition, it contains the unannotated test, and background sets will be released.
For Named Entity Recognition, profession detection, annotations are distributed in 2 formats: Brat standoff and TSV. See the Brat webpage for more information about the Brat standoff format (https://brat.nlplab.org/standoff.html).
The TSV format follows the format employed in SMM4H 2019 Task 2:
tweet_id | begin | end | type | extraction
In addition, we provide a tokenized version of the dataset. It follows the BIO format (similar to CONLL). The files were generated with the brat_to_conll.py script (included), which employs the es_core_news_sm-2.3.1 Spacy model for tokenization.
## Files of Named Entity Recognition subtask.
Content:
- One TSV file per corpus split (train and valid).
- brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid)
- BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid)
- train-valid-txt-files: folder with training and validation text files. One text file per tweet. One sub-- directory per corpus split (train and valid)
- train-valid-txt-files-english: folder with training and validation text files Machine Translated to English.
- test-background-txt-files: folder with the test and background text files. You must make your predictions for these files and upload them to CodaLab. |
zolak/twitter_dataset_81_1713131958 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 273893
num_examples: 669
download_size: 138555
dataset_size: 273893
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145226 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: ainize/bart-base-cnn
metrics: ['accuracy', 'rouge', 'mse']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ainize/bart-base-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model. |
CyberHarem/kamui_female_fire_emblem_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kamui_female_fire_emblem (Fire Emblem)
This is the dataset of kamui_female_fire_emblem (Fire Emblem), containing 500 images and their tags.
The core tags of this character are `long_hair, red_eyes, pointy_ears, hairband, breasts, white_hair, hair_between_eyes, grey_hair, large_breasts, black_hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 759.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamui_female_fire_emblem_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 410.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamui_female_fire_emblem_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1248 | 871.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamui_female_fire_emblem_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 662.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamui_female_fire_emblem_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1248 | 1.24 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kamui_female_fire_emblem_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kamui_female_fire_emblem_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, armor, juliet_sleeves, looking_at_viewer, solo, simple_background, bangs, blue_cape, white_background, black_gloves, holding_sword, closed_mouth, upper_body, open_mouth |
| 1 | 22 |  |  |  |  |  | 1girl, armor, cape, cleavage, solo, looking_at_viewer, smile, official_alternate_costume, gloves, simple_background, medium_breasts, closed_mouth |
| 2 | 6 |  |  |  |  |  | 1girl, bangs, medium_breasts, official_alternate_costume, solo, stone, torn_cape, armor, shiny_hair, toes, barefoot, black_leotard, feet, floating_object, gloves, open_mouth, toeless_legwear, ass, cleavage, full_body, grey_background, looking_at_viewer, simple_background, soles, thighhighs, thighs |
| 3 | 11 |  |  |  |  |  | 1girl, smile, solo, looking_at_viewer, cleavage, navel, shell_bikini, simple_background, seashell, open_mouth, jewelry, white_background |
| 4 | 6 |  |  |  |  |  | 1girl, hair_flower, official_alternate_costume, smile, solo, white_bikini, cleavage, looking_at_viewer, navel, jewelry, underwater |
| 5 | 8 |  |  |  |  |  | 1girl, cleavage, flower_necklace, hair_flower, navel, official_alternate_costume, smile, white_bikini, looking_at_viewer, medium_breasts, solo, bikini_skirt, jewelry, collarbone, simple_background, wreath, open_mouth, white_background, twitter_username |
| 6 | 9 |  |  |  |  |  | 1girl, cleavage, cloud, hair_flower, looking_at_viewer, official_alternate_costume, blue_sky, day, navel, outdoors, smile, solo, white_bikini, blush, collarbone, flower_necklace, wreath, parted_lips, bangs, bikini_skirt, ocean |
| 7 | 9 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, navel, solo, smile, collarbone, underwear_only, blue_bra, blue_panties, blush, lingerie |
| 8 | 30 |  |  |  |  |  | 1girl, halloween_costume, official_alternate_costume, witch_hat, cleavage, looking_at_viewer, solo, smile, bare_shoulders, medium_breasts, earrings, bangs, black_dress, open_mouth, hat_ornament, simple_background, lantern |
| 9 | 7 |  |  |  |  |  | 1girl, simple_background, solo, veil, white_dress, barefoot, butterfly, smile, full_body, open_mouth, single_glove, white_background, white_gloves, anklet, armlet, asymmetrical_clothes, bangs, choker, collarbone, official_alternate_costume, shiny, single_elbow_glove, thighhighs |
| 10 | 13 |  |  |  |  |  | 1girl, fake_animal_ears, playboy_bunny, rabbit_ears, solo, cleavage, alternate_costume, black_leotard, blush, looking_at_viewer, smile, detached_collar, pantyhose, bare_shoulders, wrist_cuffs, strapless, bow, open_mouth, simple_background |
| 11 | 5 |  |  |  |  |  | blush, open_mouth, penis, 2girls, nipples, nude, tongue_out, vaginal, 1boy, hetero, multiple_boys, navel, pussy_juice, solo_focus, sweat, blonde_hair, futa_with_female, saliva, spread_legs, threesome, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | juliet_sleeves | looking_at_viewer | solo | simple_background | bangs | blue_cape | white_background | black_gloves | holding_sword | closed_mouth | upper_body | open_mouth | cape | cleavage | smile | official_alternate_costume | gloves | medium_breasts | stone | torn_cape | shiny_hair | toes | barefoot | black_leotard | feet | floating_object | toeless_legwear | ass | full_body | grey_background | soles | thighhighs | thighs | navel | shell_bikini | seashell | jewelry | hair_flower | white_bikini | underwater | flower_necklace | bikini_skirt | collarbone | wreath | twitter_username | cloud | blue_sky | day | outdoors | blush | parted_lips | ocean | underwear_only | blue_bra | blue_panties | lingerie | halloween_costume | witch_hat | bare_shoulders | earrings | black_dress | hat_ornament | lantern | veil | white_dress | butterfly | single_glove | white_gloves | anklet | armlet | asymmetrical_clothes | choker | shiny | single_elbow_glove | fake_animal_ears | playboy_bunny | rabbit_ears | alternate_costume | detached_collar | pantyhose | wrist_cuffs | strapless | bow | penis | 2girls | nipples | nude | tongue_out | vaginal | 1boy | hetero | multiple_boys | pussy_juice | solo_focus | sweat | blonde_hair | futa_with_female | saliva | spread_legs | threesome | uncensored |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:-----------------|:--------------------|:-------|:--------------------|:--------|:------------|:-------------------|:---------------|:----------------|:---------------|:-------------|:-------------|:-------|:-----------|:--------|:-----------------------------|:---------|:-----------------|:--------|:------------|:-------------|:-------|:-----------|:----------------|:-------|:------------------|:------------------|:------|:------------|:------------------|:--------|:-------------|:---------|:--------|:---------------|:-----------|:----------|:--------------|:---------------|:-------------|:------------------|:---------------|:-------------|:---------|:-------------------|:--------|:-----------|:------|:-----------|:--------|:--------------|:--------|:-----------------|:-----------|:---------------|:-----------|:--------------------|:------------|:-----------------|:-----------|:--------------|:---------------|:----------|:-------|:--------------|:------------|:---------------|:---------------|:---------|:---------|:-----------------------|:---------|:--------|:---------------------|:-------------------|:----------------|:--------------|:--------------------|:------------------|:------------|:--------------|:------------|:------|:--------|:---------|:----------|:-------|:-------------|:----------|:-------|:---------|:----------------|:--------------|:-------------|:--------|:--------------|:-------------------|:---------|:--------------|:------------|:-------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 22 |  |  |  |  |  | X | X | | X | X | X | | | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | X | X | X | X | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | | | X | X | X | | | X | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | X | X | X | | | X | | | | | X | | X | X | X | | X | | | | | | | | | | | | | | | | X | | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | | X | X | | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | X | | | | X | X | | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 30 |  |  |  |  |  | X | | | X | X | X | X | | | | | | | X | | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | | | | X | X | X | | X | | | | | X | | | X | X | | | | | | | X | | | | | | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 13 |  |  |  |  |  | X | | | X | X | X | | | | | | | | X | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CarlosFMarques/sppoot | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 90803.0
num_examples: 4
download_size: 92562
dataset_size: 90803.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
corentinm7/MyoQuant-SDH-Data | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: control
1: sick
config_name: SDH_16k
splits:
- name: test
num_bytes: 683067
num_examples: 3358
- name: train
num_bytes: 2466024
num_examples: 12085
- name: validation
num_bytes: 281243
num_examples: 1344
download_size: 2257836789
dataset_size: 3430334
annotations_creators:
- expert-generated
language: []
language_creators:
- expert-generated
license:
- agpl-3.0
multilinguality: []
pretty_name: SDH staining muscle fiber histology images used to train MyoQuant model.
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- myology
- biology
- histology
- muscle
- cells
- fibers
- myopathy
- SDH
- myoquant
task_categories:
- image-classification
---
# Dataset Card for MyoQuant SDH Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances and Splits](#data-instances-and-splits)
- [Dataset Creation and Annotations](#dataset-creation-and-annotations)
- [Source Data and annotation process](#source-data-and-annotation-process)
- [Who are the annotators ?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases and Limitations](#discussion-of-biases-and-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [The Team Behind this Dataset](#the-team-behind-this-dataset)
- [Partners](#partners)
## Dataset Description
- **Homepage:** https://github.com/lambda-science/MyoQuant
- **Repository:** https://huggingface.co/corentinm7/MyoQuant-SDH-Model
- **Paper:** Yet To Come
- **Leaderboard:** N/A
- **Point of Contact:** [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://cmeyer.fr) email: <corentin.meyer@etu.unistra.fr>
### Dataset Summary
<p align="center">
<img src="https://i.imgur.com/mzALgZL.png" alt="MyoQuant Banner" style="border-radius: 25px;" />
</p>
This dataset contains images of individual muscle fiber used to train [MyoQuant](https://github.com/lambda-science/MyoQuant) SDH Model. The goal of these data is to train a tool to classify SDH stained muscle fibers depending on the presence of mitochondria repartition anomalies. A pathological feature useful for diagnosis and classification in patient with congenital myopathies.
## Dataset Structure
### Data Instances and Splits
A total of 16 787 single muscle fiber images are in the dataset, split in three sets: train, validation and test set.
See the table for the exact count of images in each category:
| | Train (72%) | Validation (8%) | Test (20%) | TOTAL |
|---------|-------------|-----------------|------------|-------------|
| control | 9165 | 1019 | 2546 | 12730 (76%) |
| sick | 2920 | 325 | 812 | 4057 (24%) |
| TOTAL | 12085 | 1344 | 3358 | 16787 |
## Dataset Creation and Annotations
### Source Data and annotation process
To create this dataset of single muscle images, whole slide image of mice muscle fiber with SDH staining were taken from WT mice (1), BIN1 KO mice (10) and mutated DNM2 mice (7). Cells contained within these slides manually counted, labeled and classified in two categories: control (no anomaly) or sick (mitochondria anomaly) by two experts/annotators. Then all single muscle images were extracted from the image using CellPose to detect each individual cell’s boundaries. Resulting in 16787 images from 18 whole image slides.
### Who are the annotators?
All data in this dataset were generated and manually annotated by two experts:
- [**Quentin GIRAUD, PhD Student**](https://twitter.com/GiraudGiraud20) @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <quentin.giraud@igbmc.fr>
- **Charlotte GINESTE, Post-Doc** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <charlotte.gineste@igbmc.fr>
A second pass of verification was done by:
- **Bertrand VERNAY, Platform Leader** @ [Light Microscopy Facility, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy), 1 rue Laurent Fries, 67404 Illkirch, France <bertrand.vernay@igbmc.fr>
### Personal and Sensitive Information
All image data comes from mice, there is no personal nor sensitive information in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The aim of this dataset is to improve congenital myopathies diagnosis by providing tools to automatically quantify specific pathogenic features in muscle fiber histology images.
### Discussion of Biases and Limitations
This dataset has several limitations (non-exhaustive list):
- The images are from mice and thus might not be ideal to represent actual mechanism in human muscle
- The image comes only from two mice models with mutations in two genes (BIN1, DNM2) while congenital myopathies can be caused by a mutation in more than 35+ genes.
- Only mitochondria anomaly was considered to classify cells as "sick", other anomalies were not considered, thus control cells might present other anomalies (such as what is called "cores" in congenital myopathies for examples)
## Additional Information
### Licensing Information
This dataset is under the GNU AFFERO GENERAL PUBLIC LICENSE Version 3, to ensure that what's open-source, stays open-source and available to the community.
### Citation Information
MyoQuant publication with model and data is yet to come.
## The Team Behind this Dataset
**The creator, uploader and main maintainer of this dataset, associated model and MyoQuant is:**
- **[Corentin Meyer, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://cmeyer.fr) Email: <corentin.meyer@etu.unistra.fr> Github: [@lambda-science](https://github.com/lambda-science)**
Special thanks to the experts that created the data for this dataset and all the time they spend counting cells :
- **Quentin GIRAUD, PhD Student** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <quentin.giraud@igbmc.fr>
- **Charlotte GINESTE, Post-Doc** @ [Department Translational Medicine, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/recherche/teams/pathophysiology-of-neuromuscular-diseases), 1 rue Laurent Fries, 67404 Illkirch, France <charlotte.gineste@igbmc.fr>
Last but not least thanks to Bertrand Vernay being at the origin of this project:
- **Bertrand VERNAY, Platform Leader** @ [Light Microscopy Facility, IGBMC, CNRS UMR 7104](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy), 1 rue Laurent Fries, 67404 Illkirch, France <bertrand.vernay@igbmc.fr>
## Partners
<p align="center">
<img src="https://i.imgur.com/m5OGthE.png" alt="Partner Banner" style="border-radius: 25px;" />
</p>
MyoQuant-SDH-Data is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte
|
bigscience-data/roots_indic-as_ted_talks_iwslt | ---
language: as
license: cc-by-nc-nd-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_indic-as_ted_talks_iwslt
# WIT Ted Talks
- Dataset uid: `ted_talks_iwslt`
### Description
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
### Homepage
https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md
### Licensing
- open license
- cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks!
### Speaker Locations
- Southern Europe
- Italy
### Sizes
- 0.0305 % of total
- 0.0736 % of ar
- 0.2002 % of pt
- 0.0128 % of zh
- 0.2236 % of vi
- 0.0330 % of fr
- 0.0545 % of es
- 0.0122 % of en
- 0.3704 % of id
- 0.0373 % of indic-hi
- 0.0330 % of indic-ta
- 0.1393 % of indic-mr
- 0.0305 % of ca
- 0.1179 % of indic-ur
- 0.0147 % of indic-bn
- 0.0240 % of indic-ml
- 0.0244 % of indic-te
- 0.0503 % of indic-gu
- 0.0211 % of indic-kn
- 0.0274 % of eu
- 0.0023 % of indic-as
- 0.0001 % of indic-pa
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ca
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ur
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-as
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-pa
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
gyrojeff/YuzuMarker.FontDetection | ---
license: mit
---
# YuzuMarker.FontDetection
- Code: https://github.com/JeffersonQin/YuzuMarker.FontDetection
- Space: https://huggingface.co/spaces/gyrojeff/YuzuMarker.FontDetection
- Dataset: https://huggingface.co/datasets/gyrojeff/YuzuMarker.FontDetection/tree/master
The generated dataset is now available. Data are on the `master` branch due to initial commit error.
To use the data, each `.tar` package contains a `train`, `val`, `test` split. Move them to `./dataset`, then untar them.
To train the model, provide the path(s) of the untarred folders to the training script. For detail, please check the code repository.
|
cakiki/makefile_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 28586262
num_examples: 1087444
download_size: 22517681
dataset_size: 28586262
---
# Dataset Card for "makefile_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BHOSAI/Translated_English_Wikipedia_on_Azerbaijani | ---
license: cc
task_categories:
- text-generation
- translation
language:
- az
---
# Translated English Wikipedia Dataset to Azerbaijani
Research and Development Center on AI at Baku Higher Oil School presents the unique dataset on Azerbaijani Language!
Considering the lack of the resources in Azerbaijani language, only way to get more pretraining data was to translate foreign (global) sources to provide dataset with meaningful knowledge. However, considering that it is very time consuming to translate very large content, we decided to release a translated wikipedia by using well-known translation models. This means that, translated part is synthetic while the translation quality is high.
## Where can you use this dataset
This kind of dataset are mainly used for the pretraining of LLMs to build foundation model.
## Source Dataset
The source of this dataset is from wiki40B repository (https://huggingface.co/datasets/wiki40b ). We took train split from the dataset, 1.3B translation model and started translation on 4 RTX 4090 GPUs.
There are 2.5M of articles in the source dataset while we succeded to translate 250k articles as v1.0
# Collaborators
Great Thanks to the team members of Baku Higher Oil School Research and Development Center on AI!
---
license: cc-by-sa-4.0
--- |
Davincilee/door_inner_with_SAE | ---
license: other
---
|
LouisM2001/gaml | ---
license: mit
---
|
AdaptLLM/medicine-tasks | ---
configs:
- config_name: ChemProt
data_files:
- split: test
path: ChemProt/test.json
- config_name: MQP
data_files:
- split: test
path: MedQs/test.json
- config_name: PubMedQA
data_files:
- split: test
path: pubmed_qa/test.json
- config_name: RCT
data_files:
- split: test
path: RCT/test.json
- config_name: USMLE
data_files:
- split: test
path: usmle/test.json
task_categories:
- text-classification
- question-answering
- zero-shot-classification
language:
- en
tags:
- biology
- medical
---
# Domain Adaptation of Large Language Models
This repo contains the **evaluation datasets** for our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
**************************** **Updates** ****************************
* 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
* 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉
* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
* 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
### LLaMA-1-7B
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
### LLaMA-1-13B
Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the biomedicine-chat model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat")
# Put your input here:
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X
Please provide your choice first and then provide explanations if possible.'''
# Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!)
our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this
prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]"
# # NOTE:
# # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this:
# your_system_prompt = "Please, answer this question faithfully."
# prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
### Pre-templatized/Formatted Testing Splits
To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
### Raw Datasets
We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages:
- [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
- [RCT](https://huggingface.co/datasets/AdaptLLM/RCT)
- [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA)
- [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA)
- [Headline](https://huggingface.co/datasets/AdaptLLM/Headline)
- [NER](https://huggingface.co/datasets/AdaptLLM/NER)
- [FPB](https://huggingface.co/datasets/AdaptLLM/FPB)
The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code:
```python
from datasets import load_dataset
# MQP:
dataset = load_dataset('medical_questions_pairs')
# PubmedQA:
dataset = load_dataset('bigbio/pubmed_qa')
# USMLE:
dataset=load_dataset('GBaker/MedQA-USMLE-4-options')
# SCOTUS
dataset = load_dataset("lex_glue", 'scotus')
# CaseHOLD
dataset = load_dataset("lex_glue", 'case_hold')
# UNFAIR-ToS
dataset = load_dataset("lex_glue", 'unfair_tos')
```
## Citation
If you find our work helpful, please cite us:
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
``` |
gokul00060/heeidk | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3970
num_examples: 10
download_size: 4124
dataset_size: 3970
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "heeidk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
izhx/mewsli-x | ---
language:
- af
- ar
- az
- bg
- bn
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gu
- he
- hi
- ht
- hu
- id
- it
- ja
- jv
- ka
- kk
- ko
- lt
- ml
- mr
- ms
- my
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- wo
- yo
- zh
license: apache-2.0
pretty_name: Mewsli-X
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
configs:
- config_name: wikipedia_pairs
data_files:
- split: train
path: wikipedia_pairs/train.jsonl.tar.gz
- split: validation
path: wikipedia_pairs/dev.jsonl.tar.gz
- config_name: ar
data_files:
- split: validation
path: wikinews_mentions/ar/dev.jsonl
- split: test
path: wikinews_mentions/ar/test.jsonl
- config_name: de
data_files:
- split: validation
path: wikinews_mentions/de/dev.jsonl
- split: test
path: wikinews_mentions/de/test.jsonl
- config_name: en
data_files:
- split: validation
path: wikinews_mentions/en/dev.jsonl
- split: test
path: wikinews_mentions/en/test.jsonl
- config_name: es
data_files:
- split: validation
path: wikinews_mentions/es/dev.jsonl
- split: test
path: wikinews_mentions/es/test.jsonl
- config_name: fa
data_files:
- split: validation
path: wikinews_mentions/fa/dev.jsonl
- split: test
path: wikinews_mentions/fa/test.jsonl
- config_name: ja
data_files:
- split: validation
path: wikinews_mentions/ja/dev.jsonl
- split: test
path: wikinews_mentions/ja/test.jsonl
- config_name: pl
data_files:
- split: validation
path: wikinews_mentions/pl/dev.jsonl
- split: test
path: wikinews_mentions/pl/test.jsonl
- config_name: ro
data_files:
- split: validation
path: wikinews_mentions/ro/dev.jsonl
- split: test
path: wikinews_mentions/ro/test.jsonl
- config_name: ta
data_files:
- split: validation
path: wikinews_mentions/ta/dev.jsonl
- split: test
path: wikinews_mentions/ta/test.jsonl
- config_name: tr
data_files:
- split: validation
path: wikinews_mentions/tr/dev.jsonl
- split: test
path: wikinews_mentions/tr/test.jsonl
- config_name: uk
data_files:
- split: validation
path: wikinews_mentions/uk/dev.jsonl
- split: test
path: wikinews_mentions/uk/test.jsonl
- config_name: candidate_entities
data_files:
- split: test
path: candidate_entities.jsonl.tar.gz
size_categories:
- 100K<n<1M
---
I generated the dataset following [mewsli-x.md#getting-started](https://github.com/google-research/google-research/blob/master/dense_representations_for_entity_retrieval/mel/mewsli-x.md#getting-started)
and converted into different parts (see [`process.py`](process.py)):
- ar/de/en/es/fa/ja/pl/ro/ta/tr/uk wikinews_mentions dev and test (from `wikinews_mentions-dev/test.jsonl`)
- candidate entities of 50 languages (from `candidate_set_entities.jsonl`)
- English wikipedia_pairs to fine-tune models (from `wikipedia_pairs-dev/train.jsonl`)
Raw data files are in [`raw.tar.gz`](raw.tar.gz), which contains:
```
[...] 535M Feb 24 22:06 candidate_set_entities.jsonl
[...] 9.8M Feb 24 22:06 wikinews_mentions-dev.jsonl
[...] 35M Feb 24 22:06 wikinews_mentions-test.jsonl
[...] 24M Feb 24 22:06 wikipedia_pairs-dev.jsonl
[...] 283M Feb 24 22:06 wikipedia_pairs-train.jsonl
```
**Below is from the original [readme](https://github.com/google-research/google-research/blob/master/dense_representations_for_entity_retrieval/mel/mewsli-x.md)**
# Mewsli-X
Mewsli-X is a multilingual dataset of entity mentions appearing in
[WikiNews](https://www.wikinews.org/) and
[Wikipedia](https://www.wikipedia.org/) articles, that have been automatically
linked to [WikiData](https://www.wikidata.org/) entries.
The primary use case is to evaluate transfer-learning in the zero-shot
cross-lingual setting of the
[XTREME-R benchmark suite](https://sites.research.google/xtremer):
1. Fine-tune a pretrained model on English Wikipedia examples;
2. Evaluate on WikiNews in other languages — **given an *entity mention*
in a WikiNews article, retrieve the correct *entity* from the predefined
candidate set by means of its textual description.**
Mewsli-X constitutes a *doubly zero-shot* task by construction: at test time, a
model has to contend with different languages and a different set of entities
from those observed during fine-tuning.
👉 For data examples and other editions of Mewsli, see [README.md](https://github.com/google-research/google-research/blob/master/dense_representations_for_entity_retrieval/mel/README.md).
👉 Consider submitting to the
**[XTREME-R leaderboard](https://sites.research.google/xtremer)**. The XTREME-R
[repository](https://github.com/google-research/xtreme) includes code for
getting started with training and evaluating a baseline model in PyTorch.
👉 Please cite this paper if you use the data/code in your work: *[XTREME-R:
Towards More Challenging and Nuanced Multilingual Evaluation (Ruder et al.,
2021)](https://aclanthology.org/2021.emnlp-main.802.pdf)*.
> _**NOTE:** New evaluation results on Mewsli-X are **not** directly comparable to those reported in the paper because the dataset required further updates, as detailed [below](#updated-dataset). This does not affect the overall findings of the paper._
```
@inproceedings{ruder-etal-2021-xtreme,
title = "{XTREME}-{R}: Towards More Challenging and Nuanced Multilingual Evaluation",
author = "Ruder, Sebastian and
Constant, Noah and
Botha, Jan and
Siddhant, Aditya and
Firat, Orhan and
Fu, Jinlan and
Liu, Pengfei and
Hu, Junjie and
Garrette, Dan and
Neubig, Graham and
Johnson, Melvin",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.802",
doi = "10.18653/v1/2021.emnlp-main.802",
pages = "10215--10245",
}
``` |
Aayush196/funsd | ---
license: mit
---
|
SocialGrep/the-reddit-covid-dataset | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-covid-dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Disclaimer
Due to file size limitations, we are not able to provide the comments for this dataset. Please feel free to download them from the [website](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset#the-reddit-covid-dataset) - no registration required.
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset)
### Dataset Summary
This corpus contains all the mentions of the term `covid` in post titles on the social media platform Reddit, up until the 25th of October, 2021.
The data was procured from Reddit using [SocialGrep](https://socialgrep.com/?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset).
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC-BY v4.0
### Contributions
[Needs More Information] |
open-llm-leaderboard/details_Joseph717171__BigOrca-2-XB | ---
pretty_name: Evaluation run of Joseph717171/BigOrca-2-XB
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Joseph717171/BigOrca-2-XB](https://huggingface.co/Joseph717171/BigOrca-2-XB)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Joseph717171__BigOrca-2-XB\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T15:07:43.836091](https://huggingface.co/datasets/open-llm-leaderboard/details_Joseph717171__BigOrca-2-XB/blob/main/results_2024-03-21T15-07-43.836091.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\": 0.6072078709868403,\n\
\ \"acc_stderr\": 0.03279976046807251,\n \"acc_norm\": 0.6124532929353041,\n\
\ \"acc_norm_stderr\": 0.033468216147350405,\n \"mc1\": 0.412484700122399,\n\
\ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5800339581744233,\n\
\ \"mc2_stderr\": 0.015870074765992156\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5878839590443686,\n \"acc_stderr\": 0.014383915302225405,\n\
\ \"acc_norm\": 0.6160409556313993,\n \"acc_norm_stderr\": 0.01421244498065189\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6472814180442144,\n\
\ \"acc_stderr\": 0.004768395354146801,\n \"acc_norm\": 0.836387173869747,\n\
\ \"acc_norm_stderr\": 0.0036916784957679682\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\
\ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\
\ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\
\ \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \
\ \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5962264150943396,\n \"acc_stderr\": 0.030197611600197946,\n\
\ \"acc_norm\": 0.5962264150943396,\n \"acc_norm_stderr\": 0.030197611600197946\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n\
\ \"acc_stderr\": 0.04016660030451233,\n \"acc_norm\": 0.6388888888888888,\n\
\ \"acc_norm_stderr\": 0.04016660030451233\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n\
\ \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n\
\ \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726367,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726367\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n\
\ \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n\
\ \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.040703290137070705,\n\
\ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.040703290137070705\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36772486772486773,\n \"acc_stderr\": 0.024833839825562424,\n \"\
acc_norm\": 0.36772486772486773,\n \"acc_norm_stderr\": 0.024833839825562424\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7193548387096774,\n\
\ \"acc_stderr\": 0.02556060472102288,\n \"acc_norm\": 0.7193548387096774,\n\
\ \"acc_norm_stderr\": 0.02556060472102288\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959214,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959214\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n\
\ \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253255,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6302521008403361,\n \"acc_stderr\": 0.03135709599613591,\n \
\ \"acc_norm\": 0.6302521008403361,\n \"acc_norm_stderr\": 0.03135709599613591\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8110091743119267,\n \"acc_stderr\": 0.01678548115920363,\n \"\
acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.01678548115920363\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294635,\n \"\
acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294635\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8480392156862745,\n \"acc_stderr\": 0.02519565842893178,\n \"\
acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.02519565842893178\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023348,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023348\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\
\ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.36607142857142855,\n\
\ \"acc_stderr\": 0.0457237235873743,\n \"acc_norm\": 0.36607142857142855,\n\
\ \"acc_norm_stderr\": 0.0457237235873743\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.043546310772605956,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.043546310772605956\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8058748403575989,\n\
\ \"acc_stderr\": 0.014143970276657578,\n \"acc_norm\": 0.8058748403575989,\n\
\ \"acc_norm_stderr\": 0.014143970276657578\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6791907514450867,\n \"acc_stderr\": 0.025131000233647893,\n\
\ \"acc_norm\": 0.6791907514450867,\n \"acc_norm_stderr\": 0.025131000233647893\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23575418994413408,\n\
\ \"acc_stderr\": 0.014196375686290804,\n \"acc_norm\": 0.23575418994413408,\n\
\ \"acc_norm_stderr\": 0.014196375686290804\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156844,\n\
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156844\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603746,\n\
\ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603746\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4517601043024772,\n\
\ \"acc_stderr\": 0.012710662233660247,\n \"acc_norm\": 0.4517601043024772,\n\
\ \"acc_norm_stderr\": 0.012710662233660247\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5772058823529411,\n \"acc_stderr\": 0.030008562845003476,\n\
\ \"acc_norm\": 0.5772058823529411,\n \"acc_norm_stderr\": 0.030008562845003476\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6454248366013072,\n \"acc_stderr\": 0.019353360547553697,\n \
\ \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.019353360547553697\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\
\ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\
\ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174927,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174927\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8009950248756219,\n\
\ \"acc_stderr\": 0.028231365092758406,\n \"acc_norm\": 0.8009950248756219,\n\
\ \"acc_norm_stderr\": 0.028231365092758406\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.031581495393387324,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.031581495393387324\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.412484700122399,\n\
\ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5800339581744233,\n\
\ \"mc2_stderr\": 0.015870074765992156\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091088\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.34950720242608035,\n \
\ \"acc_stderr\": 0.013133836511705995\n }\n}\n```"
repo_url: https://huggingface.co/Joseph717171/BigOrca-2-XB
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|arc:challenge|25_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|gsm8k|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hellaswag|10_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-07-43.836091.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T15-07-43.836091.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- '**/details_harness|winogrande|5_2024-03-21T15-07-43.836091.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T15-07-43.836091.parquet'
- config_name: results
data_files:
- split: 2024_03_21T15_07_43.836091
path:
- results_2024-03-21T15-07-43.836091.parquet
- split: latest
path:
- results_2024-03-21T15-07-43.836091.parquet
---
# Dataset Card for Evaluation run of Joseph717171/BigOrca-2-XB
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Joseph717171/BigOrca-2-XB](https://huggingface.co/Joseph717171/BigOrca-2-XB) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Joseph717171__BigOrca-2-XB",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T15:07:43.836091](https://huggingface.co/datasets/open-llm-leaderboard/details_Joseph717171__BigOrca-2-XB/blob/main/results_2024-03-21T15-07-43.836091.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": 0.6072078709868403,
"acc_stderr": 0.03279976046807251,
"acc_norm": 0.6124532929353041,
"acc_norm_stderr": 0.033468216147350405,
"mc1": 0.412484700122399,
"mc1_stderr": 0.01723329939957122,
"mc2": 0.5800339581744233,
"mc2_stderr": 0.015870074765992156
},
"harness|arc:challenge|25": {
"acc": 0.5878839590443686,
"acc_stderr": 0.014383915302225405,
"acc_norm": 0.6160409556313993,
"acc_norm_stderr": 0.01421244498065189
},
"harness|hellaswag|10": {
"acc": 0.6472814180442144,
"acc_stderr": 0.004768395354146801,
"acc_norm": 0.836387173869747,
"acc_norm_stderr": 0.0036916784957679682
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901408,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901408
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5962264150943396,
"acc_stderr": 0.030197611600197946,
"acc_norm": 0.5962264150943396,
"acc_norm_stderr": 0.030197611600197946
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.04016660030451233,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04016660030451233
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726367,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726367
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.67,
"acc_stderr": 0.047258156262526094,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526094
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5446808510638298,
"acc_stderr": 0.03255525359340355,
"acc_norm": 0.5446808510638298,
"acc_norm_stderr": 0.03255525359340355
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.04579639422070434
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6068965517241379,
"acc_stderr": 0.040703290137070705,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.040703290137070705
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36772486772486773,
"acc_stderr": 0.024833839825562424,
"acc_norm": 0.36772486772486773,
"acc_norm_stderr": 0.024833839825562424
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7193548387096774,
"acc_stderr": 0.02556060472102288,
"acc_norm": 0.7193548387096774,
"acc_norm_stderr": 0.02556060472102288
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.03510766597959214,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.03510766597959214
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.03287666758603491,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.03287666758603491
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8808290155440415,
"acc_stderr": 0.02338193534812142,
"acc_norm": 0.8808290155440415,
"acc_norm_stderr": 0.02338193534812142
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6102564102564103,
"acc_stderr": 0.024726967886647074,
"acc_norm": 0.6102564102564103,
"acc_norm_stderr": 0.024726967886647074
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253255,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6302521008403361,
"acc_stderr": 0.03135709599613591,
"acc_norm": 0.6302521008403361,
"acc_norm_stderr": 0.03135709599613591
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8110091743119267,
"acc_stderr": 0.01678548115920363,
"acc_norm": 0.8110091743119267,
"acc_norm_stderr": 0.01678548115920363
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4583333333333333,
"acc_stderr": 0.03398110890294635,
"acc_norm": 0.4583333333333333,
"acc_norm_stderr": 0.03398110890294635
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8480392156862745,
"acc_stderr": 0.02519565842893178,
"acc_norm": 0.8480392156862745,
"acc_norm_stderr": 0.02519565842893178
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023348,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023348
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.037683359597287434,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.71900826446281,
"acc_stderr": 0.04103203830514512,
"acc_norm": 0.71900826446281,
"acc_norm_stderr": 0.04103203830514512
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037181,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037181
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7116564417177914,
"acc_stderr": 0.035590395316173425,
"acc_norm": 0.7116564417177914,
"acc_norm_stderr": 0.035590395316173425
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.36607142857142855,
"acc_stderr": 0.0457237235873743,
"acc_norm": 0.36607142857142855,
"acc_norm_stderr": 0.0457237235873743
},
"harness|hendrycksTest-management|5": {
"acc": 0.7378640776699029,
"acc_stderr": 0.043546310772605956,
"acc_norm": 0.7378640776699029,
"acc_norm_stderr": 0.043546310772605956
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.0230866350868414,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.0230866350868414
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8058748403575989,
"acc_stderr": 0.014143970276657578,
"acc_norm": 0.8058748403575989,
"acc_norm_stderr": 0.014143970276657578
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6791907514450867,
"acc_stderr": 0.025131000233647893,
"acc_norm": 0.6791907514450867,
"acc_norm_stderr": 0.025131000233647893
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23575418994413408,
"acc_stderr": 0.014196375686290804,
"acc_norm": 0.23575418994413408,
"acc_norm_stderr": 0.014196375686290804
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.026716118380156844,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.026716118380156844
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.025755865922632945,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.691358024691358,
"acc_stderr": 0.025702640260603746,
"acc_norm": 0.691358024691358,
"acc_norm_stderr": 0.025702640260603746
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4517601043024772,
"acc_stderr": 0.012710662233660247,
"acc_norm": 0.4517601043024772,
"acc_norm_stderr": 0.012710662233660247
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5772058823529411,
"acc_stderr": 0.030008562845003476,
"acc_norm": 0.5772058823529411,
"acc_norm_stderr": 0.030008562845003476
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6454248366013072,
"acc_stderr": 0.019353360547553697,
"acc_norm": 0.6454248366013072,
"acc_norm_stderr": 0.019353360547553697
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6181818181818182,
"acc_stderr": 0.046534298079135075,
"acc_norm": 0.6181818181818182,
"acc_norm_stderr": 0.046534298079135075
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7551020408163265,
"acc_stderr": 0.027529637440174927,
"acc_norm": 0.7551020408163265,
"acc_norm_stderr": 0.027529637440174927
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8009950248756219,
"acc_stderr": 0.028231365092758406,
"acc_norm": 0.8009950248756219,
"acc_norm_stderr": 0.028231365092758406
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.463855421686747,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.463855421686747,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.783625730994152,
"acc_stderr": 0.031581495393387324,
"acc_norm": 0.783625730994152,
"acc_norm_stderr": 0.031581495393387324
},
"harness|truthfulqa:mc|0": {
"mc1": 0.412484700122399,
"mc1_stderr": 0.01723329939957122,
"mc2": 0.5800339581744233,
"mc2_stderr": 0.015870074765992156
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091088
},
"harness|gsm8k|5": {
"acc": 0.34950720242608035,
"acc_stderr": 0.013133836511705995
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
yzhuang/metatree_fri_c4_1000_100 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 590400
num_examples: 720
- name: validation
num_bytes: 229600
num_examples: 280
download_size: 1008641
dataset_size: 820000
---
# Dataset Card for "metatree_fri_c4_1000_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/carmilla_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of carmilla/カーミラ/卡米拉 (Fate/Grand Order)
This is the dataset of carmilla/カーミラ/卡米拉 (Fate/Grand Order), containing 199 images and their tags.
The core tags of this character are `long_hair, breasts, large_breasts, yellow_eyes, white_hair, grey_hair, curly_hair, sunglasses`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 199 | 301.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carmilla_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 199 | 263.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carmilla_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 498 | 501.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carmilla_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/carmilla_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_bikini, bracelet, choker, cleavage, collarbone, eyewear_on_head, looking_at_viewer, see-through, solo, black_nails, nail_polish, simple_background, white_background, fingernails, navel, parted_bangs, smile, thighs, bare_shoulders, brown_eyes, chain, closed_mouth, medium_breasts, parted_lips, upper_body |
| 1 | 13 |  |  |  |  |  | 1girl, black_bikini, cleavage, looking_at_viewer, solo, choker, thighs, bracelet, eyewear_on_head, holding_gun, navel, parted_lips, collarbone, handgun, sitting, crossed_legs, earrings, side-tie_bikini_bottom, see-through |
| 2 | 10 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, black_bikini, day, cloud, navel, outdoors, smile, thighs, blue_sky, choker, collarbone, eyewear_on_head, water, bracelet, ocean, parted_bangs, see-through, bare_shoulders, beach, fingernails, stomach |
| 3 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, long_fingernails, cleavage, nail_polish, sharp_fingernails, horns, simple_background, smile, white_background, blush, detached_sleeves, thighhighs |
| 4 | 6 |  |  |  |  |  | 1girl, bikini_armor, cleavage, cosplay, horns, looking_at_viewer, pauldrons, red_armor, red_bikini, silver_trim, solo, blush, navel, tiara, white_cape, gloves, vambraces, black_thighhighs, choker, simple_background |
| 5 | 6 |  |  |  |  |  | 1girl, necklace, solo, turtleneck_sweater, black_gloves, looking_at_viewer, smile, white_sweater, belt, black_skirt, long_sleeves, open_coat, pantyhose, red_coat, ribbed_sweater, simple_background, sitting, thigh_boots, thighhighs, white_background, black_footwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bikini | bracelet | choker | cleavage | collarbone | eyewear_on_head | looking_at_viewer | see-through | solo | black_nails | nail_polish | simple_background | white_background | fingernails | navel | parted_bangs | smile | thighs | bare_shoulders | brown_eyes | chain | closed_mouth | medium_breasts | parted_lips | upper_body | holding_gun | handgun | sitting | crossed_legs | earrings | side-tie_bikini_bottom | day | cloud | outdoors | blue_sky | water | ocean | beach | stomach | long_fingernails | sharp_fingernails | horns | blush | detached_sleeves | thighhighs | bikini_armor | cosplay | pauldrons | red_armor | red_bikini | silver_trim | tiara | white_cape | gloves | vambraces | black_thighhighs | necklace | turtleneck_sweater | black_gloves | white_sweater | belt | black_skirt | long_sleeves | open_coat | pantyhose | red_coat | ribbed_sweater | thigh_boots | black_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------|:---------|:-----------|:-------------|:------------------|:--------------------|:--------------|:-------|:--------------|:--------------|:--------------------|:-------------------|:--------------|:--------|:---------------|:--------|:---------|:-----------------|:-------------|:--------|:---------------|:-----------------|:--------------|:-------------|:--------------|:----------|:----------|:---------------|:-----------|:-------------------------|:------|:--------|:-----------|:-----------|:--------|:--------|:--------|:----------|:-------------------|:--------------------|:--------|:--------|:-------------------|:-------------|:---------------|:----------|:------------|:------------|:-------------|:--------------|:--------|:-------------|:---------|:------------|:-------------------|:-----------|:---------------------|:---------------|:----------------|:-------|:--------------|:---------------|:------------|:------------|:-----------|:-----------------|:--------------|:-----------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | X | | | X | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 19 |  |  |  |  |  | X | | | | X | | | X | | X | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | | X | X | | | X | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | | | | | X | | X | | | X | X | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
NomeIncrivel/pomniFanDub | ---
license: openrail
---
|
hardikch05/NSText2SQL-custom-50000 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 72523469
num_examples: 50000
download_size: 13535722
dataset_size: 72523469
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AlexXD352/The_Paper | ---
license: openrail
---
|
dkoterwa/kor-sts-cosine-embedding-loss | ---
dataset_info:
features:
- name: id
dtype: int64
- name: genre
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int64
splits:
- name: train
num_bytes: 1034815
num_examples: 5691
- name: test
num_bytes: 247409
num_examples: 1376
- name: valid
num_bytes: 297254
num_examples: 1465
download_size: 828725
dataset_size: 1579478
---
---
license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: genre
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 1034815
num_examples: 5691
- name: valid
num_bytes: 297254
num_examples: 1465
- name: test
num_bytes: 247409
num_examples: 1376
download_size: 837346
dataset_size: 1579478
---
# Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)
For a better dataset description, please visit this GitHub repository prepared by the authors of the article: [LINK](https://github.com/kakaobrain/kor-nlu-datasets) <br>
<br>
**This dataset was prepared by converting [this dataset](https://huggingface.co/datasets/dkoterwa/kor-sts)** Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).
I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library [LINK](https://pytorch.org/docs/stable/generated/torch.nn.CosineEmbeddingLoss.html).
**How to download**
```
from datasets import load_dataset
data = load_dataset("dkoterwa/kor-sts-cosine-embedding-loss")
```
**If you use this dataset for research, please cite this paper:**
```
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
``` |
Multimodal-Fatima/VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_4_open_ended | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_bs_16
num_bytes: 783
num_examples: 4
download_size: 0
dataset_size: 783
---
# Dataset Card for "VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_4_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/anchorage_oni_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of anchorage_oni/泊地棲鬼 (Kantai Collection)
This is the dataset of anchorage_oni/泊地棲鬼 (Kantai Collection), containing 20 images and their tags.
The core tags of this character are `long_hair, white_hair, red_eyes, colored_skin, white_skin, very_long_hair, breasts, pale_skin`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 20 | 29.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anchorage_oni_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 17.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anchorage_oni_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 29.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anchorage_oni_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 26.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anchorage_oni_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 39.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anchorage_oni_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/anchorage_oni_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, abyssal_ship, solo, black_dress, looking_at_viewer, bare_shoulders, elbow_gloves, black_gloves, glowing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | abyssal_ship | solo | black_dress | looking_at_viewer | bare_shoulders | elbow_gloves | black_gloves | glowing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------------|:--------------------|:-----------------|:---------------|:---------------|:----------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
|
newsletter/SDXL-Artists | ---
license: unknown
---
SDXL Artists |
BAAI/JudgeLM-100K |
---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: JudgeLM-100K
task_categories:
- text-generation
---
# Dataset Card for JudgeLM
## Dataset Description
- **Repository:** https://github.com/baaivision/JudgeLM
- **Paper:** https://arxiv.org/abs/2310.17631
### Dataset Summary
JudgeLM-100K dataset contains 100,000 judge samples for training and 5,000 judge samples for validation. All the judge samples have the GPT-4-generated high-quality judgements.
This instruction data can be used to conduct instruction-tuning for language models and make the language model has ability to judge open-ended answer pairs.
See more details in the "Dataset" section and the appendix sections of this [paper](https://arxiv.org/abs/2310.17631).
This produced a judge dataset for instruction-following with 100K examples, obtained at a cost of about $4,000.
### Supported Tasks and Leaderboards
The JudgeLM-100K dataset designed for fine-tuning large language models as judges.
### Languages
The data in JudgeLM-100K are in English.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"review_id": "HZKJaPyrQSdPyxiz2hUZx9",
"question_id": 0,
"answer1_id": "",
"answer2_id": "",
"reviewer_id": 1,
"metadata": {},
"text": "2 8\nAssistant 1's response is not very helpful or detailed. It simply suggests a website for professional tattoo advice, but does not provide any suggestions or guidance on what kind of design the user might consider. On the other hand, Assistant 2's response is much more detailed and helpful. It provides several suggestions for tattoo designs, such as something meaningful, something based on personal interests, or something based on the user's own body. It also advises the user to get a tattoo they like and to seek help from a reputable tattoo artist. The response is relevant, accurate, and provides a good level of detail.",
"score": [2.0, 8.0],
"question_body": "\nI want to get a tattoo but I'm not sure what kind of design to get. What do you suggest?",
"answer1_body": "myDesigner.com\n\nA:\n\n I suggest to ask for professional tattoo advice on Inkchat.com",
"answer2_body": "1. Something meaningful. There are so many ways to go on this.\n2. Something based on personal interests. Something you love, something funny, something you do for fun.\n3. Something based on your own body. The idea of having a tattoo that covers part of your skin is, well, a little unappealing. If you're open to more creative solutions, and you don't mind having a tattoo all over your body, you can go for a design that is meant to be covered up.\nI wouldn't suggest getting a tattoo just to cover up a bad tattoo or because it would be a good story. Get a tattoo you like, that you would love to look at a hundred years from now as a reminder of a time and place in your life.I\u2019m sure you know this, but if you don\u2019t, you don\u2019t have to go at it alone. You have a whole army of people who can help you.\nThe best way to avoid a bad tattoo is to get a lot of help. The most important thing is to find a reputable tattoo artist. Tattoo artists are like any other skilled professional. You want someone who has a lot of experience and training.\nThe best way to",
"answer1_model_id": "dolly-v2-12b",
"answer2_model_id": "mpt-7b",
"answer1_metadata": {"decoding_method": "top_p_sampling"},
"answer2_metadata": {"decoding_method": "top_p_sampling"},
"reference": {
"decoding_method": "None",
"model": "unified_chip2/83622",
"text": "As an AI language model, I cannot recommend any specific tattoo designs. However, I recommend researching various designs and styles to find one that resonates with you and has a personal meaning. It's also important to find a reputable tattoo artist that you trust and feel comfortable with. Best of luck in your tattoo journey!",
"scores": {}
},
"score_w_reference": [3.0, 7.0],
"text_w_reference": "3 7\n\nAssistant 1's response was not very helpful or detailed. It simply suggested a website for professional tattoo advice without providing any context or explanation. The assistant did not address the user's uncertainty about the kind of design to get, which was the main point of the question. \n\nOn the other hand, Assistant 2's response was more detailed and relevant. It provided several suggestions for the user to consider when choosing a tattoo design, such as choosing something meaningful or based on personal interests. It also emphasized the importance of finding a reputable tattoo artist, which aligns with the reference answer. However, the response was a bit rambling and could have been more concise and organized."}
```
### Data Fields
The main data fields are as follows:
* `question_id`: describes the index of seed questions.
* `question_body`: the text of seed questions.
* `answer1_body` / `answer2_body`: the text of answer that generated by LLMs.
* `answer1_model_id` / `answer2_model_id`: the name of LLM that produced this answer.
* `answer1_metadata` / `answer1_metadata`: some metadata informations of answers, including `decoding_method`.
* `text`: the judgements produced by the GPT-4 teacher.
* `score`: the structured score of GPT-4-generated Judgements.
* `reference`: the reference answer that could be used in judging, which could provide extra knowledge or a specific preference.
* `text_w_reference`: the GPT-4-generated Judgements when given `reference` as the reference answer.
- `score_w_reference`: the structured score of `text_w_reference`.
### Data Splits
| | train | val (w/o reference) | val (w/ reference) |
|---------------|------:|----:|----:|
| JudgeLM-100K | 99647 | 4849 | 4942 |
## Additional Information
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@article{zhu2023judgelm,
title={JudgeLM: Fine-tuned Large Language Models are Scalable Judges},
author={Lianghui Zhu and Xinggang Wang and Xinlong Wang},
year={2023},
eprint={2310.17631},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
SandO114/Medical_Interview | ---
license: apache-2.0
task_categories:
- automatic-speech-recognition
- conversational
language:
- en
tags:
- medical
- biology
pretty_name: Medical Interview
size_categories:
- 10K<n<100K
--- |
MITCriticalData/DATASET_81_CITIES_v1.0 | ---
license: mit
---
Zip back using the following command:
`zip -F 81_cities_v1.0.zip --out single-archive_v1.zip` |
sanak/bergains_conversatation | ---
license: apache-2.0
---
|
seokwoni/review_subset_test | ---
dataset_info:
features:
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: Review
dtype: string
- name: en_review
dtype: string
splits:
- name: test
num_bytes: 627736
num_examples: 1000
- name: train
num_bytes: 627736
num_examples: 1000
download_size: 769994
dataset_size: 1255472
---
# Dataset Card for "review_subset_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/natsu_megumi_istheorderarabbit | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Natsu Megumi
This is the dataset of Natsu Megumi, containing 288 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 | 288 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 669 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 724 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 288 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 288 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 288 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 669 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 669 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 557 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 724 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 724 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
KITSCH/miniimagenet-LT | ---
license: openrail
---
# mini-imagenet-LT_longtail-dataset
长尾数据集的分类任务是一个较为常见的话题，但是数据集整理较为麻烦，并且有些数据集例如Imagenet-LT相对来说还是太多，算力不够的情况下做实验成本较高。因此我根据mini-Imagenet重新整理出了mini-Imagenet-LT长尾数据集。并且使用了RSG模型和stable diffusion扩充数据集两种方法进行性能上的对比。
RSG方法,allacc:72.62% headacc:75.91% middleacc:62.45% tailacc:50.83%
SD方法,allacc:75.88% headacc:79.36% middleacc:64.31% tailacc:56.25%
数据集整理过程如下：
1.下载原始mini-imagenet数据集，其由从imagenet中抽取的100个类别的数据构成，每个类别600张图片，总计60000张图片。我们从每个类别的图像中抽取10%的测试集10%的验证集，剩下80%作为训练集。测试集和验证集会生成val.csv和test.csv两个表格文件，记录了路径和标签。
2.为了制作长尾数据集我们需要对训练集进行再抽样。我们对每个类别的训练数据集从中随机抽取10到480不等的数据构成了分布不均匀的长尾数据集，生成train.csv文件，每个类别的数据量记录在cls_label.json。
3.使用stable diffusion扩充我们的长尾数据集，讲每个类别的图片数量从10-480补齐到480张，生成的图片在genimages文件夹加，标签路径文件为gentrain.csv。具体生成方法我们使用图生图的方式，以某图片及其标签作为prompt对现在的图片轮流生成直到补齐480张为止。（由于seed的随机性或图片的问题，生成的图片有部分为损坏的纯黑图片，在下游任务中记得做筛选去除）。语义标签保存在classname.txt中。
The classification task of long-tail data sets is a relatively common topic, but the data set sorting is more troublesome, and some data sets such as Imagenet-LT are relatively too much, and the cost of experimentation is high when the computing power is not enough. So I rearranged the mini-Imagenet-LT long-tail dataset based on mini-Imagenet. And use the RSG model and stable diffusion to expand the data set two methods for performance comparison.
RSG method, allacc: 72.62 headacc: 75.91 middleacc: 62.45 tailacc: 50.83
SD method, allacc: 75.88 headacc: 79.36 middleacc: 64.31 tailacc: 56.25
The process of organizing the data set is as follows:
1. Download the original mini-imagenet dataset, which consists of 100 categories of data extracted from imagenet, with 600 pictures for each category, and a total of 60,000 pictures. We sample 10% of the test set, 10% of the validation set, and the remaining 80% as the training set from images in each category. The test set and validation set will generate two table files, val.csv and test.csv, which record the path and label.
2. In order to make a long tail dataset we need to resample the training set. We randomly sampled 10 to 480 data from the training data set of each category to form an unevenly distributed long-tail data set, and generated a train.csv file. The data volume of each category is recorded in cls_label.json.
3. Use stable diffusion to expand our long-tail data set. The number of pictures in each category is filled from 10-480 to 480. The generated pictures are added in the genimages folder, and the label path file is gentrain.csv. For the specific generation method, we use the image generation method, using a certain image and its label as a prompt to generate the current images in turn until 480 images are completed. (Due to the randomness of the seed or the problem of the picture, some of the generated pictures are damaged pure black pictures, remember to filter and remove them in downstream tasks). Semantic tags are stored in classname.txt.
|
Crapp/sadQuotes | ---
license: cc
---
Sadhguru Quotes collected from https://isha.sadhguru.org/au/en/wisdom/type/quotes |
aqureshiest/earnest-blog-entries | ---
dataset_info:
features:
- name: content
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 3519072
num_examples: 1757
download_size: 1399300
dataset_size: 3519072
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nortsformer/jaffer_cnn_dailymail_tokenized | ---
dataset_info:
features:
- name: article
dtype: string
- name: abstract
dtype: string
splits:
- name: train
num_bytes: 1290630799
num_examples: 287227
- name: test
num_bytes: 51030983
num_examples: 11490
- name: val
num_bytes: 59014911
num_examples: 13368
download_size: 815637520
dataset_size: 1400676693
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
HuggingFaceM4/VSR_chatbot | Invalid username or password. |
ayoubkirouane/Orca-Direct-Preference-Optimization | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 35914686
num_examples: 12859
download_size: 19539812
dataset_size: 35914686
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
teowu/public_iqa_vqa_databases | ---
license: cc-by-nc-4.0
---
|
JaehyungKim/p2c_polite_wiki | ---
license: other
license_name: following-original-dataset
license_link: LICENSE
---
|
CyberHarem/vee_neuralcloud | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of vee/ヴィー/薇 (Neural Cloud)
This is the dataset of vee/ヴィー/薇 (Neural Cloud), containing 24 images and their tags.
The core tags of this character are `black_hair, breasts, eyewear_on_head, multicolored_hair, streaked_hair, pink_hair, sunglasses, bangs, blue_eyes, heterochromia, large_breasts, purple_eyes, long_hair, hair_bun, ahoge, tinted_eyewear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 34.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vee_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 18.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vee_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 69 | 46.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vee_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 29.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vee_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 69 | 64.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vee_neuralcloud/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/vee_neuralcloud',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, jacket, bandaged_arm, holding, mole, smile, bandaged_leg, blush, braid, simple_background, stuffed_animal, stuffed_bunny, white_background, white_dress, :q, belt, mask, short_dress, single_hair_bun, thighs |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, bare_shoulders, cleavage, red_dress, hair_ornament, pink_eyes, sleeveless, virtual_youtuber, braid, hair_ribbon, open_mouth, pink_background, red_ribbon, round_eyewear, santa_dress, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | jacket | bandaged_arm | holding | mole | smile | bandaged_leg | blush | braid | simple_background | stuffed_animal | stuffed_bunny | white_background | white_dress | :q | belt | mask | short_dress | single_hair_bun | thighs | bare_shoulders | red_dress | hair_ornament | pink_eyes | sleeveless | virtual_youtuber | hair_ribbon | open_mouth | pink_background | red_ribbon | round_eyewear | santa_dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:---------|:---------------|:----------|:-------|:--------|:---------------|:--------|:--------|:--------------------|:-----------------|:----------------|:-------------------|:--------------|:-----|:-------|:-------|:--------------|:------------------|:---------|:-----------------|:------------|:----------------|:------------|:-------------|:-------------------|:--------------|:-------------|:------------------|:-------------|:----------------|:--------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | | | | | X | | | X | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
philikai/SQL_Spider_DDL | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: DDL_schema
dtype: string
- name: query
dtype: string
- name: question
dtype: string
- name: db_id
dtype: string
splits:
- name: train
num_bytes: 15862354
num_examples: 8659
- name: validation
num_bytes: 1328365
num_examples: 1034
download_size: 621170
dataset_size: 17190719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
size_categories:
- 1K<n<10K
--- |
CyberHarem/yasaka_kanako_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yasaka_kanako/八坂神奈子/야사카카나코 (Touhou)
This is the dataset of yasaka_kanako/八坂神奈子/야사카카나코 (Touhou), containing 500 images and their tags.
The core tags of this character are `short_hair, purple_hair, red_eyes, hair_ornament, breasts, leaf_hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 516.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yasaka_kanako_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 352.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yasaka_kanako_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 986 | 644.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yasaka_kanako_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 480.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yasaka_kanako_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 986 | 825.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yasaka_kanako_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yasaka_kanako_touhou',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, mirror, shimenawa, solo, leaf, smile, shide, onbashira, sandals |
| 1 | 5 |  |  |  |  |  | 1girl, sakazuki, sake, shimenawa, solo, mirror, shide, blue_hair, sandals, smile |
| 2 | 5 |  |  |  |  |  | 1girl, bangs, black_skirt, mirror, red_shirt, shimenawa, short_over_long_sleeves, solo, looking_at_viewer, shide, simple_background, smile, large_breasts, open_mouth, puffy_sleeves, white_background, brown_skirt, closed_mouth, cowboy_shot, long_skirt |
| 3 | 6 |  |  |  |  |  | 1girl, blush, huge_breasts, nipples, rope, solo, sweat, mosaic_censoring, bangs, purple_eyes, sitting, thick_thighs, looking_at_viewer, navel, pussy_juice, socks, spread_legs |
| 4 | 7 |  |  |  |  |  | 1girl, cleavage, solo, large_breasts, navel, looking_at_viewer, red_bikini, leaf, white_bikini |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | mirror | shimenawa | solo | leaf | smile | shide | onbashira | sandals | sakazuki | sake | blue_hair | bangs | black_skirt | red_shirt | short_over_long_sleeves | looking_at_viewer | simple_background | large_breasts | open_mouth | puffy_sleeves | white_background | brown_skirt | closed_mouth | cowboy_shot | long_skirt | blush | huge_breasts | nipples | rope | sweat | mosaic_censoring | purple_eyes | sitting | thick_thighs | navel | pussy_juice | socks | spread_legs | cleavage | red_bikini | white_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:------------|:-------|:-------|:--------|:--------|:------------|:----------|:-----------|:-------|:------------|:--------|:--------------|:------------|:--------------------------|:--------------------|:--------------------|:----------------|:-------------|:----------------|:-------------------|:--------------|:---------------|:--------------|:-------------|:--------|:---------------|:----------|:-------|:--------|:-------------------|:--------------|:----------|:---------------|:--------|:--------------|:--------|:--------------|:-----------|:-------------|:---------------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | X | | | | | | | | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | |
| 4 | 7 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | X | | | | X | X | X |
|
huggingartists/michael-jackson | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/michael-jackson"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 2.372605 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f3b36efb63495f1fefe130d8c68cf0d8.800x800x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/michael-jackson">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Michael Jackson</div>
<a href="https://genius.com/artists/michael-jackson">
<div style="text-align: center; font-size: 14px;">@michael-jackson</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/michael-jackson).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/michael-jackson")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|1414| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/michael-jackson")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE2_3w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T05:30:33.523762](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w/blob/main/results_2023-10-23T05-30-33.523762.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\": 0.19316275167785235,\n\
\ \"em_stderr\": 0.004042912227684817,\n \"f1\": 0.2405106963087243,\n\
\ \"f1_stderr\": 0.004012764038516629,\n \"acc\": 0.44387175953656505,\n\
\ \"acc_stderr\": 0.010404181547690496\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.19316275167785235,\n \"em_stderr\": 0.004042912227684817,\n\
\ \"f1\": 0.2405106963087243,\n \"f1_stderr\": 0.004012764038516629\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1197877179681577,\n \
\ \"acc_stderr\": 0.008944213403553045\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827948\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w
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_10_23T05_30_33.523762
path:
- '**/details_harness|drop|3_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T05-30-33.523762.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T05-30-33.523762.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- '**/details_harness|winogrande|5_2023-10-23T05-30-33.523762.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T05-30-33.523762.parquet'
- config_name: results
data_files:
- split: 2023_10_23T05_30_33.523762
path:
- results_2023-10-23T05-30-33.523762.parquet
- split: latest
path:
- results_2023-10-23T05-30-33.523762.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE2_3w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE2_3w](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE2_3w) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T05:30:33.523762](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE2_3w/blob/main/results_2023-10-23T05-30-33.523762.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": 0.19316275167785235,
"em_stderr": 0.004042912227684817,
"f1": 0.2405106963087243,
"f1_stderr": 0.004012764038516629,
"acc": 0.44387175953656505,
"acc_stderr": 0.010404181547690496
},
"harness|drop|3": {
"em": 0.19316275167785235,
"em_stderr": 0.004042912227684817,
"f1": 0.2405106963087243,
"f1_stderr": 0.004012764038516629
},
"harness|gsm8k|5": {
"acc": 0.1197877179681577,
"acc_stderr": 0.008944213403553045
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827948
}
}
```
### 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] |
huashen218/convxai-cia-dataset | ---
license: afl-3.0
---
|
BangumiBase/fireforce | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Fire Force
This is the image base of bangumi Fire Force, we detected 60 characters, 5217 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 | 1278 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 231 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 55 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 65 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 89 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 30 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 73 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 140 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 47 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 56 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 264 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 25 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 41 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 173 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 73 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 35 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 70 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 23 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 26 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 57 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 34 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 156 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 29 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 218 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 34 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 67 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 20 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 42 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 33 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 69 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 34 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 41 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 177 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 36 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 299 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 52 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 135 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 26 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 26 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 5 | [Download](40/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 41 | 25 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 15 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 12 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 22 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 85 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 12 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 85 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 33 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 37 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 17 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 122 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 25 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 60 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 6 | [Download](56/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 57 | 5 | [Download](57/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 58 | 10 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 209 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4 | ---
pretty_name: Evaluation run of beaugogh/Llama2-13b-sharegpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beaugogh/Llama2-13b-sharegpt4](https://huggingface.co/beaugogh/Llama2-13b-sharegpt4)\
\ 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_beaugogh__Llama2-13b-sharegpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T09:30:37.851108](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4/blob/main/results_2023-10-15T09-30-37.851108.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\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968571027,\n \"f1\": 0.05843015939597327,\n\
\ \"f1_stderr\": 0.0013137444686186492,\n \"acc\": 0.4200579473220307,\n\
\ \"acc_stderr\": 0.009967774108676528\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968571027,\n\
\ \"f1\": 0.05843015939597327,\n \"f1_stderr\": 0.0013137444686186492\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08794541319181198,\n \
\ \"acc_stderr\": 0.007801162197487711\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n\
\ }\n}\n```"
repo_url: https://huggingface.co/beaugogh/Llama2-13b-sharegpt4
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_10_15T09_30_37.851108
path:
- '**/details_harness|drop|3_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T09-30-37.851108.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- '**/details_harness|gsm8k|5_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T09-30-37.851108.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- '**/details_harness|winogrande|5_2023-10-15T09-30-37.851108.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T09-30-37.851108.parquet'
- config_name: results
data_files:
- split: 2023_10_15T09_30_37.851108
path:
- results_2023-10-15T09-30-37.851108.parquet
- split: latest
path:
- results_2023-10-15T09-30-37.851108.parquet
---
# Dataset Card for Evaluation run of beaugogh/Llama2-13b-sharegpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beaugogh/Llama2-13b-sharegpt4
- **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 [beaugogh/Llama2-13b-sharegpt4](https://huggingface.co/beaugogh/Llama2-13b-sharegpt4) 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_beaugogh__Llama2-13b-sharegpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T09:30:37.851108](https://huggingface.co/datasets/open-llm-leaderboard/details_beaugogh__Llama2-13b-sharegpt4/blob/main/results_2023-10-15T09-30-37.851108.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": 0.001153523489932886,
"em_stderr": 0.00034761798968571027,
"f1": 0.05843015939597327,
"f1_stderr": 0.0013137444686186492,
"acc": 0.4200579473220307,
"acc_stderr": 0.009967774108676528
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571027,
"f1": 0.05843015939597327,
"f1_stderr": 0.0013137444686186492
},
"harness|gsm8k|5": {
"acc": 0.08794541319181198,
"acc_stderr": 0.007801162197487711
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### 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] |
sordonia/wiki_mmlu_from_valid_all | ---
dataset_info:
features:
- name: subject
dtype: string
- name: docno
dtype: int64
- name: score
dtype: float64
- name: dfq
dtype: int64
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1394179124
num_examples: 136591
download_size: 767951516
dataset_size: 1394179124
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wiki_mmlu_from_valid_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_175 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 971691084.0
num_examples: 190827
download_size: 992119081
dataset_size: 971691084.0
---
# Dataset Card for "chunk_175"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jp1924/GanwonSpeech | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: standard_form
dtype: string
- name: dialect_form
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- name: note
dtype: string
- name: eojeolList
list:
- name: id
dtype: int8
- name: eojeol
dtype: string
- name: standard
dtype: string
- name: isDialect
dtype: bool
- name: speaker
struct:
- name: id
dtype: string
- name: name
dtype: string
- name: age
dtype: string
- name: occupation
dtype: string
- name: sex
dtype: string
- name: birthplace
dtype: string
- name: principal_residence
dtype: string
- name: current_residence
dtype: string
- name: education
dtype: string
- name: metadata
struct:
- name: title
dtype: string
- name: creator
dtype: string
- name: distributor
dtype: string
- name: year
dtype: string
- name: category
dtype: string
- name: annotation_level
list: string
- name: sampling
dtype: string
- name: author
dtype: string
- name: publisher
dtype: string
- name: date
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 734680595321.856
num_examples: 1572548
- name: validation
num_bytes: 106720975245.728
num_examples: 276799
download_size: 743809809616
dataset_size: 841401570567.584
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct | ---
pretty_name: Evaluation run of TencentARC/LLaMA-Pro-8B-Instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-06T13:12:05.796061](https://huggingface.co/datasets/open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct/blob/main/results_2024-01-06T13-12-05.796061.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\": 0.5281709550040744,\n\
\ \"acc_stderr\": 0.034190129304935035,\n \"acc_norm\": 0.5299752077852407,\n\
\ \"acc_norm_stderr\": 0.03489132244520177,\n \"mc1\": 0.3353733170134639,\n\
\ \"mc1_stderr\": 0.01652753403966899,\n \"mc2\": 0.4942677553605431,\n\
\ \"mc2_stderr\": 0.015656020272217592\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.014611390804670088,\n \
\ \"acc_norm\": 0.5298634812286689,\n \"acc_norm_stderr\": 0.014585305840007105\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5853415654252141,\n\
\ \"acc_stderr\": 0.0049165612135912825,\n \"acc_norm\": 0.7697669786895041,\n\
\ \"acc_norm_stderr\": 0.004201215520808244\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.040657710025626036,\n\
\ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.040657710025626036\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n\
\ \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04181210050035455\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4161849710982659,\n\
\ \"acc_stderr\": 0.03758517775404948,\n \"acc_norm\": 0.4161849710982659,\n\
\ \"acc_norm_stderr\": 0.03758517775404948\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.04336432707993177,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.04336432707993177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340354,\n\
\ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340354\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\
\ \"acc_stderr\": 0.042270544512322004,\n \"acc_norm\": 0.2807017543859649,\n\
\ \"acc_norm_stderr\": 0.042270544512322004\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3544973544973545,\n \"acc_stderr\": 0.024636830602841997,\n \"\
acc_norm\": 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602841997\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5903225806451613,\n\
\ \"acc_stderr\": 0.027976054915347357,\n \"acc_norm\": 0.5903225806451613,\n\
\ \"acc_norm_stderr\": 0.027976054915347357\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.034304624161038716,\n\
\ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.034304624161038716\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.035886248000917075,\n\
\ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.035886248000917075\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6212121212121212,\n \"acc_stderr\": 0.03456088731993747,\n \"\
acc_norm\": 0.6212121212121212,\n \"acc_norm_stderr\": 0.03456088731993747\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.030276909945178263,\n\
\ \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.030276909945178263\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.48205128205128206,\n \"acc_stderr\": 0.025334667080954942,\n\
\ \"acc_norm\": 0.48205128205128206,\n \"acc_norm_stderr\": 0.025334667080954942\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5042016806722689,\n \"acc_stderr\": 0.0324773433444811,\n \
\ \"acc_norm\": 0.5042016806722689,\n \"acc_norm_stderr\": 0.0324773433444811\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\
acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7192660550458716,\n \"acc_stderr\": 0.019266055045871616,\n \"\
acc_norm\": 0.7192660550458716,\n \"acc_norm_stderr\": 0.019266055045871616\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653063,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653063\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7205882352941176,\n \"acc_stderr\": 0.031493281045079556,\n \"\
acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.031493281045079556\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955924,\n \
\ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955924\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5739910313901345,\n\
\ \"acc_stderr\": 0.033188332862172806,\n \"acc_norm\": 0.5739910313901345,\n\
\ \"acc_norm_stderr\": 0.033188332862172806\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.0418644516301375,\n\
\ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.0418644516301375\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.044492703500683836,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.044492703500683836\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04750077341199984,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04750077341199984\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6257668711656442,\n \"acc_stderr\": 0.03802068102899615,\n\
\ \"acc_norm\": 0.6257668711656442,\n \"acc_norm_stderr\": 0.03802068102899615\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.04541609446503948,\n\
\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.04541609446503948\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7863247863247863,\n\
\ \"acc_stderr\": 0.02685345037700916,\n \"acc_norm\": 0.7863247863247863,\n\
\ \"acc_norm_stderr\": 0.02685345037700916\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \
\ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7113665389527458,\n\
\ \"acc_stderr\": 0.016203792703197797,\n \"acc_norm\": 0.7113665389527458,\n\
\ \"acc_norm_stderr\": 0.016203792703197797\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.02651126136940924,\n\
\ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.02651126136940924\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32625698324022345,\n\
\ \"acc_stderr\": 0.01568044151888918,\n \"acc_norm\": 0.32625698324022345,\n\
\ \"acc_norm_stderr\": 0.01568044151888918\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.565359477124183,\n \"acc_stderr\": 0.028384256704883037,\n\
\ \"acc_norm\": 0.565359477124183,\n \"acc_norm_stderr\": 0.028384256704883037\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5852090032154341,\n\
\ \"acc_stderr\": 0.027982680459759563,\n \"acc_norm\": 0.5852090032154341,\n\
\ \"acc_norm_stderr\": 0.027982680459759563\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.027648477877413327,\n\
\ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.027648477877413327\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.37943262411347517,\n \"acc_stderr\": 0.02894733885161411,\n \
\ \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.02894733885161411\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3754889178617992,\n\
\ \"acc_stderr\": 0.012367945396728208,\n \"acc_norm\": 0.3754889178617992,\n\
\ \"acc_norm_stderr\": 0.012367945396728208\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.45588235294117646,\n \"acc_stderr\": 0.030254372573976687,\n\
\ \"acc_norm\": 0.45588235294117646,\n \"acc_norm_stderr\": 0.030254372573976687\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.49836601307189543,\n \"acc_stderr\": 0.020227726838150117,\n \
\ \"acc_norm\": 0.49836601307189543,\n \"acc_norm_stderr\": 0.020227726838150117\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\
\ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6915422885572139,\n\
\ \"acc_stderr\": 0.032658195885126966,\n \"acc_norm\": 0.6915422885572139,\n\
\ \"acc_norm_stderr\": 0.032658195885126966\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3353733170134639,\n\
\ \"mc1_stderr\": 0.01652753403966899,\n \"mc2\": 0.4942677553605431,\n\
\ \"mc2_stderr\": 0.015656020272217592\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871593\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44200151630022744,\n \
\ \"acc_stderr\": 0.013679514492814581\n }\n}\n```"
repo_url: https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|arc:challenge|25_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|arc:challenge|25_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|arc:challenge|25_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|arc:challenge|25_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|arc:challenge|25_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|arc:challenge|25_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|gsm8k|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|gsm8k|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|gsm8k|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|gsm8k|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|gsm8k|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|gsm8k|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hellaswag|10_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hellaswag|10_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hellaswag|10_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hellaswag|10_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hellaswag|10_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hellaswag|10_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T05-36-22.722674.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T06-15-48.429229.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T06-43-15.789213.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T06-43-15.789213.parquet'
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path:
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path:
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- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-12-05.796061.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T09-13-09.739975.parquet'
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T09-16-27.017995.parquet'
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T06-43-15.789213.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T09-13-09.739975.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T09-16-27.017995.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T11-33-07.175402.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T06-43-15.789213.parquet'
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path:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T06-43-15.789213.parquet'
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-management|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
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- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
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- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T13-05-18.668611.parquet'
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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- split: 2024_01_06T06_15_48.429229
path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T05-36-22.722674.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T13-12-05.796061.parquet'
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T05-36-22.722674.parquet'
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path:
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- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-06T13-12-05.796061.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- '**/details_harness|winogrande|5_2024-01-06T05-36-22.722674.parquet'
- split: 2024_01_06T06_15_48.429229
path:
- '**/details_harness|winogrande|5_2024-01-06T06-15-48.429229.parquet'
- split: 2024_01_06T06_43_15.789213
path:
- '**/details_harness|winogrande|5_2024-01-06T06-43-15.789213.parquet'
- split: 2024_01_06T09_13_09.739975
path:
- '**/details_harness|winogrande|5_2024-01-06T09-13-09.739975.parquet'
- split: 2024_01_06T09_16_27.017995
path:
- '**/details_harness|winogrande|5_2024-01-06T09-16-27.017995.parquet'
- split: 2024_01_06T11_33_07.175402
path:
- '**/details_harness|winogrande|5_2024-01-06T11-33-07.175402.parquet'
- split: 2024_01_06T13_05_18.668611
path:
- '**/details_harness|winogrande|5_2024-01-06T13-05-18.668611.parquet'
- split: 2024_01_06T13_12_05.796061
path:
- '**/details_harness|winogrande|5_2024-01-06T13-12-05.796061.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-06T13-12-05.796061.parquet'
- config_name: results
data_files:
- split: 2024_01_06T05_36_22.722674
path:
- results_2024-01-06T05-36-22.722674.parquet
- split: 2024_01_06T06_15_48.429229
path:
- results_2024-01-06T06-15-48.429229.parquet
- split: 2024_01_06T06_43_15.789213
path:
- results_2024-01-06T06-43-15.789213.parquet
- split: 2024_01_06T09_13_09.739975
path:
- results_2024-01-06T09-13-09.739975.parquet
- split: 2024_01_06T09_16_27.017995
path:
- results_2024-01-06T09-16-27.017995.parquet
- split: 2024_01_06T11_33_07.175402
path:
- results_2024-01-06T11-33-07.175402.parquet
- split: 2024_01_06T13_05_18.668611
path:
- results_2024-01-06T13-05-18.668611.parquet
- split: 2024_01_06T13_12_05.796061
path:
- results_2024-01-06T13-12-05.796061.parquet
- split: latest
path:
- results_2024-01-06T13-12-05.796061.parquet
---
# Dataset Card for Evaluation run of TencentARC/LLaMA-Pro-8B-Instruct
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TencentARC/LLaMA-Pro-8B-Instruct](https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-06T13:12:05.796061](https://huggingface.co/datasets/open-llm-leaderboard/details_TencentARC__LLaMA-Pro-8B-Instruct/blob/main/results_2024-01-06T13-12-05.796061.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": 0.5281709550040744,
"acc_stderr": 0.034190129304935035,
"acc_norm": 0.5299752077852407,
"acc_norm_stderr": 0.03489132244520177,
"mc1": 0.3353733170134639,
"mc1_stderr": 0.01652753403966899,
"mc2": 0.4942677553605431,
"mc2_stderr": 0.015656020272217592
},
"harness|arc:challenge|25": {
"acc": 0.5,
"acc_stderr": 0.014611390804670088,
"acc_norm": 0.5298634812286689,
"acc_norm_stderr": 0.014585305840007105
},
"harness|hellaswag|10": {
"acc": 0.5853415654252141,
"acc_stderr": 0.0049165612135912825,
"acc_norm": 0.7697669786895041,
"acc_norm_stderr": 0.004201215520808244
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5197368421052632,
"acc_stderr": 0.040657710025626036,
"acc_norm": 0.5197368421052632,
"acc_norm_stderr": 0.040657710025626036
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5433962264150943,
"acc_stderr": 0.03065674869673943,
"acc_norm": 0.5433962264150943,
"acc_norm_stderr": 0.03065674869673943
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5,
"acc_stderr": 0.04181210050035455,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04181210050035455
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4161849710982659,
"acc_stderr": 0.03758517775404948,
"acc_norm": 0.4161849710982659,
"acc_norm_stderr": 0.03758517775404948
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.04336432707993177,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.04336432707993177
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4553191489361702,
"acc_stderr": 0.03255525359340354,
"acc_norm": 0.4553191489361702,
"acc_norm_stderr": 0.03255525359340354
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.042270544512322004,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.042270544512322004
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.47586206896551725,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.47586206896551725,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3544973544973545,
"acc_stderr": 0.024636830602841997,
"acc_norm": 0.3544973544973545,
"acc_norm_stderr": 0.024636830602841997
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.043435254289490965,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.043435254289490965
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5903225806451613,
"acc_stderr": 0.027976054915347357,
"acc_norm": 0.5903225806451613,
"acc_norm_stderr": 0.027976054915347357
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3891625615763547,
"acc_stderr": 0.034304624161038716,
"acc_norm": 0.3891625615763547,
"acc_norm_stderr": 0.034304624161038716
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.696969696969697,
"acc_stderr": 0.035886248000917075,
"acc_norm": 0.696969696969697,
"acc_norm_stderr": 0.035886248000917075
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6212121212121212,
"acc_stderr": 0.03456088731993747,
"acc_norm": 0.6212121212121212,
"acc_norm_stderr": 0.03456088731993747
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.772020725388601,
"acc_stderr": 0.030276909945178263,
"acc_norm": 0.772020725388601,
"acc_norm_stderr": 0.030276909945178263
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.48205128205128206,
"acc_stderr": 0.025334667080954942,
"acc_norm": 0.48205128205128206,
"acc_norm_stderr": 0.025334667080954942
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.28888888888888886,
"acc_stderr": 0.027634907264178544,
"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.027634907264178544
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5042016806722689,
"acc_stderr": 0.0324773433444811,
"acc_norm": 0.5042016806722689,
"acc_norm_stderr": 0.0324773433444811
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2913907284768212,
"acc_stderr": 0.037101857261199946,
"acc_norm": 0.2913907284768212,
"acc_norm_stderr": 0.037101857261199946
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7192660550458716,
"acc_stderr": 0.019266055045871616,
"acc_norm": 0.7192660550458716,
"acc_norm_stderr": 0.019266055045871616
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.03372343271653063,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.03372343271653063
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7205882352941176,
"acc_stderr": 0.031493281045079556,
"acc_norm": 0.7205882352941176,
"acc_norm_stderr": 0.031493281045079556
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7172995780590717,
"acc_stderr": 0.029312814153955924,
"acc_norm": 0.7172995780590717,
"acc_norm_stderr": 0.029312814153955924
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5739910313901345,
"acc_stderr": 0.033188332862172806,
"acc_norm": 0.5739910313901345,
"acc_norm_stderr": 0.033188332862172806
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.648854961832061,
"acc_stderr": 0.0418644516301375,
"acc_norm": 0.648854961832061,
"acc_norm_stderr": 0.0418644516301375
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6115702479338843,
"acc_stderr": 0.044492703500683836,
"acc_norm": 0.6115702479338843,
"acc_norm_stderr": 0.044492703500683836
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04750077341199984,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04750077341199984
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6257668711656442,
"acc_stderr": 0.03802068102899615,
"acc_norm": 0.6257668711656442,
"acc_norm_stderr": 0.03802068102899615
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
"acc_stderr": 0.04541609446503948,
"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.04541609446503948
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7863247863247863,
"acc_stderr": 0.02685345037700916,
"acc_norm": 0.7863247863247863,
"acc_norm_stderr": 0.02685345037700916
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7113665389527458,
"acc_stderr": 0.016203792703197797,
"acc_norm": 0.7113665389527458,
"acc_norm_stderr": 0.016203792703197797
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5867052023121387,
"acc_stderr": 0.02651126136940924,
"acc_norm": 0.5867052023121387,
"acc_norm_stderr": 0.02651126136940924
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.32625698324022345,
"acc_stderr": 0.01568044151888918,
"acc_norm": 0.32625698324022345,
"acc_norm_stderr": 0.01568044151888918
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.565359477124183,
"acc_stderr": 0.028384256704883037,
"acc_norm": 0.565359477124183,
"acc_norm_stderr": 0.028384256704883037
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5852090032154341,
"acc_stderr": 0.027982680459759563,
"acc_norm": 0.5852090032154341,
"acc_norm_stderr": 0.027982680459759563
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.027648477877413327,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.027648477877413327
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.37943262411347517,
"acc_stderr": 0.02894733885161411,
"acc_norm": 0.37943262411347517,
"acc_norm_stderr": 0.02894733885161411
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3754889178617992,
"acc_stderr": 0.012367945396728208,
"acc_norm": 0.3754889178617992,
"acc_norm_stderr": 0.012367945396728208
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.45588235294117646,
"acc_stderr": 0.030254372573976687,
"acc_norm": 0.45588235294117646,
"acc_norm_stderr": 0.030254372573976687
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.49836601307189543,
"acc_stderr": 0.020227726838150117,
"acc_norm": 0.49836601307189543,
"acc_norm_stderr": 0.020227726838150117
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.046075820907199756,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.046075820907199756
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6448979591836734,
"acc_stderr": 0.030635655150387638,
"acc_norm": 0.6448979591836734,
"acc_norm_stderr": 0.030635655150387638
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6915422885572139,
"acc_stderr": 0.032658195885126966,
"acc_norm": 0.6915422885572139,
"acc_norm_stderr": 0.032658195885126966
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.463855421686747,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.463855421686747,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7426900584795322,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.7426900584795322,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3353733170134639,
"mc1_stderr": 0.01652753403966899,
"mc2": 0.4942677553605431,
"mc2_stderr": 0.015656020272217592
},
"harness|winogrande|5": {
"acc": 0.7221783741120757,
"acc_stderr": 0.012588918183871593
},
"harness|gsm8k|5": {
"acc": 0.44200151630022744,
"acc_stderr": 0.013679514492814581
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
medarc/mmlu_college_medicine | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: training
num_bytes: 10095
num_examples: 27
- name: test
num_bytes: 85845
num_examples: 173
download_size: 52576
dataset_size: 95940
configs:
- config_name: default
data_files:
- split: training
path: data/training-*
- split: test
path: data/test-*
---
|
KotiyaSanae/nanatsunomaken | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Nanatsu No Maken
This is the image base of bangumi Nanatsu no Maken, we detected 118 characters, 6989 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 | 491 | [Download](0\dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 41 | [Download](1\dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 38 | [Download](2\dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 868 | [Download](3\dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 84 | [Download](4\dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 93 | [Download](5\dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 52 | [Download](6\dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 97 | [Download](7\dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 46 | [Download](8\dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 79 | [Download](9\dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 98 | [Download](10\dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 24 | [Download](11\dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 105 | [Download](12\dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 11 | [Download](13\dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 14 | [Download](14\dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 139 | [Download](15\dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 326 | [Download](16\dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 14 | [Download](17\dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 11 | [Download](18\dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 12 | [Download](19\dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 28 | [Download](20\dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 26 | [Download](21\dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 23 | [Download](22\dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 83 | [Download](23\dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 10 | [Download](24\dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 34 | [Download](25\dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 13 | [Download](26\dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 129 | [Download](27\dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 82 | [Download](28\dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 9 | [Download](29\dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 238 | [Download](30\dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 83 | [Download](31\dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 14 | [Download](32\dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 49 | [Download](33\dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 18 | [Download](34\dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 30 | [Download](35\dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 37 | [Download](36\dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 288 | [Download](37\dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 377 | [Download](38\dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39\dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 20 | [Download](40\dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 35 | [Download](41\dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 34 | [Download](42\dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 9 | [Download](43\dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 19 | [Download](44\dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 29 | [Download](45\dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 17 | [Download](46\dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 14 | [Download](47\dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 16 | [Download](48\dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 7 | [Download](49\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 50 | 18 | [Download](50\dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 25 | [Download](51\dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 28 | [Download](52\dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 14 | [Download](53\dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 39 | [Download](54\dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 12 | [Download](55\dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 58 | [Download](56\dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 59 | [Download](57\dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 12 | [Download](58\dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 24 | [Download](59\dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 13 | [Download](60\dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 34 | [Download](61\dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 26 | [Download](62\dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 6 | [Download](63\dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 64 | 21 | [Download](64\dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 16 | [Download](65\dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 41 | [Download](66\dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 14 | [Download](67\dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 11 | [Download](68\dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 10 | [Download](69\dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 24 | [Download](70\dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 17 | [Download](71\dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 16 | [Download](72\dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 9 | [Download](73\dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 13 | [Download](74\dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 32 | [Download](75\dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 17 | [Download](76\dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 494 | [Download](77\dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 29 | [Download](78\dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 208 | [Download](79\dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 16 | [Download](80\dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 17 | [Download](81\dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 15 | [Download](82\dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 7 | [Download](83\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 84 | 381 | [Download](84\dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 14 | [Download](85\dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 13 | [Download](86\dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 10 | [Download](87\dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 12 | [Download](88\dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 78 | [Download](89\dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 11 | [Download](90\dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 37 | [Download](91\dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 19 | [Download](92\dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 7 | [Download](93\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 94 | 15 | [Download](94\dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 25 | [Download](95\dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 6 | [Download](96\dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 97 | 34 | [Download](97\dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 17 | [Download](98\dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 10 | [Download](99\dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 15 | [Download](100\dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 15 | [Download](101\dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 11 | [Download](102\dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 10 | [Download](103\dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 7 | [Download](104\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 105 | 9 | [Download](105\dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 11 | [Download](106\dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 6 | [Download](107\dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 108 | 9 | [Download](108\dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 16 | [Download](109\dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 7 | [Download](110\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 111 | 7 | [Download](111\dataset.zip) |  |  |  |  |  |  |  | N/A |
| 112 | 17 | [Download](112\dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 8 | [Download](113\dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 13 | [Download](114\dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 9 | [Download](115\dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 7 | [Download](116\dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 295 | [Download](-1\dataset.zip) |  |  |  |  |  |  |  |  |
|
qazisaad/news_recommendations_base_vectorized | ---
dataset_info:
features:
- name: category
dtype: string
- name: sub-category
dtype: string
- name: title
dtype: string
- name: times
dtype: timestamp[ns]
- name: url
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 7692557
num_examples: 3981
download_size: 9317253
dataset_size: 7692557
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "news_recommendations_base_vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
weaviate/WithRetrieval-SchemaSplit-Test-40 | ---
license: apache-2.0
---
|
FudanSELab/SO_KGXQR_HISTORY | ---
license: mit
dataset_info:
- config_name: history_csharp
features:
- name: so_question_id
dtype: string
- name: historical_title
dtype: string
splits:
- name: test
num_bytes: 133783
num_examples: 2000
download_size: 95916
dataset_size: 133783
- config_name: history_java
features:
- name: so_question_id
dtype: string
- name: historical_title
dtype: string
splits:
- name: test
num_bytes: 132058
num_examples: 2000
download_size: 93956
dataset_size: 132058
- config_name: history_javascript
features:
- name: so_question_id
dtype: string
- name: historical_title
dtype: string
splits:
- name: test
num_bytes: 132306
num_examples: 2000
download_size: 91896
dataset_size: 132306
- config_name: history_python
features:
- name: so_question_id
dtype: string
- name: historical_title
dtype: string
splits:
- name: test
num_bytes: 132390
num_examples: 2000
download_size: 92772
dataset_size: 132390
configs:
- config_name: history_csharp
data_files:
- split: test
path: history_csharp/test-*
- config_name: history_java
data_files:
- split: test
path: history_java/test-*
- config_name: history_javascript
data_files:
- split: test
path: history_javascript/test-*
- config_name: history_python
data_files:
- split: test
path: history_python/test-*
language:
- en
size_categories:
- 1K<n<10K
---
## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) |
open-llm-leaderboard/details_alignment-handbook__zephyr-7b-sft-full | ---
pretty_name: Evaluation run of alignment-handbook/zephyr-7b-sft-full
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_alignment-handbook__zephyr-7b-sft-full\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-16T04:10:47.293422](https://huggingface.co/datasets/open-llm-leaderboard/details_alignment-handbook__zephyr-7b-sft-full/blob/main/results_2024-01-16T04-10-47.293422.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\": 0.5996645941169282,\n\
\ \"acc_stderr\": 0.03304659614732094,\n \"acc_norm\": 0.606170977115199,\n\
\ \"acc_norm_stderr\": 0.03373440620721248,\n \"mc1\": 0.27906976744186046,\n\
\ \"mc1_stderr\": 0.015702107090627897,\n \"mc2\": 0.4170825132034481,\n\
\ \"mc2_stderr\": 0.014670567942290037\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5435153583617748,\n \"acc_stderr\": 0.014555949760496442,\n\
\ \"acc_norm\": 0.5767918088737202,\n \"acc_norm_stderr\": 0.014438036220848036\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.608743278231428,\n\
\ \"acc_stderr\": 0.004870342592915048,\n \"acc_norm\": 0.8082055367456682,\n\
\ \"acc_norm_stderr\": 0.003929076276473378\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.038607315993160904,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.038607315993160904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n\
\ \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.03899073687357335,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.03899073687357335\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\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\": 0.04533838195929778,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929778\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\
\ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\
\ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\
\ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3968253968253968,\n \"acc_stderr\": 0.02519710107424649,\n \"\
acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.02519710107424649\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7354838709677419,\n \"acc_stderr\": 0.02509189237885928,\n \"\
acc_norm\": 0.7354838709677419,\n \"acc_norm_stderr\": 0.02509189237885928\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\
\ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5897435897435898,\n \"acc_stderr\": 0.024939313906940798,\n\
\ \"acc_norm\": 0.5897435897435898,\n \"acc_norm_stderr\": 0.024939313906940798\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37407407407407406,\n \"acc_stderr\": 0.02950286112895529,\n \
\ \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.02950286112895529\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658753,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658753\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7926605504587156,\n \"acc_stderr\": 0.017381415563608674,\n \"\
acc_norm\": 0.7926605504587156,\n \"acc_norm_stderr\": 0.017381415563608674\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\
acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7401960784313726,\n \"acc_stderr\": 0.030778554678693264,\n \"\
acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.030778554678693264\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \
\ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\
\ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\
\ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847834,\n\
\ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847834\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\
\ \"acc_stderr\": 0.023902325549560396,\n \"acc_norm\": 0.8418803418803419,\n\
\ \"acc_norm_stderr\": 0.023902325549560396\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.014866821664709588,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.014866821664709588\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.02500931379006971,\n\
\ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.02500931379006971\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3653631284916201,\n\
\ \"acc_stderr\": 0.01610483388014229,\n \"acc_norm\": 0.3653631284916201,\n\
\ \"acc_norm_stderr\": 0.01610483388014229\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.0267874531119065,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.0267874531119065\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6655948553054662,\n\
\ \"acc_stderr\": 0.026795422327893934,\n \"acc_norm\": 0.6655948553054662,\n\
\ \"acc_norm_stderr\": 0.026795422327893934\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6512345679012346,\n \"acc_stderr\": 0.02651759772446501,\n\
\ \"acc_norm\": 0.6512345679012346,\n \"acc_norm_stderr\": 0.02651759772446501\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4432624113475177,\n \"acc_stderr\": 0.029634838473766006,\n \
\ \"acc_norm\": 0.4432624113475177,\n \"acc_norm_stderr\": 0.029634838473766006\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4256844850065189,\n\
\ \"acc_stderr\": 0.012628393551811943,\n \"acc_norm\": 0.4256844850065189,\n\
\ \"acc_norm_stderr\": 0.012628393551811943\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.029972807170464622,\n\
\ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.029972807170464622\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6143790849673203,\n \"acc_stderr\": 0.019691459052354032,\n \
\ \"acc_norm\": 0.6143790849673203,\n \"acc_norm_stderr\": 0.019691459052354032\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6857142857142857,\n \"acc_stderr\": 0.029719329422417475,\n\
\ \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.029719329422417475\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\
\ \"acc_stderr\": 0.027403859410786855,\n \"acc_norm\": 0.8159203980099502,\n\
\ \"acc_norm_stderr\": 0.027403859410786855\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036847,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036847\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27906976744186046,\n\
\ \"mc1_stderr\": 0.015702107090627897,\n \"mc2\": 0.4170825132034481,\n\
\ \"mc2_stderr\": 0.014670567942290037\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843905\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.287338893100834,\n \
\ \"acc_stderr\": 0.012464677060107078\n }\n}\n```"
repo_url: https://huggingface.co/alignment-handbook/zephyr-7b-sft-full
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|arc:challenge|25_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|arc:challenge|25_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|gsm8k|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|gsm8k|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hellaswag|10_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hellaswag|10_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T04-06-55.134598.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T04-06-55.134598.parquet'
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- split: 2024_01_16T04_10_47.293422
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: 2024_01_16T04_10_47.293422
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T04-06-55.134598.parquet'
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-16T04-10-47.293422.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- '**/details_harness|winogrande|5_2024-01-16T04-06-55.134598.parquet'
- split: 2024_01_16T04_10_47.293422
path:
- '**/details_harness|winogrande|5_2024-01-16T04-10-47.293422.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-16T04-10-47.293422.parquet'
- config_name: results
data_files:
- split: 2024_01_16T04_06_55.134598
path:
- results_2024-01-16T04-06-55.134598.parquet
- split: 2024_01_16T04_10_47.293422
path:
- results_2024-01-16T04-10-47.293422.parquet
- split: latest
path:
- results_2024-01-16T04-10-47.293422.parquet
---
# Dataset Card for Evaluation run of alignment-handbook/zephyr-7b-sft-full
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_alignment-handbook__zephyr-7b-sft-full",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-16T04:10:47.293422](https://huggingface.co/datasets/open-llm-leaderboard/details_alignment-handbook__zephyr-7b-sft-full/blob/main/results_2024-01-16T04-10-47.293422.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": 0.5996645941169282,
"acc_stderr": 0.03304659614732094,
"acc_norm": 0.606170977115199,
"acc_norm_stderr": 0.03373440620721248,
"mc1": 0.27906976744186046,
"mc1_stderr": 0.015702107090627897,
"mc2": 0.4170825132034481,
"mc2_stderr": 0.014670567942290037
},
"harness|arc:challenge|25": {
"acc": 0.5435153583617748,
"acc_stderr": 0.014555949760496442,
"acc_norm": 0.5767918088737202,
"acc_norm_stderr": 0.014438036220848036
},
"harness|hellaswag|10": {
"acc": 0.608743278231428,
"acc_stderr": 0.004870342592915048,
"acc_norm": 0.8082055367456682,
"acc_norm_stderr": 0.003929076276473378
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.038607315993160904,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.038607315993160904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6566037735849056,
"acc_stderr": 0.02922452646912479,
"acc_norm": 0.6566037735849056,
"acc_norm_stderr": 0.02922452646912479
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
"acc_stderr": 0.03899073687357335,
"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.03899073687357335
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"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": 0.04533838195929778,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929778
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.03261936918467382,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.03261936918467382
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.40350877192982454,
"acc_stderr": 0.046151869625837026,
"acc_norm": 0.40350877192982454,
"acc_norm_stderr": 0.046151869625837026
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.02519710107424649,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.02519710107424649
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7354838709677419,
"acc_stderr": 0.02509189237885928,
"acc_norm": 0.7354838709677419,
"acc_norm_stderr": 0.02509189237885928
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5270935960591133,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.5270935960591133,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885415,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885415
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8341968911917098,
"acc_stderr": 0.026839845022314415,
"acc_norm": 0.8341968911917098,
"acc_norm_stderr": 0.026839845022314415
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5897435897435898,
"acc_stderr": 0.024939313906940798,
"acc_norm": 0.5897435897435898,
"acc_norm_stderr": 0.024939313906940798
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37407407407407406,
"acc_stderr": 0.02950286112895529,
"acc_norm": 0.37407407407407406,
"acc_norm_stderr": 0.02950286112895529
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6176470588235294,
"acc_stderr": 0.031566630992154156,
"acc_norm": 0.6176470588235294,
"acc_norm_stderr": 0.031566630992154156
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.03822746937658753,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.03822746937658753
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7926605504587156,
"acc_stderr": 0.017381415563608674,
"acc_norm": 0.7926605504587156,
"acc_norm_stderr": 0.017381415563608674
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.03376922151252336,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.03376922151252336
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7401960784313726,
"acc_stderr": 0.030778554678693264,
"acc_norm": 0.7401960784313726,
"acc_norm_stderr": 0.030778554678693264
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7341772151898734,
"acc_stderr": 0.02875679962965834,
"acc_norm": 0.7341772151898734,
"acc_norm_stderr": 0.02875679962965834
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6636771300448431,
"acc_stderr": 0.031708824268455,
"acc_norm": 0.6636771300448431,
"acc_norm_stderr": 0.031708824268455
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7251908396946565,
"acc_stderr": 0.03915345408847834,
"acc_norm": 0.7251908396946565,
"acc_norm_stderr": 0.03915345408847834
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8418803418803419,
"acc_stderr": 0.023902325549560396,
"acc_norm": 0.8418803418803419,
"acc_norm_stderr": 0.023902325549560396
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.014866821664709588,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.014866821664709588
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.684971098265896,
"acc_stderr": 0.02500931379006971,
"acc_norm": 0.684971098265896,
"acc_norm_stderr": 0.02500931379006971
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3653631284916201,
"acc_stderr": 0.01610483388014229,
"acc_norm": 0.3653631284916201,
"acc_norm_stderr": 0.01610483388014229
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6764705882352942,
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"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.0267874531119065
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6655948553054662,
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"acc_norm": 0.6655948553054662,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.6512345679012346,
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},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.4432624113475177,
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
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},
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"acc": 0.6,
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6857142857142857,
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
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"acc_norm": 0.8,
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.5060240963855421,
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},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
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},
"harness|truthfulqa:mc|0": {
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"mc2": 0.4170825132034481,
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},
"harness|winogrande|5": {
"acc": 0.760852407261247,
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},
"harness|gsm8k|5": {
"acc": 0.287338893100834,
"acc_stderr": 0.012464677060107078
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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result-kand2-sdxl-wuerst-karlo/ac298fb2 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 165
num_examples: 10
download_size: 1316
dataset_size: 165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ac298fb2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ohsuz/DACON_16000_TEMP | ---
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 10359910
num_examples: 16000
download_size: 3981363
dataset_size: 10359910
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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