datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
vhtran/de-en-official | ---
license: cc-by-4.0
---
|
ynklab/XCodeSearchNet | ---
license: mit
language:
- en
- fr
- ja
- zh
tags:
- codesearch
pretty_name: XCodeSearchNet
---
[Paper on arXiv](https://arxiv.org/abs/2306.15604)
## pre-training data
You need to manually combine each dataset if you want to use a multilingual dataset.
```python
from datasets import load_dataset
xcsn_pt_python_en = load_dataset("ynklab/XCodeSearchNet", data_dir='pretraining/python/en')
"""
DatasetDict({
train: Dataset({
features: ['function_tokens', 'docstring'],
num_rows: 453623
})
validation: Dataset({
features: ['function_tokens', 'docstring'],
num_rows: 4596
})
test: Dataset({
features: ['function_tokens', 'docstring'],
num_rows: 45283
})
})
"""
print(xcsn_pt_python_en['train'][0])
"""
{
'function_tokens': ['def', 'get_feature_ide_paths', '(', 'container_dir', ',', 'product_name', ')', ':', 'repo_name', '=', 'get_repo_name', '(', 'container_dir', ')', 'class', 'Paths', '(', 'object', ')', ':', 'feature_order_json', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/feature_order.json'", ')', 'model_xml_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/model.xml'", ')', 'config_file_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/products/'", ',', 'repo_name', ',', 'product_name', ',', "'product.equation.config'", ')', 'equation_file_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'products'", ',', 'product_name', ',', "'product.equation'", ')', 'product_spec_path', '=', 'os', '.', 'path', '.', 'join', '(', 'container_dir', ',', "'_lib/featuremodel/productline/products/'", ',', 'repo_name', ',', "'product_spec.json'", ')', 'return', 'Paths'],
'docstring': 'Takes the container_dir and the product name and returns all relevant paths from the\n feature_order_json to the config_file_path.\n :param container_dir: the full path of the container dir\n :param product_name: the name of the product\n :return: object with divert path attributes'
}
"""
```
## fine-tuning data
```python
from datasets import load_dataset
xcsn_ft_python_en = load_dataset("ynklab/XCodeSearchNet", data_dir='finetuning/python/en')
"""
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 1648684
})
validation: Dataset({
features: ['text'],
num_rows: 92426
})
})
"""
print(xcsn_ft_python_en['train'][0])
"""
{
'text': '1<CODESPLIT><CODESPLIT><CODESPLIT>Logs the definition of the object that was just auto - decorated inside the ipython notebook .<CODESPLIT>def _logdef ( self , n , o , otype ) : import re try : #The latest input cell will be the one that this got executed #from. TODO: actually, if acorn got imported after the fact, then #the import would have caused all the undecorated functions to be #decorated as soon as acorn imported. I suppose we just won\'t have #any code for that case. if otype == "classes" : cellno = max ( [ int ( k [ 2 : ] ) for k in self . shell . user_ns . keys ( ) if re . match ( "_i\\d+" , k ) ] ) elif otype == "functions" : cellno = int ( o . __code__ . co_filename . strip ( "<>" ) . split ( \'-\' ) [ 2 ] ) except : #This must not have been an ipython notebook declaration, so we #don\'t store the code. cellno = None pass code = "" if cellno is not None : cellstr = "_i{0:d}" . format ( cellno ) if cellstr in self . shell . user_ns : cellcode = self . shell . user_ns [ cellstr ] import ast astm = ast . parse ( cellcode ) ab = astm . body parts = { ab [ i ] . name : ( ab [ i ] . lineno , None if i + 1 >= len ( ab ) else ab [ i + 1 ] . lineno ) for i , d in enumerate ( ab ) } if n in parts : celllines = cellcode . split ( \'\\n\' ) start , end = parts [ n ] if end is not None : code = celllines [ start - 1 : end - 1 ] else : code = celllines [ start - 1 : ] #Now, we actually create the entry. Since the execution for function #definitions is almost instantaneous, we just log the pre and post #events at the same time. from time import time from acorn . logging . database import record entry = { "m" : "def" , "a" : None , "s" : time ( ) , "r" : None , "c" : code , } from acorn import msg record ( "__main__.{}" . format ( n ) , entry , diff = True ) msg . info ( entry , 1 )'
}
"""
```
|
Gabriel/cnn_daily_swe | ---
language:
- sv
license:
- mit
size_categories:
- 100K<n<1M
source_datasets:
- https://github.com/huggingface/datasets/tree/master/datasets/cnn_dailymail
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for Swedish CNN Dailymail Dataset
The Swedish CNN/DailyMail dataset has only been machine-translated to improve downstream fine-tuning on Swedish summarization tasks.
## Dataset Summary
Read about the full details at original English version: https://huggingface.co/datasets/cnn_dailymail
### Data Fields
- `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from
- `article`: a string containing the body of the news article
- `highlights`: a string containing the highlight of the article as written by the article author
### Data Splits
The Swedish CNN/DailyMail dataset follows the same splits as the original English version and has 3 splits: _train_, _validation_, and _test_.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 287,113 |
| Validation | 13,368 |
| Test | 11,490 |
|
xzuyn/manythings-translations-alpaca | ---
task_categories:
- translation
- text-generation
language:
- en
size_categories:
- 1M<n<10M
---
[Original Dataset](http://www.manythings.org/anki/)
3,164,972 translations from English to 84 other languages.
I've duplicated it to be *to* and *from* English, so it's now 6,329,944 translations. |
autoevaluate/autoeval-eval-futin__feed-top_vi-71f14a-2175469964 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: facebook/opt-6.7b
metrics: []
dataset_name: futin/feed
dataset_config: top_vi
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: futin/feed
* Config: top_vi
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
HydraLM/partitioned_v3_standardized_012 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: dataset_id
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
num_bytes: 19913272.771467183
num_examples: 37033
download_size: 16406844
dataset_size: 19913272.771467183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "partitioned_v3_standardized_012"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hf-doc-build/doc-build-dev | ---
license: mit
tags:
- documentation
pretty_name: HF Documentation (PRs)
---
This is a dataset which contains the docs from all the PRs updating one of the docs from https://huggingface.co/docs.
It is automatically updated by this [github action](https://github.com/huggingface/doc-builder/blob/main/.github/workflows/build_pr_documentation.yml) from the [doc-buider](https://github.com/huggingface/doc-builder) repo. |
infiagent/DABench | ---
license: apache-2.0
tags:
- code
--- |
NeuralShell/Gore-Blood-Dataset-v1.0 | ---
license: mit
task_categories:
- image-to-image
- image-classification
- image-segmentation
language:
- en
tags:
- art
- blood
- death
- not-for-all-audiences
pretty_name: gore-blood
size_categories:
- n<1K
---
# Gore Blood Dataset (Version 1.0)
## Overview
The Gore Blood Dataset (Version 1.0) is a collection of images curated by NeuralShell specifically designed for training AI models, particularly for stable diffusion models. These images are intended to aid in the development and enhancement of machine learning models, leveraging the advancements in the field of computer vision and AI.
## Dataset Information
- **Dataset Name**: Gore-Blood-Dataset-v1.0
- **Creator**: NeuralShell
- **Base Model Version**: sd v2.1
- **AI Refiners Version**: sd v1.5
## Purpose
This dataset serves as a resource to train AI models, particularly focusing on stable diffusion models within the realm of computer vision. It contains images pertinent to blood-related visual data, curated and optimized using the base model version sd v2.1 and AI refiners version sd v1.5.
## Contents
The dataset comprises a diverse collection of Gore images related to blood, meticulously chosen and preprocessed to facilitate robust model training. It is a valuable resource for researchers and developers aiming to advance the capabilities of AI in understanding and interpreting blood-related visual information.
## Usage
This dataset can be utilized for various purposes within the field of computer vision and machine learning, including but not limited to:
- Training stable diffusion models
- Experimentation and research in AI development
- Benchmarking and evaluation of new algorithms and models
## Acknowledgments
We would like to express our gratitude to the contributors and researchers involved in the creation and curation of this dataset. Their efforts have enabled the availability of this resource for the wider AI and machine learning community.
## Citation
If you use this dataset in your research or work, kindly cite it using the following format:
```
@dataset{Gore-Blood-Dataset-v1.0,
author = {NeuralShell},
title = {Gore Blood Dataset},
year = {2023},
publisher = {Hugging Face},
version = {1.0},
url = {https://huggingface.co/NeuralShell/Gore-Blood-Dataset-v1.0}
}
```
## License
This dataset is provided under the specified license terms by NeuralShell. Please refer to the LICENSE file accompanying the dataset for detailed information on permitted usage and redistribution. |
open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.3 | ---
pretty_name: Evaluation run of RatanRohith/NeuralPizza-7B-V0.3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [RatanRohith/NeuralPizza-7B-V0.3](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.3)\
\ 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_RatanRohith__NeuralPizza-7B-V0.3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-01T17:49:35.277472](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.3/blob/main/results_2024-02-01T17-49-35.277472.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\": 0.6463893662332927,\n\
\ \"acc_stderr\": 0.03224452934744884,\n \"acc_norm\": 0.6479975016510882,\n\
\ \"acc_norm_stderr\": 0.032891778674840784,\n \"mc1\": 0.5140758873929009,\n\
\ \"mc1_stderr\": 0.017496563717042776,\n \"mc2\": 0.6793456051279607,\n\
\ \"mc2_stderr\": 0.015369634410362739\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6783276450511946,\n \"acc_stderr\": 0.013650488084494162,\n\
\ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393441\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.710017924716192,\n\
\ \"acc_stderr\": 0.004528264116475881,\n \"acc_norm\": 0.8738299143596893,\n\
\ \"acc_norm_stderr\": 0.0033136235601649287\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047423976,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047423976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\
\ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.04161808503501531,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.04161808503501531\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137595,\n \"\
acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137595\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.0351760354036101,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.0351760354036101\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7310924369747899,\n \"acc_stderr\": 0.028801392193631273,\n\
\ \"acc_norm\": 0.7310924369747899,\n \"acc_norm_stderr\": 0.028801392193631273\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546837,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546837\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39553072625698327,\n\
\ \"acc_stderr\": 0.016353415410075775,\n \"acc_norm\": 0.39553072625698327,\n\
\ \"acc_norm_stderr\": 0.016353415410075775\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.02573885479781873,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.02573885479781873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460842,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460842\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142248,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142248\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\
\ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\
\ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031204,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031204\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6552287581699346,\n \"acc_stderr\": 0.01922832201869664,\n \
\ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.01922832201869664\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.02411267824090083,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.02411267824090083\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5140758873929009,\n\
\ \"mc1_stderr\": 0.017496563717042776,\n \"mc2\": 0.6793456051279607,\n\
\ \"mc2_stderr\": 0.015369634410362739\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938273\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5890826383623957,\n \
\ \"acc_stderr\": 0.01355213290142322\n }\n}\n```"
repo_url: https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|arc:challenge|25_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|gsm8k|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hellaswag|10_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-49-35.277472.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T17-49-35.277472.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- '**/details_harness|winogrande|5_2024-02-01T17-49-35.277472.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-01T17-49-35.277472.parquet'
- config_name: results
data_files:
- split: 2024_02_01T17_49_35.277472
path:
- results_2024-02-01T17-49-35.277472.parquet
- split: latest
path:
- results_2024-02-01T17-49-35.277472.parquet
---
# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-7B-V0.3](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.3) 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_RatanRohith__NeuralPizza-7B-V0.3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-01T17:49:35.277472](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.3/blob/main/results_2024-02-01T17-49-35.277472.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
{
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"mc1_stderr": 0.017496563717042776,
"mc2": 0.6793456051279607,
"mc2_stderr": 0.015369634410362739
},
"harness|arc:challenge|25": {
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"acc_norm": 0.7107508532423208,
"acc_norm_stderr": 0.013250012579393441
},
"harness|hellaswag|10": {
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},
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},
"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.5890826383623957,
"acc_stderr": 0.01355213290142322
}
}
```
## 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] |
tyzhu/random25eof_find_passage_train5000000_eval1000_rare | ---
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: 1042263000
num_examples: 10001000
- name: validation
num_bytes: 118222
num_examples: 1000
download_size: 0
dataset_size: 1042381222
---
# Dataset Card for "random25eof_find_passage_train5000000_eval1000_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
michalby24/dataset_combined_4 | ---
dataset_info:
features:
- name: language
dtype: string
- name: input_values
sequence: float32
splits:
- name: train
num_bytes: 2023164070
num_examples: 31607
- name: test
num_bytes: 505807020
num_examples: 7902
download_size: 1287752556
dataset_size: 2528971090
---
# Dataset Card for "dataset_combined_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
stanfordnlp/sst2 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: sst
pretty_name: Stanford Sentiment Treebank v2
dataset_info:
features:
- name: idx
dtype: int32
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 4681603
num_examples: 67349
- name: validation
num_bytes: 106252
num_examples: 872
- name: test
num_bytes: 216640
num_examples: 1821
download_size: 3331058
dataset_size: 5004495
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://nlp.stanford.edu/sentiment/
- **Repository:**
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the
compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005)
and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and
includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.
Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive
with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.
### Supported Tasks and Leaderboards
- `sentiment-classification`
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
```
{'idx': 0,
'sentence': 'hide new secretions from the parental units ',
'label': 0}
```
### Data Fields
- `idx`: Monotonically increasing index ID.
- `sentence`: Complete sentence expressing an opinion about a film.
- `label`: Sentiment of the opinion, either "negative" (0) or positive (1). The test set labels are hidden (-1).
### Data Splits
| | train | validation | test |
|--------------------|---------:|-----------:|-----:|
| Number of examples | 67349 | 872 | 1821 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Rotten Tomatoes reviewers.
### 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
Unknown.
### Citation Information
```bibtex
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
sivan22/synth-HTR | ---
dataset_info:
features:
- name: image
dtype: image
- name: labels
dtype: string
splits:
- name: train
num_bytes: 2904123997.0
num_examples: 30000
download_size: 0
dataset_size: 2904123997.0
---
# Dataset Card for "synth-HTR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jonathan-roberts1/USTC_SmokeRS | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cloud
'1': dust
'2': haze
'3': land
'4': seaside
'5': smoke
splits:
- name: train
num_bytes: 1229029078.725
num_examples: 6225
download_size: 1115042620
dataset_size: 1229029078.725
license: other
---
# Dataset Card for "USTC_SmokeRS"
## Dataset Description
- **Paper:** [SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention](https://www.mdpi.com/2072-4292/11/14/1702/pdf)
### Licensing Information
For research/education purposes.
## Citation Information
[SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention](https://www.mdpi.com/2072-4292/11/14/1702/pdf)
```
@article{ba2019smokenet,
title = {SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention},
author = {Ba, Rui and Chen, Chen and Yuan, Jing and Song, Weiguo and Lo, Siuming},
year = 2019,
journal = {Remote Sensing},
publisher = {MDPI},
volume = 11,
number = 14,
pages = 1702
}
``` |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667119 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_v5
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_v5
dataset_config: mathemakitten--winobias_antistereotype_test_v5
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-6.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_v5
* Config: mathemakitten--winobias_antistereotype_test_v5
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
ksukrit/training_data_hands | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bad
'1': good
splits:
- name: train
num_bytes: 4654722697.352
num_examples: 3974
download_size: 0
dataset_size: 4654722697.352
---
# Dataset Card for "training_data_hands"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Azure99/blossom-math-v2 | ---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
# BLOSSOM MATH V2
### 介绍
[Blossom Math V3](https://huggingface.co/datasets/Azure99/blossom-math-v3)版本已发布！🤗
Blossom Math V2是基于Math23K和GSM8K衍生而来的中英双语数学对话数据集，适用于数学问题微调。
相比于blossom-math-v1，新增了2500条GSM8K数据和翻译为中文的2500条GSM8K-CN数据。此外，优化了答案的检查逻辑，还移除了<<1+1=2>>等计算步骤，以统一推理步骤的风格。
本数据集采用全量Math23K、GSM8K和翻译后的GSM8K的问题，随后调用gpt-3.5-turbo-0613生成结果，并使用原始数据集中的答案对生成的结果进行验证，过滤掉错误答案，很大程度上保证了问题和答案的准确性。
本次发布了全量数据的25%，包含10K记录。
### 语言
中文和英文
### 数据集结构
每条数据代表一个完整的题目及答案，包含id、input、output、answer、dataset四个字段。
- id：字符串，代表原始数据集中的题目id，与dataset字段结合可确定唯一题目。
- input：字符串，代表问题。
- output：字符串，代表gpt-3.5-turbo-0613生成的答案。
- answer：字符串，代表正确答案。
- dataset：字符串，代表原始数据集。
### 数据集限制
本数据集的所有响应均由gpt-3.5-turbo-0613生成，并经过初步校验，但仍可能包含不准确的回答。 |
zolak/twitter_dataset_79_1713219372 | ---
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: 162885
num_examples: 423
download_size: 90656
dataset_size: 162885
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BeIR/nfcorpus-generated-queries | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
result-kand2-sdxl-wuerst-karlo/a9adf6d9 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1395
dataset_size: 182
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a9adf6d9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/annand_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of annand (Fire Emblem)
This is the dataset of annand (Fire Emblem), containing 20 images and their tags.
The core tags of this character are `green_hair, long_hair, green_eyes, breasts`, 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 | 18.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annand_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 12.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annand_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 35 | 20.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annand_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 16.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annand_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 35 | 26.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annand_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/annand_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 | 20 |  |  |  |  |  | 1girl, solo, circlet, smile, breastplate, elbow_gloves, simple_background, thighhighs, white_background, belt, boots, closed_mouth, looking_at_viewer, white_dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | circlet | smile | breastplate | elbow_gloves | simple_background | thighhighs | white_background | belt | boots | closed_mouth | looking_at_viewer | white_dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:--------|:--------------|:---------------|:--------------------|:-------------|:-------------------|:-------|:--------|:---------------|:--------------------|:--------------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
OUTEIRAL2/VOZIA2 | ---
license: openrail
---
|
cg1177/fineaction_internvideo2_1b_w16_s4 | ---
license: apache-2.0
---
|
blenderwang/meruem-cell-1m | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: exprs
sequence: float32
- name: length
dtype: int32
- name: species
dtype:
class_label:
names:
'0': arabidopsis_thaliana
'1': danio_rerio
'2': drosophila_melanogaster
'3': homo_sapiens
'4': mus_musculus
'5': rattus_norvegicus
- name: dataset_id
dtype: int16
- name: cell_id
dtype: int32
splits:
- name: train
num_bytes: 14854205784
num_examples: 1000000
download_size: 6831816916
dataset_size: 14854205784
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- biology
size_categories:
- 1M<n<10M
---
# Meruem Cell 10m dataset's subset
A single cell RNA expression dataset
## Source
- Original data is from [EMBL](https://www.ebi.ac.uk/gxa/sc/home)
- Only species that have atleast 100k cells are included
- Only protein coding genes are included
- The dataset ids can be mapped to dataset names in `./dataset_names.txt`
- The input_ids is follow the proteins listed in `./gene_map_cleaned.tsv`
- cell ids are their original indices in the dataset
## Note
- This dataset is for development only
- Full set will be uploaded when I make sure my model can fit on this subset |
The13thDrifter/Cayde-6_DATASET | ---
license: cc-by-3.0
---
|
AdapterOcean/data-standardized_cluster_4 | ---
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: 46206021
num_examples: 4509
download_size: 13041751
dataset_size: 46206021
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/iris_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of iris (Pokémon)
This is the dataset of iris (Pokémon), containing 500 images and their tags.
The core tags of this character are `dark-skinned_female, dark_skin, long_hair, purple_hair, bangs, big_hair, brown_eyes, very_long_hair, two_side_up, breasts, eyelashes`, 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 | 426.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 272.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 997 | 509.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 387.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 997 | 679.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_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/iris_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 | 12 |  |  |  |  |  | 1girl, solo, open_mouth, waist_bow, dress, smile, blush, crown |
| 1 | 16 |  |  |  |  |  | 1girl, :d, open_mouth, tongue, dress, hair_rings, long_sleeves, tiara, upper_teeth_only, wide_sleeves, looking_at_viewer, bow, blush, sandals, solo, pokemon_(creature), red_eyes, toes, white_footwear, collarbone, spread_fingers |
| 2 | 6 |  |  |  |  |  | 1girl, :d, armlet, black_dress, fake_horns, official_alternate_costume, open_mouth, tongue, twintails, upper_teeth_only, wrist_cuffs, bare_shoulders, black_hairband, claw_pose, hair_rings, hands_up, looking_at_viewer, red_eyes, sleeveless_dress, solo, blush, fake_wings, halloween |
| 3 | 6 |  |  |  |  |  | 1girl, :d, collarbone, fake_horns, fangs, hair_rings, official_alternate_costume, open_mouth, tongue, black_hairband, twintails, wrist_cuffs, armlet, bare_shoulders, black_dress, blush, looking_at_viewer, solo, wings, hands_up, upper_teeth_only |
| 4 | 22 |  |  |  |  |  | 1girl, nipples, nude, blush, solo, open_mouth, collarbone, looking_at_viewer, navel, pussy, small_breasts, tongue, :d, barefoot, light_areolae, shiny_skin, censored, simple_background, white_background |
| 5 | 11 |  |  |  |  |  | 1girl, hetero, nipples, nude, blush, 1boy, sex, small_breasts, vaginal, penis, pussy, solo_focus, pokemon_(creature), red_eyes, uncensored, bestiality, navel, spread_legs, open_mouth, pokephilia, :q, closed_mouth, collarbone, hair_tie, loli, looking_down, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | open_mouth | waist_bow | dress | smile | blush | crown | :d | tongue | hair_rings | long_sleeves | tiara | upper_teeth_only | wide_sleeves | looking_at_viewer | bow | sandals | pokemon_(creature) | red_eyes | toes | white_footwear | collarbone | spread_fingers | armlet | black_dress | fake_horns | official_alternate_costume | twintails | wrist_cuffs | bare_shoulders | black_hairband | claw_pose | hands_up | sleeveless_dress | fake_wings | halloween | fangs | wings | nipples | nude | navel | pussy | small_breasts | barefoot | light_areolae | shiny_skin | censored | simple_background | white_background | hetero | 1boy | sex | vaginal | penis | solo_focus | uncensored | bestiality | spread_legs | pokephilia | :q | closed_mouth | hair_tie | loli | looking_down |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:------------|:--------|:--------|:--------|:--------|:-----|:---------|:-------------|:---------------|:--------|:-------------------|:---------------|:--------------------|:------|:----------|:---------------------|:-----------|:-------|:-----------------|:-------------|:-----------------|:---------|:--------------|:-------------|:-----------------------------|:------------|:--------------|:-----------------|:-----------------|:------------|:-----------|:-------------------|:-------------|:------------|:--------|:--------|:----------|:-------|:--------|:--------|:----------------|:-----------|:----------------|:-------------|:-----------|:--------------------|:-------------------|:---------|:-------|:------|:----------|:--------|:-------------|:-------------|:-------------|:--------------|:-------------|:-----|:---------------|:-----------|:-------|:---------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 16 |  |  |  |  |  | X | X | X | | X | | X | | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | | | | X | | X | X | X | | | X | | X | | | | | | | X | | X | X | X | X | X | X | X | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 22 |  |  |  |  |  | X | X | X | | | | X | | X | X | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | 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 |
|
arthurmluz/GPTextSum2_data-xlsum_temario_results | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: summary
dtype: string
- name: gen_summary
dtype: string
- name: rouge
struct:
- name: rouge1
dtype: float64
- name: rouge2
dtype: float64
- name: rougeL
dtype: float64
- name: rougeLsum
dtype: float64
- name: bert
struct:
- name: f1
sequence: float64
- name: hashcode
dtype: string
- name: precision
sequence: float64
- name: recall
sequence: float64
- name: moverScore
dtype: float64
splits:
- name: validation
num_bytes: 92617
num_examples: 20
download_size: 93095
dataset_size: 92617
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "gptextsum2_data-xlsum_temario_results"
rouge= {'rouge1': 0.34663019021874986, 'rouge2': 0.14819749362220133, 'rougeL': 0.21196170584218882, 'rougeLsum': 0.21196170584218882}
bert= {'precision': 0.7504127502441407, 'recall': 0.6941693127155304, 'f1': 0.720111683011055}
mover = 0.5858268677961962 |
MottsCoding/MeltpoolsLabeled | ---
dataset_info:
features:
- name: images
dtype: image
- name: labels
sequence:
sequence: int32
splits:
- name: train
num_bytes: 51624539.0
num_examples: 12
download_size: 15662464
dataset_size: 51624539.0
---
# Dataset Card for "MeltpoolsLabeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jordanfungtc/cnntorsion | ---
size_categories:
- 10K<n<100K
--- |
adalbertojunior/ICD_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
sequence: string
splits:
- name: train
num_bytes: 418410601
num_examples: 39354
- name: test
num_bytes: 53529100
num_examples: 5000
- name: validation
num_bytes: 52947510
num_examples: 5000
download_size: 301971173
dataset_size: 524887211
---
# Dataset Card for "ICD_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_after_perfect | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 239959
num_examples: 1035
- name: dev_mismatched
num_bytes: 273238
num_examples: 1082
- name: test_matched
num_bytes: 262731
num_examples: 1038
- name: test_mismatched
num_bytes: 277346
num_examples: 1143
- name: train
num_bytes: 10108342
num_examples: 41417
download_size: 6773376
dataset_size: 11161616
---
# Dataset Card for "MULTI_VALUE_mnli_after_perfect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Hemg/Emotion-audio-Dataset | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Angry
'1': Disgusted
'2': Fearful
'3': Happy
'4': Neutral
'5': Sad
'6': Suprised
splits:
- name: train
num_bytes: 2836512748.06
num_examples: 12798
download_size: 1577902101
dataset_size: 2836512748.06
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liaad/translation_sample | ---
dataset_info:
- config_name: ai2_arc
features:
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: choices
sequence: string
- name: choices_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 713
num_examples: 1
download_size: 7660
dataset_size: 713
- config_name: boolq
features:
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: passage
dtype: string
- name: passage_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 1338
num_examples: 1
download_size: 13729
dataset_size: 1338
- config_name: gsm8k
features:
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answer
dtype: string
- name: answer_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 2249
num_examples: 1
download_size: 19759
dataset_size: 2249
- config_name: hellaswag
features:
- name: activity_label
dtype: string
- name: activity_label_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: ctx
dtype: string
- name: ctx_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: endings
sequence: string
- name: endings_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 3111
num_examples: 1
download_size: 17613
dataset_size: 3111
- config_name: mbpp
features:
- name: text
dtype: string
- name: text_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 358
num_examples: 1
download_size: 4822
dataset_size: 358
- config_name: natural_questions_parsed
features:
- name: document
dtype: string
- name: document_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: candidates
sequence: string
- name: candidates_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: long_answer
dtype: string
- name: long_answer_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 5399
num_examples: 1
download_size: 38881
dataset_size: 5399
- config_name: openbookqa
features:
- name: question_stem
dtype: string
- name: question_stem_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: choices
sequence: string
- name: choices_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: fact1
dtype: string
- name: fact1_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 776
num_examples: 1
download_size: 10475
dataset_size: 776
- config_name: quac
features:
- name: background
dtype: string
- name: background_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: context
dtype: string
- name: context_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: questions
sequence: string
- name: questions_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: orig_answers
sequence: string
- name: orig_answers_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 11166
num_examples: 1
download_size: 76251
dataset_size: 11166
- config_name: social_i_qa
features:
- name: context
dtype: string
- name: context_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answerA
dtype: string
- name: answerA_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answerB
dtype: string
- name: answerB_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answerC
dtype: string
- name: answerC_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 677
num_examples: 1
download_size: 15127
dataset_size: 677
- config_name: squad_v1_pt
features:
- name: context
dtype: string
- name: context_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
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- name: libre_translation
dtype: string
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answers
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- name: answers_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 1587
num_examples: 1
download_size: 17739
dataset_size: 1587
- config_name: trivia_qa
features:
- name: question
dtype: string
- name: question_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
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- name: libre_translation
dtype: string
- name: search_results_search_context
sequence: string
- name: search_results_search_context_translated
list:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: answer_value
dtype: string
- name: answer_value_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 1154
num_examples: 1
download_size: 15177
dataset_size: 1154
- config_name: winogrande
features:
- name: sentence
dtype: string
- name: sentence_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: option1
dtype: string
- name: option1_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
- name: option2
dtype: string
- name: option2_translated
struct:
- name: Helsinki-NLP/opus-mt-tc-big-en-pt
dtype: string
- name: google_translation
dtype: string
- name: libre_translation
dtype: string
splits:
- name: test
num_bytes: 677
num_examples: 1
download_size: 11112
dataset_size: 677
configs:
- config_name: ai2_arc
data_files:
- split: test
path: ai2_arc/test-*
- config_name: boolq
data_files:
- split: test
path: boolq/test-*
- config_name: gsm8k
data_files:
- split: test
path: gsm8k/test-*
- config_name: hellaswag
data_files:
- split: test
path: hellaswag/test-*
- config_name: mbpp
data_files:
- split: test
path: mbpp/test-*
- config_name: natural_questions_parsed
data_files:
- split: test
path: natural_questions_parsed/test-*
- config_name: openbookqa
data_files:
- split: test
path: openbookqa/test-*
- config_name: quac
data_files:
- split: test
path: quac/test-*
- config_name: social_i_qa
data_files:
- split: test
path: social_i_qa/test-*
- config_name: squad_v1_pt
data_files:
- split: test
path: squad_v1_pt/test-*
- config_name: trivia_qa
data_files:
- split: test
path: trivia_qa/test-*
- config_name: winogrande
data_files:
- split: test
path: winogrande/test-*
---
|
w11wo/imdb-javanese | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- jv
license:
- odbl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
extended:
- original
---
# Dataset Card for "imdb-javanese"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits Sample Size](#data-instances-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Repository:** [Github](https://github.com/w11wo/nlp-datasets#javanese-imdb)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Point of Contact:** [Wilson Wongso](https://github.com/w11wo)
- **Size of downloaded dataset files:** 17.0 MB
- **Size of the generated dataset:** 47.5 MB
- **Total amount of disk used:** 64.5 MB
### Dataset Summary
Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well. We translated the [original IMDB Dataset](https://huggingface.co/datasets/imdb) to Javanese using the multi-lingual MarianMT Transformer model from [`Helsinki-NLP/opus-mt-en-mul`](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
An example of `javanese_imdb_train.csv` looks as follows.
| label | text |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1 | "Drama romantik sing digawé karo direktur Martin Ritt kuwi ora dingertèni, nanging ana momen-momen sing marahi karisma lintang Jane Fonda lan Robert De Niro (kelompok sing luar biasa). Dhèwèké dadi randha sing ora isa mlaku, iso anu anyar lan anyar-inventor-- kowé isa nganggep isiné. Adapsi novel Pat Barker ""Union Street"" (yak titel sing apik!) arep dinggo-back-back it on bland, lan pendidikan film kuwi gampang, nanging isih nyenengké; a rosy-hued-inventor-fantasi. Ora ana sing ngganggu gambar sing sejati ding kok iso dinggo nggawe gambar sing paling nyeneng." |
| 0 | "Pengalaman wong lanang sing nduwé perasaan sing ora lumrah kanggo babi. Mulai nganggo tuladha sing luar biasa yaiku komedia. Wong orkestra termel digawé dadi wong gila, sing kasar merga nyanyian nyanyi. Sayangé, kuwi tetep absurd wektu WHOLE tanpa ceramah umum sing mung digawé. Malah, sing ana ing jaman kuwi kudu ditinggalké. Diyalog kryptik sing nggawé Shakespeare marah gampang kanggo kelas telu. Pak teknis kuwi luwih apik timbang kowe mikir nganggo cinematografi sing apik sing jenengé Vilmos Zsmond. Masa depan bintang Saly Kirkland lan Frederic Forrest isa ndelok." |
### Data Fields
- `text`: The movie review translated into Javanese.
- `label`: The sentiment exhibited in the review, either `1` (positive) or `0` (negative).
### Data Splits Sample Size
| train | unsupervised | test |
| ----: | -----------: | ----: |
| 25000 | 50000 | 25000 |
## 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
If you use this dataset in your research, please cite:
```
@inproceedings{wongso2021causal,
title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures},
author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin},
booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={1--7},
year={2021},
organization={IEEE}
}
```
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
|
Tngarg/hindi_test | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: text
dtype: string
- name: sentiment
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 81183
num_examples: 554
download_size: 53504
dataset_size: 81183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hindi_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_162 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1154530644.0
num_examples: 224967
download_size: 1183013324
dataset_size: 1154530644.0
---
# Dataset Card for "chunk_162"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bkueper/test | ---
language:
- de
--- |
davanstrien/test_imdb_embedd2 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: imdb
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: imdb-movie-reviews
pretty_name: IMDB
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
0: neg
1: pos
config_name: plain_text
splits:
- name: train
num_bytes: 33432835
num_examples: 25000
- name: test
num_bytes: 32650697
num_examples: 25000
- name: unsupervised
num_bytes: 67106814
num_examples: 50000
download_size: 84125825
dataset_size: 133190346
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
- name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "test_imdb_embedd2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibranze/araproje_arc_tr_s3 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: validation
num_bytes: 86423.0
num_examples: 250
download_size: 46973
dataset_size: 86423.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_arc_tr_s3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SEACrowd/id_panl_bppt | ---
tags:
- machine-translation
language:
- ind
---
# id_panl_bppt
Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains about 24K sentences in English and Bahasa Indonesia from 4 different topics
(Economy, International Affairs, Science & Technology, and Sports).
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{id_panl_bppt,
author = {PAN Localization - BPPT},
title = {Parallel Text Corpora, English Indonesian},
year = {2009},
url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf},
}
```
## Homepage
[http://digilib.bppt.go.id/sampul/p92-budiono.pdf](http://digilib.bppt.go.id/sampul/p92-budiono.pdf)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
ruanchaves/reli-sa_por_Latn_to_glg_Latn | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: book
dtype: string
- name: review_id
dtype: string
- name: score
dtype: float64
- name: sentence_id
dtype: int64
- name: unique_review_id
dtype: string
- name: sentence
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1776947
num_examples: 7875
- name: validation
num_bytes: 313722
num_examples: 1348
- name: test
num_bytes: 652065
num_examples: 3288
download_size: 0
dataset_size: 2742734
---
# Dataset Card for "reli-sa_por_Latn_to_glg_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sonrin/Thorneworks | ---
license: artistic-2.0
---
|
youngwoo3283/summarize_llama_20k | ---
size_categories:
- 10K<n<100K
--- |
Kasuzu/522 | ---
license: unknown
---
|
itisarainyday/phys_gre_question | ---
dataset_info:
features:
- name: '0'
dtype: string
splits:
- name: train
num_bytes: 466194
num_examples: 395
- name: validation
num_bytes: 5448
num_examples: 5
download_size: 135395
dataset_size: 471642
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
augsaksham/small_train | ---
dataset_info:
features:
- name: PII
dtype: string
- name: TOOL
dtype: string
- name: full_text
dtype: string
- name: document
dtype: int64
- name: is_valid
dtype: bool
splits:
- name: train
num_bytes: 36712
num_examples: 9
- name: validation
num_bytes: 6082
num_examples: 1
download_size: 42287
dataset_size: 42794
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
heliosprime/twitter_dataset_1713017877 | ---
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: 10831
num_examples: 25
download_size: 9459
dataset_size: 10831
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713017877"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mpazdzioch/oasst1_pl | ---
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int64
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
struct:
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: obscene
dtype: float64
- name: severe_toxicity
dtype: float64
- name: sexual_explicit
dtype: float64
- name: threat
dtype: float64
- name: toxicity
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int64
- name: name
sequence: string
- name: value
sequence: float64
splits:
- name: validation
num_bytes: 1081868
num_examples: 1481
- name: train
num_bytes: 65002941
num_examples: 77374
download_size: 19411941
dataset_size: 66084809
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
---
|
Maciel/FinCUGE-Instruction | ---
license: apache-2.0
dataset_info:
features:
- name: task
dtype: string
- name: desc
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 62215078
num_examples: 123137
- name: eval
num_bytes: 7548859
num_examples: 15167
download_size: 32078572
dataset_size: 69763937
task_categories:
- question-answering
language:
- zh
tags:
- finance
pretty_name: s
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
## Dataset Description
本数据集包含八项中文金融自然语言处理基准任务，分别为金融新闻摘要(FinNA)、金融新闻公告事件问答(FinQA)、金融新闻分类(FinNL)、金融新闻关系抽取(FinRE)、金融社交媒体文本情绪分类(FinNE)、金融负面消息及其主体判定(FinNSP)、金融因果事件抽取(FinCQA)、金融事件主体抽取(FinESE)。
## Dataset Structure
（1）FinNA
金融新闻摘要数据集。输入一段金融新闻，需要模型生成一句话摘要。其中训练集包含24000条数据，验证集包含3000条数据。
```json
{
"instruction": "根据以下新闻生成摘要。",
"input": "天宇股份公告，预计2021年半年度归属于上公司股东的净利润1.7亿元-2.3亿元，同比下降39.68%-55.41%。公司主营产品沙坦类原料药受低端市场激烈竞争影响，原料药销售价格较去年同期下降；子公司山东昌邑一期项目和京圣药业生产基地建设完成，进入试生产阶段和达产阶段，产能利用率没有完全释放，生产成本阶段性较高等原因导致报告期毛利率较上年同期下降。",
"output": "天宇股份：半年度净利润预降40%-55%"
}
```
（2）FinQA
金融新闻公告事件问答数据集。由DuEE-fin数据集转化得到。输入一段金融新闻或公告，和一个与文本中发生的事件有关的问题，需要模型根据文本生成问题答案。问题的范围包含文本中包含的事件类型，以及某个事件对应的发生时间和人物等要素；答案为问题对应的文本中的事件类型或事件要素的列表。其中训练集包含16000条数据，验证集包含2000条数据。
```json
{
"instruction": "新城悦服务股份回购事件对应的每股交易价格是什么？原标题：新城悦“自救”：1064万港元回购公司190万股股份 来源：新浪乐居 \
7月8日，新城悦服务（01755.hk）发布公告称，公司于今日回购190万股普通股票，占据现有已发行股份的0.23171%。回购股份每股付出价格区间为5.30港元至5.83港元，付出总额为1064万港元。 \
值得注意的是，新城控股(28.500,1.52,5.63%)董事长涉嫌猥亵儿童被刑拘事件发生后第四个交易日（7月8日），新城悦服务股价开始回升，收涨12.20%。 \
据悉，新城控股董事长涉嫌猥亵儿童被刑拘事件发生第三个交易日（7月5日），新城系港股上市房企市值共蒸发约256亿港元。截至7月5日收盘，新城发展（01030.HK）收于6.71港元\/股，市值自事件发生后减少227.11亿港元；新城悦（01755.HK）收于5.08港元\/股，市值自事件发生后减少28.86亿港元。",
"input": "",
"output": "5.30港元至5.83港元"
}
```
（3）FinNL
金融新闻分类数据集。对于给出的金融新闻，需要模型将其多标签分类到可能的十五种类别，类别包括公司、行业、大盘、国际、经济、政策、政治、期货、债券、房地产、外汇、虚拟货币、新冠、能源和其它。其中训练集包含8000条数据，验证集包含1000条数据。
```json
{
"instruction": "新城悦服务股份回购事件对应的每股交易价格是什么？原标题：新城悦“自救”：1064万港元回购公司190万股股份 来源：新浪乐居 \
7月8日，新城悦服务（01755.hk）发布公告称，公司于今日回购190万股普通股票，占据现有已发行股份的0.23171%。回购股份每股付出价格区间为5.30港元至5.83港元，付出总额为1064万港元。 \
值得注意的是，新城控股(28.500,1.52,5.63%)董事长涉嫌猥亵儿童被刑拘事件发生后第四个交易日（7月8日），新城悦服务股价开始回升，收涨12.20%。 \
据悉，新城控股董事长涉嫌猥亵儿童被刑拘事件发生第三个交易日（7月5日），新城系港股上市房企市值共蒸发约256亿港元。截至7月5日收盘，新城发展（01030.HK）收于6.71港元\/股，市值自事件发生后减少227.11亿港元；新城悦（01755.HK）收于5.08港元\/股，市值自事件发生后减少28.86亿港元。",
"input": "",
"output": "5.30港元至5.83港元"
}
```
（4）FinRE
金融新闻关系抽取数据集。对于给出的金融新闻和头实体-尾实体对，需要模型分类实体对的关系到包含空关系的44种关系类别，包含拥有、持股、竞争、收购、交易、合作、减持等财经金融领域的特有关系类别。其中训练集包含7454条数据，验证集包含1489条数据。
```json
{
"instruction": "根据以下文本，描述以下两个实体东方航空和上航之间的关系。",
"input": "东方航空AH股临时停牌传将与上航合并",
"output": "合并"
}
```
（5）FinFE
金融社交媒体文本情绪分类数据集。对于给出的金融社交媒体文本，需要模型分类该文本的情绪为消极-中性-积极三种类别。其中训练集包含8000条数据，验证集包含1000条数据。
```json
{
"instruction": "这个文本的情感倾向是积极、消极还是中性的。",
"input": "补仓补的心力憔悴",
"output": "该文本情感是消极的。"
}
```
（6）FinNSP
金融负面消息及其主体判定数据集。对于给出的金融新闻或社交媒体文本及其中包含的实体，需要模型判断该文本中是否包含有针对某个实体的负面消息，并指出负面消息的主体是哪个实体。其中训练集包含4800条数据，验证集包含600条数据。
```json
{
"instruction": "识别以下内容中的负面金融实体信息。",
"input": "11月5日，警方对众车在线涉嫌非法吸收公众存款案立案侦查",
"output": "文中包含的负面主体：众车在线。"
}
```
（7）FinCQA
金融因果事件抽取数据集。因果事件抽取专注于在文本中识别出具有因果关系的两个事件及其事件参数，并将其整理为机构化数据。该数据集的因果事件数据集包含对大宗商品领域的因果事件识别，识别的事件类型包括台风/地震，供给增加/减少，需求增加/减少，价格上升/下降等可能为原因和结果事件及其对应关系和对应的产品、地区等参数 。其中训练集包含21965条数据，验证集包含2741条数据。
```json
{
"instruction": "下文中有哪些因果事件？铁矿：中长期，今年铁矿供需格局明显改善，巴西矿难及飓风对发运的影响，导致铁矿石全年供应走低",
"input": "",
"output": "台风导致供给减少"
}
```
（8）FinESE
金融事件主体抽取数据集。从真实的新闻语料中，抽取特定事件类型的主体。其中训练集包含11752条数据，验证集包含1464条数据。
```json
{
"instruction": "分析以下新闻，抽取资金账户风险事件相关的主体信息。",
"input": "金一文化违规减持仅””罚酒三杯””未来减持或””仍不手软””雅虎承认发生大规模数据泄露 2亿账户信息被盗科远股份(002380)股东减持202万股套现5989万",
"output": "所属资金账户风险事件的金融主体是雅虎。"
}
``` |
Hazqeel/ms-patriots | ---
language:
- ms
---
thepatriots(dot)asia data scraped on 8/7/2023 |
fffilo/genre-classifier-2 | ---
dataset_info:
features:
- name: example
dtype: string
splits:
- name: train
num_bytes: 130697
num_examples: 118
- name: test
num_bytes: 5589
num_examples: 5
download_size: 32844
dataset_size: 136286
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
cboettig/biodiversity | ---
license: pddl
---
# Public Biodiversity Data
A collection of biodiversity-related datasets in the public domain.
Data objects are copied here to make more easily available over virtual filesystem protocols. Some datasets are also translated into cloud-optimized formats.
|
lhoestq/small-publaynet-wds | ---
tags:
- webdataset
---
# Small PubLayNet (WebDataset)
This dataset consists in the first WebDataset shards of PubLayNet from http://storage.googleapis.com/nvdata-publaynet
It is mostly used to test the WebDataset integration within the Hugging Face ecosystem. |
tyzhu/flan_max_300 | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 2253528229.0133214
num_examples: 1321267
- name: test
num_bytes: 118607826.10465212
num_examples: 69541
- name: validation
num_bytes: 118607826.10465212
num_examples: 69541
download_size: 34774605
dataset_size: 2490743881.2226253
---
# Dataset Card for "flan_max_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/watanabe_you_lovelivesunshine | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of watanabe_you/渡辺曜/와타나베요 (Love Live! Sunshine!!)
This is the dataset of watanabe_you/渡辺曜/와타나베요 (Love Live! Sunshine!!), containing 500 images and their tags.
The core tags of this character are `blue_eyes, short_hair, brown_hair, grey_hair, bangs, breasts, medium_breasts`, 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 | 660.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/watanabe_you_lovelivesunshine/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 375.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/watanabe_you_lovelivesunshine/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1206 | 811.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/watanabe_you_lovelivesunshine/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 585.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/watanabe_you_lovelivesunshine/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1206 | 1.12 GiB | [Download](https://huggingface.co/datasets/CyberHarem/watanabe_you_lovelivesunshine/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/watanabe_you_lovelivesunshine',
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, blush, grey_skirt, pleated_skirt, serafuku, smile, solo, uranohoshi_school_uniform, long_sleeves, looking_at_viewer, red_bowtie, simple_background, white_background, buttons, salute, grey_sailor_collar, miniskirt, shirt, collarbone, cowboy_shot |
| 1 | 27 |  |  |  |  |  | 1girl, solo, earrings, looking_at_viewer, midriff, navel, tiara, smile, skirt, birthday, fish, detached_sleeves, thighhighs, blush, bubble, underwater |
| 2 | 13 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, open_mouth, smile, dress, hair_flower, white_background, detached_sleeves, earrings, simple_background, bow, tiara |
| 3 | 14 |  |  |  |  |  | 1girl, solo, competition_swimsuit, looking_at_viewer, blue_one-piece_swimsuit, blush, collarbone, smile, covered_navel, wet, water, highleg_swimsuit, poolside, bare_shoulders, open_mouth |
| 4 | 7 |  |  |  |  |  | 1girl, floral_print, obi, smile, solo, looking_at_viewer, blush, hair_flower, open_mouth, upper_body, alternate_hairstyle, wide_sleeves, yukata |
| 5 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, outdoors, smile, solo, blue_sky, cleavage, day, navel, cloud, ocean, blush, collarbone, earrings, blue_bikini, bracelet, open_mouth, salute, skirt, striped_bikini, x_hair_ornament, rainbow |
| 6 | 5 |  |  |  |  |  | 1girl, beret, blush, cleavage, collarbone, looking_at_viewer, necklace, open_mouth, sailor_collar, sailor_hat, short_sleeves, solo, white_headwear, :d, navel, white_skirt, wrist_cuffs, bikini_top_only, crop_top, midriff, miniskirt, pleated_skirt, simple_background, stomach, teeth, white_background, white_thighhighs, blue_bikini, bow, frilled_skirt, from_above, open_clothes, pendant, polka_dot_bikini, swept_bangs, water_drop, zettai_ryouiki |
| 7 | 7 |  |  |  |  |  | 1girl, solo, bag, looking_at_viewer, straw_hat, open_mouth, sun_hat, anchor_necklace, outdoors, shorts, vertical-striped_dress, :d, bare_shoulders, black_ribbon, blush, collarbone, day, ocean, sky, sleeveless, wrist_ribbon |
| 8 | 8 |  |  |  |  |  | 1girl, hat, looking_at_viewer, solo, simple_background, white_background, short_shorts, thighhighs, blush, long_sleeves, salute, thigh_strap, white_shorts, detached_sleeves, epaulettes, grin, one_eye_closed, open_mouth, waist_cape, white_headwear, ;d, blue_ribbon, gun, holding_weapon, jewelry, necktie, white_footwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | grey_skirt | pleated_skirt | serafuku | smile | solo | uranohoshi_school_uniform | long_sleeves | looking_at_viewer | red_bowtie | simple_background | white_background | buttons | salute | grey_sailor_collar | miniskirt | shirt | collarbone | cowboy_shot | earrings | midriff | navel | tiara | skirt | birthday | fish | detached_sleeves | thighhighs | bubble | underwater | open_mouth | dress | hair_flower | bow | competition_swimsuit | blue_one-piece_swimsuit | covered_navel | wet | water | highleg_swimsuit | poolside | bare_shoulders | floral_print | obi | upper_body | alternate_hairstyle | wide_sleeves | yukata | outdoors | blue_sky | cleavage | day | cloud | ocean | blue_bikini | bracelet | striped_bikini | x_hair_ornament | rainbow | beret | necklace | sailor_collar | sailor_hat | short_sleeves | white_headwear | :d | white_skirt | wrist_cuffs | bikini_top_only | crop_top | stomach | teeth | white_thighhighs | frilled_skirt | from_above | open_clothes | pendant | polka_dot_bikini | swept_bangs | water_drop | zettai_ryouiki | bag | straw_hat | sun_hat | anchor_necklace | shorts | vertical-striped_dress | black_ribbon | sky | sleeveless | wrist_ribbon | hat | short_shorts | thigh_strap | white_shorts | epaulettes | grin | one_eye_closed | waist_cape | ;d | blue_ribbon | gun | holding_weapon | jewelry | necktie | white_footwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:----------------|:-----------|:--------|:-------|:----------------------------|:---------------|:--------------------|:-------------|:--------------------|:-------------------|:----------|:---------|:---------------------|:------------|:--------|:-------------|:--------------|:-----------|:----------|:--------|:--------|:--------|:-----------|:-------|:-------------------|:-------------|:---------|:-------------|:-------------|:--------|:--------------|:------|:-----------------------|:--------------------------|:----------------|:------|:--------|:-------------------|:-----------|:-----------------|:---------------|:------|:-------------|:----------------------|:---------------|:---------|:-----------|:-----------|:-----------|:------|:--------|:--------|:--------------|:-----------|:-----------------|:------------------|:----------|:--------|:-----------|:----------------|:-------------|:----------------|:-----------------|:-----|:--------------|:--------------|:------------------|:-----------|:----------|:--------|:-------------------|:----------------|:-------------|:---------------|:----------|:-------------------|:--------------|:-------------|:-----------------|:------|:------------|:----------|:------------------|:---------|:-------------------------|:---------------|:------|:-------------|:---------------|:------|:---------------|:--------------|:---------------|:-------------|:-------|:-----------------|:-------------|:-----|:--------------|:------|:-----------------|:----------|:----------|:-----------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 27 |  |  |  |  |  | X | X | | | | X | X | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | X | | | | X | X | | | X | | X | X | | | | | | | | X | | | X | | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | 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 | 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | | | | | 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 | X | X | X | X | X | X | X |
|
sent_comp | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: sentence-compression
pretty_name: Google Sentence Compression
tags:
- sentence-compression
dataset_info:
features:
- name: graph
struct:
- name: id
dtype: string
- name: sentence
dtype: string
- name: node
sequence:
- name: form
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: word
sequence:
- name: id
dtype: int32
- name: form
dtype: string
- name: stem
dtype: string
- name: tag
dtype: string
- name: gender
dtype: int32
- name: head_word_index
dtype: int32
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: label
dtype: string
- name: entity_mention
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: head
dtype: int32
- name: name
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: is_proper_name_entity
dtype: bool
- name: gender
dtype: int32
- name: compression
struct:
- name: text
dtype: string
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: headline
dtype: string
- name: compression_ratio
dtype: float32
- name: doc_id
dtype: string
- name: source_tree
struct:
- name: id
dtype: string
- name: sentence
dtype: string
- name: node
sequence:
- name: form
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: word
sequence:
- name: id
dtype: int32
- name: form
dtype: string
- name: stem
dtype: string
- name: tag
dtype: string
- name: gender
dtype: int32
- name: head_word_index
dtype: int32
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: label
dtype: string
- name: entity_mention
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: head
dtype: int32
- name: name
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: is_proper_name_entity
dtype: bool
- name: gender
dtype: int32
- name: compression_untransformed
struct:
- name: text
dtype: string
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
splits:
- name: validation
num_bytes: 55823979
num_examples: 10000
- name: train
num_bytes: 1135684803
num_examples: 200000
download_size: 259652560
dataset_size: 1191508782
---
# Dataset Card for Google Sentence Compression
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Repository:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Paper:** [https://www.aclweb.org/anthology/D13-1155/](https://www.aclweb.org/anthology/D13-1155/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence supervised systems which require a structural alignment between the input and output can be successfully trained. We also extend an existing unsupervised compression method with a learning module. The new system uses structured prediction to learn from lexical, syntactic and other features. An evaluation with human raters shows that the presented data harvesting method indeed produces a parallel corpus of high quality. Also, the supervised system trained on this corpus gets high scores both from human raters and in an automatic evaluation setting, significantly outperforming a strong baseline.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each data instance should contains the information about the original sentence in `instance["graph"]["sentence"]` as well as the compressed sentence in `instance["compression"]["text"]`. As this dataset was created by pruning dependency connections, the author also includes the dependency tree and transformed graph of the original sentence and compressed sentence.
### Data Fields
Each instance should contains these information:
- `graph` (`Dict`): the transformation graph/tree for extracting compression (a modified version of a dependency tree).
- This will have features similar to a dependency tree (listed bellow)
- `compression` (`Dict`)
- `text` (`str`)
- `edge` (`List`)
- `headline` (`str`): the headline of the original news page.
- `compression_ratio` (`float`): the ratio between compressed sentence vs original sentence.
- `doc_id` (`str`): url of the original news page.
- `source_tree` (`Dict`): the original dependency tree (features listed bellow).
- `compression_untransformed` (`Dict`)
- `text` (`str`)
- `edge` (`List`)
Dependency tree features:
- `id` (`str`)
- `sentence` (`str`)
- `node` (`List`): list of nodes, each node represent a word/word phrase in the tree.
- `form` (`string`)
- `type` (`string`): the enity type of a node. Defaults to `""` if it's not an entity.
- `mid` (`string`)
- `word` (`List`): list of words the node contains.
- `id` (`int`)
- `form` (`str`): the word from the sentence.
- `stem` (`str`): the stemmed/lemmatized version of the word.
- `tag` (`str`): dependency tag of the word.
- `gender` (`int`)
- `head_word_index` (`int`)
- `edge`: list of the dependency connections between words.
- `parent_id` (`int`)
- `child_id` (`int`)
- `label` (`str`)
- `entity_mention` list of the entities in the sentence.
- `start` (`int`)
- `end` (`int`)
- `head` (`str`)
- `name` (`str`)
- `type` (`str`)
- `mid` (`str`)
- `is_proper_name_entity` (`bool`)
- `gender` (`int`)
### 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
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. |
PleIAs/Ukrainian-CulturalHeritage-Books | ---
task_categories:
- text-generation
language:
- uk
tags:
- ocr
pretty_name: Ukrainian-Public Domain-Books
---
# 🇺🇦 Ukrainian-Cultural Heritage-Books 🇺🇦
**Ukrainian-Cultural Heritage-Books** or **Ukrainian-CulturalHeritage-Books** is a collection of Ukrainian cultural heritage books and periodicals, most of them being in the public domain.
## Dataset summary
The collection has been compiled by Pierre-Carl Langlais from 19,574 digitized files hosted on Internet Archive (462M words) and will be expanded to other cultural heritage sources.
## Curation method
The composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years. Additionally, the initial consolidation of public domain status for cultural heritage operates in the EU under the 2019 Copyright Directive (art. 14).
As of March 2024, to limit rights verification, we have retained exclusively titles published prior to 1884.
The corpus will be expanded at a later stage to encompass late 19th century and early 20th century publications, after checking for public domain validity.
## Uses
The collection aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.
The rationales for creation of this collection are multifold:
* **Scientific**: We observe that the closure of training corpora represents a major barrier to AI research. Large language models face a real crisis of reproducibility.
* **Legal**: With the adoption of the AI Act with its obligations in terms of copyright law compliance for the pretraining corpora, the European AI ecosystem will have to change its provenance practices.
* **Cultural**: The linguistic diversity of the European Union is currently underrepresented. Unlike web archives, open, heritage, administrative, or scientific texts are often of high quality: they are long, multilingual, and editorialized publications.
* **Economical**: Today, value capture is concentrated on players whose financial resources are already considerable, allowing them to collect or purchase data at a high price. Making a royalty-free corpus available to as many people as possible frees innovation in uses and minimizes economic dependencies on dominant actors.
## License
The entire collection is in the public domain in all regions. This means that the patrimonial rights of each individual or collective right holders have expired.
There has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that "Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation." (art. 14)
## Future work
This dataset is not a one-time work but will continue to evolve significantly in three directions:
* Expansion of the dataset to the late 19th and early 20th century works and its further enhancement with currently unexploited collections coming from European patrimonial data repositories.
* Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.
* Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well-formatted.
## Acknowledgements
The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC).
Corpus collection has been largely facilitated thanks to the open science LLM community insights and cooperation (Occiglot, Eleuther AI, Allen AI).
<div style="text-align: center;">
<img src="https://github.com/mch-dd/datasetlogo/blob/main/scaleway.jpeg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/ministere.png?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/occiglot.jpg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
</div>
|
expertai/BUSTER | ---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- token-classification
pretty_name: buster
tags:
- finance
configs:
- config_name: default
data_files:
- split: FOLD_1
path: data/FOLD_1-*
- split: FOLD_2
path: data/FOLD_2-*
- split: FOLD_3
path: data/FOLD_3-*
- split: FOLD_4
path: data/FOLD_4-*
- split: FOLD_5
path: data/FOLD_5-*
- split: SILVER
path: data/SILVER-*
dataset_info:
features:
- name: document_id
dtype: string
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: FOLD_1
num_bytes: 13597946
num_examples: 753
- name: FOLD_2
num_bytes: 13477878
num_examples: 759
- name: FOLD_3
num_bytes: 13602552
num_examples: 758
- name: FOLD_4
num_bytes: 13834760
num_examples: 755
- name: FOLD_5
num_bytes: 13632431
num_examples: 754
- name: SILVER
num_bytes: 108914416
num_examples: 6196
download_size: 0
dataset_size: 177059983
---
# Dataset Card for BUSTER
BUSiness Transaction Entity Recognition dataset.
BUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of
3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,
plus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system.
### Data Splits Statistics
<table border="1" cellspacing="0" cellpadding="5" style="border-collapse: collapse; width: 100%;">
<thead>
<tr>
<th></th>
<th></th>
<th colspan="6" style="text-align:center;">Gold</th>
<th>Silver</th>
</tr>
<tr>
<th></th>
<th></th>
<th>fold 1</th>
<th>fold 2</th>
<th>fold 3</th>
<th>fold 4</th>
<th>fold 5</th>
<th>Total</th>
<th>Total</th>
</tr>
</thead>
<tbody>
<tr>
<td></td>
<td>N. Docs</td>
<td>753</td>
<td>759</td>
<td>758</td>
<td>755</td>
<td>754</td>
<td>3779</td>
<td>6196</td>
</tr>
<tr>
<td></td>
<td>N. Tokens</td>
<td>685K</td>
<td>680K</td>
<td>687K</td>
<td>697K</td>
<td>688K</td>
<td>3437K</td>
<td>5647K</td>
</tr>
<tr>
<td></td>
<td>N. Annotations</td>
<td>4119</td>
<td>4267</td>
<td>4100</td>
<td>4103</td>
<td>4163</td>
<td>20752</td>
<td>33272</td>
</tr>
</tbody>
</table>
### Pre-print
You can find the pre-print [here](https://arxiv.org/abs/2402.09916).
### Citation Information
If you use BUSTER in your work, please cite us:
```
@inproceedings{zugarini-etal-2023-buster,
title = "{BUSTER}: a {``}{BUS}iness Transaction Entity Recognition{''} dataset",
author = "Zugarini, Andrea and
Zamai, Andrew and
Ernandes, Marco and
Rigutini, Leonardo",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.57",
doi = "10.18653/v1/2023.emnlp-industry.57",
pages = "605--611",
abstract = "Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.",
}
```
|
open-llm-leaderboard/details_ABX-AI__Infinite-Laymons-9B | ---
pretty_name: Evaluation run of ABX-AI/Infinite-Laymons-9B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ABX-AI/Infinite-Laymons-9B](https://huggingface.co/ABX-AI/Infinite-Laymons-9B)\
\ 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_ABX-AI__Infinite-Laymons-9B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-10T11:48:19.797016](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Infinite-Laymons-9B/blob/main/results_2024-04-10T11-48-19.797016.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\": 0.645959924426964,\n\
\ \"acc_stderr\": 0.032166986596234216,\n \"acc_norm\": 0.6488478418620767,\n\
\ \"acc_norm_stderr\": 0.03281361203349943,\n \"mc1\": 0.38922888616891066,\n\
\ \"mc1_stderr\": 0.017068552680690335,\n \"mc2\": 0.5486913365249292,\n\
\ \"mc2_stderr\": 0.015339111406271384\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893446,\n\
\ \"acc_norm\": 0.6561433447098977,\n \"acc_norm_stderr\": 0.013880644570156213\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6478789085839474,\n\
\ \"acc_stderr\": 0.004766553336917499,\n \"acc_norm\": 0.8413662617008564,\n\
\ \"acc_norm_stderr\": 0.003645875568601287\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\
\ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|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-college_mathematics|5\": {\n \"acc\": 0.35,\n\
\ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \
\ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n\
\ \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n\
\ \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n\
\ \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \
\ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.574468085106383,\n\
\ \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.574468085106383,\n\
\ \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.04685473041907789,\n\
\ \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"\
acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997685,\n \"\
acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997685\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\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\": 0.7903225806451613,\n\
\ \"acc_stderr\": 0.023157879349083525,\n \"acc_norm\": 0.7903225806451613,\n\
\ \"acc_norm_stderr\": 0.023157879349083525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586804,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586804\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6923076923076923,\n \"acc_stderr\": 0.023400928918310495,\n\
\ \"acc_norm\": 0.6923076923076923,\n \"acc_norm_stderr\": 0.023400928918310495\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342853,\n\
\ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342853\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078966,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078966\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \
\ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\
\ \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.6681614349775785,\n\
\ \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159464,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\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\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.013890862162876164,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.013890862162876164\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3452513966480447,\n\
\ \"acc_stderr\": 0.015901432608930354,\n \"acc_norm\": 0.3452513966480447,\n\
\ \"acc_norm_stderr\": 0.015901432608930354\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135107,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135107\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45632333767926986,\n\
\ \"acc_stderr\": 0.012721420501462547,\n \"acc_norm\": 0.45632333767926986,\n\
\ \"acc_norm_stderr\": 0.012721420501462547\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n\
\ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000318,\n \
\ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000318\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38922888616891066,\n\
\ \"mc1_stderr\": 0.017068552680690335,\n \"mc2\": 0.5486913365249292,\n\
\ \"mc2_stderr\": 0.015339111406271384\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5375284306292646,\n \
\ \"acc_stderr\": 0.013733636059107759\n }\n}\n```"
repo_url: https://huggingface.co/ABX-AI/Infinite-Laymons-9B
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_04_10T11_48_19.797016
path:
- '**/details_harness|arc:challenge|25_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|gsm8k|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hellaswag|10_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-48-19.797016.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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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_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T11-48-19.797016.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- '**/details_harness|winogrande|5_2024-04-10T11-48-19.797016.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-10T11-48-19.797016.parquet'
- config_name: results
data_files:
- split: 2024_04_10T11_48_19.797016
path:
- results_2024-04-10T11-48-19.797016.parquet
- split: latest
path:
- results_2024-04-10T11-48-19.797016.parquet
---
# Dataset Card for Evaluation run of ABX-AI/Infinite-Laymons-9B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ABX-AI/Infinite-Laymons-9B](https://huggingface.co/ABX-AI/Infinite-Laymons-9B) 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_ABX-AI__Infinite-Laymons-9B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-10T11:48:19.797016](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Infinite-Laymons-9B/blob/main/results_2024-04-10T11-48-19.797016.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": 0.645959924426964,
"acc_stderr": 0.032166986596234216,
"acc_norm": 0.6488478418620767,
"acc_norm_stderr": 0.03281361203349943,
"mc1": 0.38922888616891066,
"mc1_stderr": 0.017068552680690335,
"mc2": 0.5486913365249292,
"mc2_stderr": 0.015339111406271384
},
"harness|arc:challenge|25": {
"acc": 0.6254266211604096,
"acc_stderr": 0.014144193471893446,
"acc_norm": 0.6561433447098977,
"acc_norm_stderr": 0.013880644570156213
},
"harness|hellaswag|10": {
"acc": 0.6478789085839474,
"acc_stderr": 0.004766553336917499,
"acc_norm": 0.8413662617008564,
"acc_norm_stderr": 0.003645875568601287
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.038424985593952694,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.038424985593952694
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.02825420034443866,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.02825420034443866
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4021164021164021,
"acc_stderr": 0.025253032554997685,
"acc_norm": 0.4021164021164021,
"acc_norm_stderr": 0.025253032554997685
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"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": 0.7903225806451613,
"acc_stderr": 0.023157879349083525,
"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586804,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586804
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768776,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768776
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6923076923076923,
"acc_stderr": 0.023400928918310495,
"acc_norm": 0.6923076923076923,
"acc_norm_stderr": 0.023400928918310495
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.02911661760608301,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.02911661760608301
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7016806722689075,
"acc_stderr": 0.029719142876342853,
"acc_norm": 0.7016806722689075,
"acc_norm_stderr": 0.029719142876342853
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8403669724770643,
"acc_stderr": 0.015703498348461763,
"acc_norm": 0.8403669724770643,
"acc_norm_stderr": 0.015703498348461763
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.034086558679777494,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.034086558679777494
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8235294117647058,
"acc_stderr": 0.026756401538078966,
"acc_norm": 0.8235294117647058,
"acc_norm_stderr": 0.026756401538078966
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
"acc_stderr": 0.027123298205229966,
"acc_norm": 0.7763713080168776,
"acc_norm_stderr": 0.027123298205229966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6681614349775785,
"acc_stderr": 0.03160295143776679,
"acc_norm": 0.6681614349775785,
"acc_norm_stderr": 0.03160295143776679
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159464,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159464
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.038260763248848646,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.0335195387952127,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.0335195387952127
},
"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": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165616,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.013890862162876164,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.013890862162876164
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
"acc_stderr": 0.02418242749657761,
"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.02418242749657761
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3452513966480447,
"acc_stderr": 0.015901432608930354,
"acc_norm": 0.3452513966480447,
"acc_norm_stderr": 0.015901432608930354
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818733,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818733
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.025583062489984813,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.025583062489984813
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.024477222856135107,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135107
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48936170212765956,
"acc_stderr": 0.029820747191422473,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.029820747191422473
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45632333767926986,
"acc_stderr": 0.012721420501462547,
"acc_norm": 0.45632333767926986,
"acc_norm_stderr": 0.012721420501462547
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6948529411764706,
"acc_stderr": 0.027971541370170595,
"acc_norm": 0.6948529411764706,
"acc_norm_stderr": 0.027971541370170595
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6650326797385621,
"acc_stderr": 0.019094228167000318,
"acc_norm": 0.6650326797385621,
"acc_norm_stderr": 0.019094228167000318
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940589,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.38922888616891066,
"mc1_stderr": 0.017068552680690335,
"mc2": 0.5486913365249292,
"mc2_stderr": 0.015339111406271384
},
"harness|winogrande|5": {
"acc": 0.8082083662194159,
"acc_stderr": 0.011065209664659527
},
"harness|gsm8k|5": {
"acc": 0.5375284306292646,
"acc_stderr": 0.013733636059107759
}
}
```
## 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] |
AymanMansour/SDN-Dialect-Dataset | ---
dataset_info:
features:
- name: filename
dtype: string
- name: text
dtype: string
- name: orthographic
dtype: string
- name: transliteration
dtype: string
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 1697895484.76
num_examples: 4830
- name: test
num_bytes: 244760635.0
num_examples: 532
download_size: 2883670807
dataset_size: 1942656119.76
---
# Dataset Card for "SDN-Dialect-Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
talktolisten/ttl-train | ---
task_categories:
- conversational
size_categories:
- 10K<n<100K
--- |
316usman/thematic5e-pw-embed-part7 | ---
dataset_info:
features:
- name: text
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
- name: country
dtype: string
splits:
- name: train
num_bytes: 263314408
num_examples: 405601
download_size: 102834585
dataset_size: 263314408
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
daje/mistral_tokenized_en_wiki | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24317150575
num_examples: 16096560
download_size: 10840035501
dataset_size: 24317150575
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ronnybehrens/mini-platypus_dc | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4168526
num_examples: 1000
download_size: 2239555
dataset_size: 4168526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Intuit-GenSRF/all_spanish_datasets | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
- name: encoded_labels
sequence: int64
- name: lang
dtype: string
- name: has_toxic
dtype: int64
- name: has_profane
dtype: int64
- name: has_insult
dtype: int64
- name: has_hate
dtype: int64
- name: has_threat
dtype: int64
- name: has_sexual
dtype: int64
- name: has_offensive
dtype: int64
- name: has_selfharm
dtype: int64
- name: has_harassment
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1495796876
num_examples: 2814389
download_size: 603996129
dataset_size: 1495796876
---
# Dataset Card for "all_spanish_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jtjt520j/cspider_for_chinese_llama2_1.3b | ---
license: apache-2.0
---
|
projecte-aina/CA-EN_Parallel_Corpus | ---
language:
- ca
- en
multilinguality:
- multilingual
pretty_name: CA-EN Parallel Corpus
size_categories:
- 10M<n<100M
task_categories:
- translation
task_ids: []
license: cc-by-4.0
---
# Dataset Card for CA-EN Parallel Corpus
## Dataset Description
### Dataset Summary
The CA-EN Parallel Corpus is a Catalan-English dataset of **14.967.979** parallel sentences.
The dataset was created to support Catalan in NLP tasks, specifically Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train Bilingual Machine Translation models between English and Catalan in any direction,
as well as Multilingual Machine Translation models.
### Languages
The sentences included in the dataset are in Catalan (CA) and English (EN).
## Dataset Structure
### Data Instances
The dataset is a single tsv file where each row contains a parallel sentence pair, as well as the following information per sentence:
* language probability score calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py),
* alignment score calculated with [LaBSE](https://huggingface.co/sentence-transformers/LaBSE),
* domain,
* text type.
### Data Fields
Each example contains the following 7 fields:
* ca: Catalan sentence
* en: English sentence
* ca_prob: Language probability for the Catalan sentence calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py)
* en_prob: Language probability for the English sentence calculated with the language detector [lingua.py](https://github.com/pemistahl/lingua-py)
* alignment: Sentence pair alignment score calculated with [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)
* Domain: Domain (see list of domains)
* Type: Text type (see list of text types)
#### Example:
<pre>
[
{
Pel que fa als motors de cerca, també es basen en l'estructura del seu contingut d'informació al lloc web per analitzar i indexar el seu lloc web. As for the search engines, they also rely on the structure of your information content on the website to analyze and index your website. 0.9999799355804416 0.9993718600460302 0.91045034 MWM SM
},
...
]
</pre>
#### List of domains (and number of sentences per domain):
AUT: Automotive, transport, traffic regulations (2.289.951)
LEG: legal, law, HR, certificates, degrees (498.676)
MWM: Marketing, web, merchandising, customer support and service, e-commerce , advertising, surveys (1.066.111)
LSM: Medicine, natural sciences, food/nutrition, biology, sexology, cosmetics, chemistry, genetics (457.647)
ENV: Environment, agriculture, forestry, fisheries, farming, zoology, ecology (681.813)
FIN: Finance, economics, business, entrepreneurship, business, competitions, labour, employment, accounting, insurance, insurance (292.865)
POL: Politics, international relations, European Union, international organisations, defence, military (451.569)
PRN: Porn, inappropriate content (597.926)
COM: Computers, IT, robotics, domotics, home automation, telecommunications (1.200.192)
ING: Pure engineering (mechanical, electrical, electronic, aerospace...), meteorology, mining, engineering, maritime, acoustics (581.722)
ARC: Architecture, civil engineering, construction, public engineering (663.985)
MAT: Mathematics, statistics, physics (216.635)
HRM: History, religion, mythology, folklore, philosophy, psychology, ethics, anthropology, tourism (1.362.302)
CUL: Art, poetry, literature, cinema, video games, theatre, theatre/film scripts, esotericism, astrology, sports, music, photography (2.774.420)
GEN: General - generic cathegory with topics such as clothing, textiles, gastronomy, etc. (1.832.164)
#### List of text types (and number of sentences per text type):
PAT: Patents (583.353)
SM: Social media, chats, forums, tweets (6.420.644)
CON: Oral language, transcription of conversations, subtitles (3.709.344)
EML: Emails (543.010)
MNL: Manuals, data sheets (1.379.021)
NEW: News, journalism (1.346.845)
GEN: Prose, generic type of text (985.761)
### Data Splits
The dataset contains a single split: `train`.
Individual domain or style specific subsets can be extracted from the original dataset
by filtering by the previously mentioned domains and text types.
## Dataset Creation
### Curation Rationale
This dataset is aimed at promoting the development of Machine Translation between Catalan and other languages, specifically English.
### Source Data
#### Initial Data Collection and Normalization
The data is a collection of parallel sentences in Catalan and English, partially derived from web crawlings
and belonging to a mix of different domains and styles.
The source data is partially Catalan authentic text translated into English and partially authentic English text translated into Catalan.
The data was obtained through a combination of human translation and machine translation with human proofreading.
The obtained corpus consists of **14.967.979** parallel sentences.
#### Who are the source language producers?
The original data gathering was entrusted to an external company through a public tender process.
### Annotations
#### Annotation process
The dataset does not contain any annotations.
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied,
personal and sensitive information may be present in the data. This needs to be considered when using the data for training models.
## Considerations for Using the Data
### Social Impact of Dataset
By providing this resource, we intend to promote the use of Catalan across NLP tasks,
thereby improving the accessibility and visibility of the Catalan language.
### Discussion of Biases
No specific bias mitigation strategies were applied to this dataset.
Inherent biases may exist within the data.
### Other Known Limitations
The dataset contains data of several specific domains. The dataset can be used as a whole or extracting subsets per domain or text types.
Applications of this dataset in domains other than the ones included in the domain list would be of limited use.
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es).
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
[N/A]
### Contributions
[N/A] |
dominguesm/brwac | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: brwac
pretty_name: BrWaC
dataset_info:
features:
- name: doc_id
dtype: string
- name: title
dtype: string
- name: uri
dtype: string
- name: text
sequence:
- name: paragraphs
sequence: string
splits:
- name: train
num_bytes: 18828412956
num_examples: 3530796
download_size: 11616550261
dataset_size: 18828412956
---
# Dataset Card for BrWaC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Repository:** [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Paper:** [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/)
- **Point of Contact:** [Jorge A. Wagner Filho](mailto:jawfilho@inf.ufrgs.br)
### Dataset Summary
The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available
solely for academic research purposes, and you agreed not to use it for any commercial applications. No need to manually download external sources.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
An example from the BrWaC dataset looks as follows:
```
{
"doc_id": "netg-1afc73",
"text": {
"paragraphs": [
[
"Conteúdo recente"
],
[
"ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\""
],
[
"31 de Agosto de 2015, 7:07 , por paulo soavinski - | No one following this article yet."
],
[
"Visualizado 202 vezes"
],
[
"JORNAL ELETRÔNICO DA ILHA DO MEL"
],
[
"Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.",
"Na faixa de areia ela aparece disseminada e não chama muito a atenção.",
"No Buraco do Aipo, com muitas pedras, ela aparece concentrada.",
"É fácil saber que esta espuma estranha está lá, quando venta.",
"Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.",
"Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site."
]
]
},
"title": "ESPUMA MARROM CHAMADA ‟NINGUÃÂM MERECE‟ - paulo soavinski",
"uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece"
}
```
### Data Fields
- `doc_id`: The document ID
- `title`: The document title
- `uri`: URI where the document was extracted from
- `text`: A list of document paragraphs (with a list of sentences in it as a list of strings)
### Data Splits
The data is only split into train set with size of 3530796 samples.
## 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
```
@inproceedings{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
``` |
sumitj39/openhathi-7b-base-q4_0.ggml | ---
license: llama2
---
This dataset contains the ggml version of OpenHathi model released by Sarvam AI. [Link to original model](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base).
The ggml file provided is 4 bit quantized version, it can be run on local devices such as an M1 MacBook or other hardware.
### How to use?
1. Download llama.cpp from [here](https://github.com/ggerganov/llama.cpp)
```bash
git clone https://github.com/ggerganov/llama.cpp
```
3. Note: The ggml support has been deprecated, new file format is gguf. But since this repository contains ggml file, we have to switch back to an older commit of llama.cpp that worked with ggml files.
Execute this command to switch to the commit that worked with ggml files:
```
bash git checkout dadbed9
```
3. Read the instructions mentioned [here](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build) to create an executable file in the llama.cpp directory.
4. Run the model:
```bash
./main -t 4 -m ~/ggml-models/openhathi-7b-base-q4_0.ggml -p "tell me about india in hindi: - भारत" --ctx-size 1024 -ngl 1 2>/dev/null
```
5. The model prints output:
>भारत दुनिया के सबसे पुराने देशों में से एक है और दुनिया की 7वीं सबसे बड़ी आबादी वाला देश है। The nation has a rich and diverse history, dating back to ancient times when it was ruled by various empires and kingdoms. भारत में दो मुख्य भौगोलिक क्षेत्र शामिल हैंः एक द्वीपसमूह जिसमें कई बड़े द्वीपों के साथ-साथ छोटे द्वीप भी शामिल हैं और दूसरा समतल क्षेत्रों से घिरा हुआ है। भारत की अनूठी सांस्कृतिक विरासत, विविध धर्मों और भाषाओं को बढ़ावा देता है जो देश की समृद्ध विविधता का प्रमाण हैं। भारत में सबसे अधिक बोली जाने वाली भाषाएँ हिंदी, बंगाली, तमिल, मराठी, कन्नड़, उड़िया और मलयालम हैं। 40 प्रतिशत आबादी हिंदू है, जबकि अन्य प्रमुख धर्म इस्लाम, बौद्ध धर्म, ईसाई धर्म और सिख धर्म हैं। भारत अपनी समृद्ध कृषि अर्थव्यवस्था के लिए जाना जाता है और यह अपने विविध व्यंजनों, समृद्ध इतिहास और जीवंत सांस्कृतिक विरासत के लिए भी मनाया जाता है। The country has made remarkable strides in areas such as information technology and manufacturing, which have contributed to its global economic position. एक बड़े देश होने के बावजूद, भारत में सभी को एक स्थान से दूसरे स्थान पर जाने की आवश्यकता नहीं है। India's transport infrastructure is extensive, with a well-developed road network that connects most major cities and towns. इसके अलावा, मुंबई और दिल्ली जैसे प्रमुख शहरों में अंतर्राष्ट्रीय हवाई अड्डे हैं जो दुनिया भर के गंतव्यों के लिए उड़ान भरते हैं। India has also gained prominence as a popular tourist destination in recent years. देश अपने अनूठे अनुभवों, आश्चर्यजनक प्राकृतिक परिदृश्यों, विविध संस्कृतियों और समृद्ध इतिहास की पेशकश करता है। From world-famous sites such as the Taj Mahal to lesser- .....
> Note: This is a base model, to use it in your applications you need to fine tune it.
|
autoevaluate/autoeval-eval-conceptual_captions-unlabeled-ccbde0-1800162251 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conceptual_captions
eval_info:
task: summarization
model: 0ys/mt5-small-finetuned-amazon-en-es
metrics: ['accuracy']
dataset_name: conceptual_captions
dataset_config: unlabeled
dataset_split: train
col_mapping:
text: image_url
target: caption
---
# 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: 0ys/mt5-small-finetuned-amazon-en-es
* Dataset: conceptual_captions
* Config: unlabeled
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@DonaldDaz](https://huggingface.co/DonaldDaz) for evaluating this model. |
EgilKarlsen/PKDD_GPT2_Finetuned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: float32
- name: '1'
dtype: float32
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dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211871323
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_GPT2_Finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k | ---
pretty_name: Evaluation run of Open-Orca/LlongOrca-7B-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Open-Orca/LlongOrca-7B-16k](https://huggingface.co/Open-Orca/LlongOrca-7B-16k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T04:31:23.491817](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k/blob/main/results_2023-10-18T04-31-23.491817.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.016988255033557047,\n\
\ \"em_stderr\": 0.0013234068882109723,\n \"f1\": 0.08061136744966452,\n\
\ \"f1_stderr\": 0.001896831507875326,\n \"acc\": 0.4100619744335266,\n\
\ \"acc_stderr\": 0.009753220057431532\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.016988255033557047,\n \"em_stderr\": 0.0013234068882109723,\n\
\ \"f1\": 0.08061136744966452,\n \"f1_stderr\": 0.001896831507875326\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07505686125852919,\n \
\ \"acc_stderr\": 0.007257633145486642\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Open-Orca/LlongOrca-7B-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|drop|3_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T04-31-23.491817.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|gsm8k|5_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T04-31-23.491817.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- '**/details_harness|winogrande|5_2023-10-18T04-31-23.491817.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T04-31-23.491817.parquet'
- config_name: results
data_files:
- split: 2023_10_18T04_31_23.491817
path:
- results_2023-10-18T04-31-23.491817.parquet
- split: latest
path:
- results_2023-10-18T04-31-23.491817.parquet
---
# Dataset Card for Evaluation run of Open-Orca/LlongOrca-7B-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Open-Orca/LlongOrca-7B-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Open-Orca/LlongOrca-7B-16k](https://huggingface.co/Open-Orca/LlongOrca-7B-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T04:31:23.491817](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__LlongOrca-7B-16k/blob/main/results_2023-10-18T04-31-23.491817.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.016988255033557047,
"em_stderr": 0.0013234068882109723,
"f1": 0.08061136744966452,
"f1_stderr": 0.001896831507875326,
"acc": 0.4100619744335266,
"acc_stderr": 0.009753220057431532
},
"harness|drop|3": {
"em": 0.016988255033557047,
"em_stderr": 0.0013234068882109723,
"f1": 0.08061136744966452,
"f1_stderr": 0.001896831507875326
},
"harness|gsm8k|5": {
"acc": 0.07505686125852919,
"acc_stderr": 0.007257633145486642
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
IlyaGusev/stihi_ru | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: genre
dtype: string
- name: topic
dtype: string
- name: author
dtype: string
splits:
- name: train
num_bytes: 6029108612
num_examples: 5151050
download_size: 1892727043
dataset_size: 6029108612
task_categories:
- text-generation
language:
- ru
size_categories:
- 1M<n<10M
---
# Stihi.ru dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Description
**Summary:** A subset if [Taiga](https://tatianashavrina.github.io/taiga_site/), uploaded here for convenience. Additional cleaning was performed.
**Script:** [create_stihi.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_stihi.py)
**Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
**Languages:** Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/stihi_ru', split="train", streaming=True)
for example in dataset:
print(example["text"])
```
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible. |
rabhinavs/new_ds | ---
license: apache-2.0
---
|
collabora/project-gutenberg-wds-preprocessed | ---
license: cc0-1.0
---
|
zolak/twitter_dataset_50_1713070262 | ---
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: 2631200
num_examples: 6583
download_size: 1309320
dataset_size: 2631200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/739614f9 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1325
dataset_size: 180
---
# Dataset Card for "739614f9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/eruda_edomaeelf | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of エルダ
This is the dataset of エルダ, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 667 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 667 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 667 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 667 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
automated-research-group/llama2_7b_chat-boolq | ---
dataset_info:
features:
- name: id
dtype: string
- name: request
dtype: string
- name: response
dtype: string
- name: input_perplexity
dtype: float64
- name: input_likelihood
dtype: float64
- name: output_perplexity
dtype: float64
- name: output_likelihood
dtype: float64
splits:
- name: validation
num_bytes: 2716450
num_examples: 3270
download_size: 1480464
dataset_size: 2716450
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "llama2_7b_chat-boolq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ZHLiu627/ultrafeedback_binarized_with_response_full | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: reference_response
dtype: string
splits:
- name: train_prefs
num_bytes: 510824465
num_examples: 61135
download_size: 0
dataset_size: 510824465
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
# Dataset Card for "ultrafeedback_binarized_with_response_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_78_1713170051 | ---
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: 354964
num_examples: 898
download_size: 175261
dataset_size: 354964
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/annie_leagueoflegends | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of annie (League of Legends)
This is the dataset of annie (League of Legends), containing 125 images and their tags.
The core tags of this character are `green_eyes, animal_ears, short_hair, red_hair, cat_ears, fake_animal_ears, pink_hair`, 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 | 125 | 92.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annie_leagueoflegends/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 125 | 67.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annie_leagueoflegends/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 260 | 128.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annie_leagueoflegends/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 125 | 85.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annie_leagueoflegends/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 260 | 155.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/annie_leagueoflegends/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/annie_leagueoflegends',
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 | 15 |  |  |  |  |  | 1girl, solo, teddy_bear, backpack, looking_at_viewer, smile, dress, puffy_sleeves, striped |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | teddy_bear | backpack | looking_at_viewer | smile | dress | puffy_sleeves | striped |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:-----------|:--------------------|:--------|:--------|:----------------|:----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
|
hippocrates/Multicare_rare_train | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 47550177
num_examples: 11334
- name: valid
num_bytes: 47550177
num_examples: 11334
- name: test
num_bytes: 47550177
num_examples: 11334
download_size: 77224462
dataset_size: 142650531
---
# Dataset Card for "Multicare_rare_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Harsha9044/MAl_MSA | ---
license: apache-2.0
dataset_info:
features:
- name: File name
dtype: string
- name: Transcript
dtype: string
- name: Labels
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 290551
num_examples: 70
download_size: 124404
dataset_size: 290551
---
|
TokenBender/HelpSteer_alpaca_reformatted | ---
license: apache-2.0
---
|
annaludicode/ladiesInColoredWaterStyle | ---
license: artistic-2.0
---
|
datahrvoje/twitter_dataset_1713138771 | ---
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: 17776
num_examples: 44
download_size: 10873
dataset_size: 17776
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B | ---
pretty_name: Evaluation run of Severian/ANIMA-Phi-Neptune-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Severian/ANIMA-Phi-Neptune-Mistral-7B](https://huggingface.co/Severian/ANIMA-Phi-Neptune-Mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T10:04:15.191273](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B/blob/main/results_2023-10-29T10-04-15.191273.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\": 0.10329278523489933,\n\
\ \"em_stderr\": 0.003116735713102519,\n \"f1\": 0.1624748322147643,\n\
\ \"f1_stderr\": 0.003266242273162539,\n \"acc\": 0.442081101118795,\n\
\ \"acc_stderr\": 0.011112320094960076\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.10329278523489933,\n \"em_stderr\": 0.003116735713102519,\n\
\ \"f1\": 0.1624748322147643,\n \"f1_stderr\": 0.003266242273162539\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14935557240333586,\n \
\ \"acc_stderr\": 0.009818090723727293\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7348066298342542,\n \"acc_stderr\": 0.01240654946619286\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Severian/ANIMA-Phi-Neptune-Mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|arc:challenge|25_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T10_04_15.191273
path:
- '**/details_harness|drop|3_2023-10-29T10-04-15.191273.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T10-04-15.191273.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T10_04_15.191273
path:
- '**/details_harness|gsm8k|5_2023-10-29T10-04-15.191273.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T10-04-15.191273.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hellaswag|10_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T23-09-12.843992.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T23-09-12.843992.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T23-09-12.843992.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T10_04_15.191273
path:
- '**/details_harness|winogrande|5_2023-10-29T10-04-15.191273.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T10-04-15.191273.parquet'
- config_name: results
data_files:
- split: 2023_10_10T23_09_12.843992
path:
- results_2023-10-10T23-09-12.843992.parquet
- split: 2023_10_29T10_04_15.191273
path:
- results_2023-10-29T10-04-15.191273.parquet
- split: latest
path:
- results_2023-10-29T10-04-15.191273.parquet
---
# Dataset Card for Evaluation run of Severian/ANIMA-Phi-Neptune-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Severian/ANIMA-Phi-Neptune-Mistral-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Severian/ANIMA-Phi-Neptune-Mistral-7B](https://huggingface.co/Severian/ANIMA-Phi-Neptune-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T10:04:15.191273](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B/blob/main/results_2023-10-29T10-04-15.191273.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": 0.10329278523489933,
"em_stderr": 0.003116735713102519,
"f1": 0.1624748322147643,
"f1_stderr": 0.003266242273162539,
"acc": 0.442081101118795,
"acc_stderr": 0.011112320094960076
},
"harness|drop|3": {
"em": 0.10329278523489933,
"em_stderr": 0.003116735713102519,
"f1": 0.1624748322147643,
"f1_stderr": 0.003266242273162539
},
"harness|gsm8k|5": {
"acc": 0.14935557240333586,
"acc_stderr": 0.009818090723727293
},
"harness|winogrande|5": {
"acc": 0.7348066298342542,
"acc_stderr": 0.01240654946619286
}
}
```
### 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] |
lucasabc/vozes | ---
license: other
license_name: teste
license_link: LICENSE
---
|
Dahoas/instruct_helpful_preferences | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 187411037
num_examples: 105161
- name: test
num_bytes: 9924509
num_examples: 5538
download_size: 119287465
dataset_size: 197335546
---
# Dataset Card for "instruct_helpful_preferences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/synpre_mix_v4_1M | ---
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: 1631375419.5
num_examples: 1000000
- name: validation
num_bytes: 16342801.5
num_examples: 10000
download_size: 10827005
dataset_size: 1647718221.0
---
# Dataset Card for "synpre_mix_v4_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sadelja/cuprum_dataset | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: description
dtype: string
splits:
- name: train
num_bytes: 3646765464.0
num_examples: 841
download_size: 3470985112
dataset_size: 3646765464.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
presencesw/multinli_entailment | ---
dataset_info:
features:
- name: gold_label
dtype: string
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 69566860
num_examples: 274829
- name: dev_matched
num_bytes: 1918017
num_examples: 9815
- name: dev_mismatched
num_bytes: 2033699
num_examples: 9832
download_size: 30820933
dataset_size: 73518576
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev_matched
path: data/dev_matched-*
- split: dev_mismatched
path: data/dev_mismatched-*
---
|
Horeknad/komi-russian-parallel-corpora | ---
license: cc-by-4.0
task_categories:
- translation
language:
- ru
- kv
size_categories:
- 10K<n<100K
annotations_creators:
- found
tags:
- text
source_datasets:
- Millet porridge by Ivan Toropov (adaptation)
- Komi media library (http://videocorpora.ru/)
- news from the website of the Komi administration (https://rkomi.ru/)
---
# Source Datasets #
<li>1 - news from the website of the Komi administration (https://rkomi.ru/)</li>
<li>2 - Komi media library (http://videocorpora.ru/)</li>
<li>3 - Millet porridge by Ivan Toropov (adaptation)</li>
<br>
# Authors #
Shilova Nadezhda<br>
Chernousov Georgy
|
cestwc/SG-subzone-poi-sentiment_1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: local_created_at
dtype: string
- name: id
dtype: int64
- name: text
dtype: string
- name: source
dtype: string
- name: truncated
dtype: bool
- name: in_reply_to_status_id
dtype: float32
- name: in_reply_to_user_id
dtype: float32
- name: user_id
dtype: int64
- name: user_name
dtype: string
- name: user_screen_name
dtype: string
- name: user_location
dtype: string
- name: user_url
dtype: string
- name: user_verified
dtype: bool
- name: user_default_profile
dtype: bool
- name: user_description
dtype: string
- name: user_followers_count
dtype: int64
- name: user_friends_count
dtype: int64
- name: user_listed_count
dtype: int64
- name: user_favourites_count
dtype: int64
- name: user_statuses_count
dtype: int64
- name: local_user_created_at
dtype: string
- name: place_id
dtype: string
- name: place_url
dtype: string
- name: place_place_type
dtype: string
- name: place_name
dtype: string
- name: place_country_code
dtype: string
- name: place_bounding_box_type
dtype: string
- name: place_bounding_box_coordinates
dtype: string
- name: is_quote_status
dtype: bool
- name: retweet_count
dtype: int64
- name: favorite_count
dtype: int64
- name: entities_hashtags
dtype: string
- name: entities_urls
dtype: string
- name: entities_symbols
dtype: string
- name: entities_user_mentions
dtype: string
- name: favorited
dtype: bool
- name: retweeted
dtype: bool
- name: possibly_sensitive
dtype: bool
- name: lang
dtype: string
- name: latitude
dtype: float32
- name: longitude
dtype: float32
- name: year_created_at
dtype: int64
- name: month_created_at
dtype: int64
- name: day_created_at
dtype: int64
- name: weekday_created_at
dtype: int64
- name: hour_created_at
dtype: int64
- name: minute_created_at
dtype: int64
- name: year_user_created_at
dtype: int64
- name: month_user_created_at
dtype: int64
- name: day_user_created_at
dtype: int64
- name: weekday_user_created_at
dtype: int64
- name: hour_user_created_at
dtype: int64
- name: minute_user_created_at
dtype: int64
- name: subzone
dtype: string
- name: planning_area
dtype: string
- name: poi_flag
dtype: float32
- name: poi_id
dtype: string
- name: poi_dist
dtype: float32
- name: poi_latitude
dtype: float32
- name: poi_longitude
dtype: float32
- name: poi_name
dtype: string
- name: poi_type
dtype: string
- name: poi_cate2
dtype: string
- name: poi_cate3
dtype: string
- name: clean_text
dtype: string
- name: joy_score
dtype: float32
- name: trust_score
dtype: float32
- name: positive_score
dtype: float32
- name: sadness_score
dtype: float32
- name: disgust_score
dtype: float32
- name: anger_score
dtype: float32
- name: anticipation_score
dtype: float32
- name: negative_score
dtype: float32
- name: fear_score
dtype: float32
- name: surprise_score
dtype: float32
- name: words
dtype: string
- name: polarity_score
dtype: float32
- name: manual_label_1
dtype: int64
- name: T0_q1
dtype: int64
- name: bart_mnli
dtype: float32
- name: T0_q2
dtype: int64
- name: num_keywords
dtype: int64
- name: preprocess-1
dtype: string
- name: preprocess-2
dtype: string
- name: llama
dtype: int64
- name: clabel
dtype: bool
splits:
- name: train
num_bytes: 1597795154
num_examples: 1025135
download_size: 490565616
dataset_size: 1597795154
---
# Dataset Card for "SG-subzone-poi-sentiment_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anthony-wss/librispeech_asr-audiodec_encodec_24k | ---
configs:
- config_name: default
data_files:
- split: train.clean.360
path: data/train.clean.360-*
- split: train.other.500
path: data/train.other.500-*
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: unit
sequence:
sequence: int64
splits:
- name: train.clean.360
num_bytes: 1070603220
num_examples: 104014
- name: train.other.500
num_bytes: 1462474737
num_examples: 148688
download_size: 406727746
dataset_size: 2533077957
---
# Dataset Card for "librispeech_asr-audiodec_encodec_24k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sdg416826/test | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1655208
num_examples: 1000
download_size: 966969
dataset_size: 1655208
---
|
open-llm-leaderboard/details_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO | ---
pretty_name: Evaluation run of cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO](https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO)\
\ 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_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-05T13:57:06.982400](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO/blob/main/results_2024-02-05T13-57-06.982400.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\": 0.7649892778549832,\n\
\ \"acc_stderr\": 0.02823313368050758,\n \"acc_norm\": 0.7681511495490131,\n\
\ \"acc_norm_stderr\": 0.028777527908042073,\n \"mc1\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.7131962651033679,\n\
\ \"mc2_stderr\": 0.014139525056193024\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7167235494880546,\n \"acc_stderr\": 0.013167478735134575,\n\
\ \"acc_norm\": 0.7406143344709898,\n \"acc_norm_stderr\": 0.012808273573927097\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6703843855805617,\n\
\ \"acc_stderr\": 0.004691128722535485,\n \"acc_norm\": 0.8666600278828919,\n\
\ \"acc_norm_stderr\": 0.003392470498816845\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7555555555555555,\n\
\ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.7555555555555555,\n\
\ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \
\ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8037735849056604,\n \"acc_stderr\": 0.024442388131100813,\n\
\ \"acc_norm\": 0.8037735849056604,\n \"acc_norm_stderr\": 0.024442388131100813\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n\
\ \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n\
\ \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.61,\n \"acc_stderr\": 0.049020713000019756,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.049020713000019756\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7109826589595376,\n\
\ \"acc_stderr\": 0.034564257450869995,\n \"acc_norm\": 0.7109826589595376,\n\
\ \"acc_norm_stderr\": 0.034564257450869995\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7574468085106383,\n \"acc_stderr\": 0.028020226271200217,\n\
\ \"acc_norm\": 0.7574468085106383,\n \"acc_norm_stderr\": 0.028020226271200217\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\
\ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\
\ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277696,\n\
\ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277696\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.7486772486772487,\n \"acc_stderr\": 0.0223404823396439,\n \"acc_norm\"\
: 0.7486772486772487,\n \"acc_norm_stderr\": 0.0223404823396439\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n\
\ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\
\ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9064516129032258,\n\
\ \"acc_stderr\": 0.016565754668270982,\n \"acc_norm\": 0.9064516129032258,\n\
\ \"acc_norm_stderr\": 0.016565754668270982\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.6699507389162561,\n \"acc_stderr\": 0.033085304262282574,\n\
\ \"acc_norm\": 0.6699507389162561,\n \"acc_norm_stderr\": 0.033085304262282574\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\"\
: 0.77,\n \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8848484848484849,\n \"acc_stderr\": 0.024925699798115344,\n\
\ \"acc_norm\": 0.8848484848484849,\n \"acc_norm_stderr\": 0.024925699798115344\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233335,\n \"\
acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233335\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.011464523356953162,\n\
\ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.011464523356953162\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588796,\n\
\ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588796\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.45925925925925926,\n \"acc_stderr\": 0.030384169232350832,\n \
\ \"acc_norm\": 0.45925925925925926,\n \"acc_norm_stderr\": 0.030384169232350832\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398897,\n\
\ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398897\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"\
acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9229357798165138,\n \"acc_stderr\": 0.011434381698911096,\n \"\
acc_norm\": 0.9229357798165138,\n \"acc_norm_stderr\": 0.011434381698911096\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\
acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \
\ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\
\ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\
\ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\
\ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\
\ \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n\
\ \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n\
\ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n\
\ \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n\
\ \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.0339329572976101,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.0339329572976101\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n\
\ \"acc_stderr\": 0.01500631280644693,\n \"acc_norm\": 0.9444444444444444,\n\
\ \"acc_norm_stderr\": 0.01500631280644693\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.9157088122605364,\n\
\ \"acc_stderr\": 0.009934966499513791,\n \"acc_norm\": 0.9157088122605364,\n\
\ \"acc_norm_stderr\": 0.009934966499513791\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8323699421965318,\n \"acc_stderr\": 0.020110579919734847,\n\
\ \"acc_norm\": 0.8323699421965318,\n \"acc_norm_stderr\": 0.020110579919734847\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.8,\n\
\ \"acc_stderr\": 0.013378001241813072,\n \"acc_norm\": 0.8,\n \
\ \"acc_norm_stderr\": 0.013378001241813072\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02082375883758091,\n\
\ \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02082375883758091\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n\
\ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n\
\ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571842,\n\
\ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571842\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.648936170212766,\n \"acc_stderr\": 0.028473501272963758,\n \
\ \"acc_norm\": 0.648936170212766,\n \"acc_norm_stderr\": 0.028473501272963758\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5912646675358539,\n\
\ \"acc_stderr\": 0.01255570134670338,\n \"acc_norm\": 0.5912646675358539,\n\
\ \"acc_norm_stderr\": 0.01255570134670338\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.022368672562886747,\n\
\ \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.022368672562886747\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.815359477124183,\n \"acc_stderr\": 0.015697029240757773,\n \
\ \"acc_norm\": 0.815359477124183,\n \"acc_norm_stderr\": 0.015697029240757773\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736847,\n\
\ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736847\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n\
\ \"acc_stderr\": 0.02019067053502792,\n \"acc_norm\": 0.9104477611940298,\n\
\ \"acc_norm_stderr\": 0.02019067053502792\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.7131962651033679,\n\
\ \"mc2_stderr\": 0.014139525056193024\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.01045089954537063\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7293404094010614,\n \
\ \"acc_stderr\": 0.012238245006183411\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|arc:challenge|25_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|gsm8k|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hellaswag|10_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T13-57-06.982400.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-05T13-57-06.982400.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- '**/details_harness|winogrande|5_2024-02-05T13-57-06.982400.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-05T13-57-06.982400.parquet'
- config_name: results
data_files:
- split: 2024_02_05T13_57_06.982400
path:
- results_2024-02-05T13-57-06.982400.parquet
- split: latest
path:
- results_2024-02-05T13-57-06.982400.parquet
---
# Dataset Card for Evaluation run of cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO](https://huggingface.co/cloudyu/TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO) 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_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-05T13:57:06.982400](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__TomGrc_FusionNet_34Bx2_MoE_v0.1_full_linear_DPO/blob/main/results_2024-02-05T13-57-06.982400.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": 0.7649892778549832,
"acc_stderr": 0.02823313368050758,
"acc_norm": 0.7681511495490131,
"acc_norm_stderr": 0.028777527908042073,
"mc1": 0.5458996328029376,
"mc1_stderr": 0.017429593091323522,
"mc2": 0.7131962651033679,
"mc2_stderr": 0.014139525056193024
},
"harness|arc:challenge|25": {
"acc": 0.7167235494880546,
"acc_stderr": 0.013167478735134575,
"acc_norm": 0.7406143344709898,
"acc_norm_stderr": 0.012808273573927097
},
"harness|hellaswag|10": {
"acc": 0.6703843855805617,
"acc_stderr": 0.004691128722535485,
"acc_norm": 0.8666600278828919,
"acc_norm_stderr": 0.003392470498816845
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7555555555555555,
"acc_stderr": 0.03712537833614866,
"acc_norm": 0.7555555555555555,
"acc_norm_stderr": 0.03712537833614866
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.875,
"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8037735849056604,
"acc_stderr": 0.024442388131100813,
"acc_norm": 0.8037735849056604,
"acc_norm_stderr": 0.024442388131100813
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9027777777777778,
"acc_stderr": 0.024774516250440182,
"acc_norm": 0.9027777777777778,
"acc_norm_stderr": 0.024774516250440182
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.049020713000019756,
"acc_norm": 0.61,
"acc_norm_stderr": 0.049020713000019756
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7109826589595376,
"acc_stderr": 0.034564257450869995,
"acc_norm": 0.7109826589595376,
"acc_norm_stderr": 0.034564257450869995
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.04971358884367406,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367406
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7574468085106383,
"acc_stderr": 0.028020226271200217,
"acc_norm": 0.7574468085106383,
"acc_norm_stderr": 0.028020226271200217
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5964912280701754,
"acc_stderr": 0.04615186962583707,
"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7517241379310344,
"acc_stderr": 0.036001056927277696,
"acc_norm": 0.7517241379310344,
"acc_norm_stderr": 0.036001056927277696
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.7486772486772487,
"acc_stderr": 0.0223404823396439,
"acc_norm": 0.7486772486772487,
"acc_norm_stderr": 0.0223404823396439
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5158730158730159,
"acc_stderr": 0.044698818540726076,
"acc_norm": 0.5158730158730159,
"acc_norm_stderr": 0.044698818540726076
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9064516129032258,
"acc_stderr": 0.016565754668270982,
"acc_norm": 0.9064516129032258,
"acc_norm_stderr": 0.016565754668270982
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6699507389162561,
"acc_stderr": 0.033085304262282574,
"acc_norm": 0.6699507389162561,
"acc_norm_stderr": 0.033085304262282574
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165044,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165044
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8848484848484849,
"acc_stderr": 0.024925699798115344,
"acc_norm": 0.8848484848484849,
"acc_norm_stderr": 0.024925699798115344
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9343434343434344,
"acc_stderr": 0.017646526677233335,
"acc_norm": 0.9343434343434344,
"acc_norm_stderr": 0.017646526677233335
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9740932642487047,
"acc_stderr": 0.011464523356953162,
"acc_norm": 0.9740932642487047,
"acc_norm_stderr": 0.011464523356953162
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8102564102564103,
"acc_stderr": 0.019880165406588796,
"acc_norm": 0.8102564102564103,
"acc_norm_stderr": 0.019880165406588796
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.45925925925925926,
"acc_stderr": 0.030384169232350832,
"acc_norm": 0.45925925925925926,
"acc_norm_stderr": 0.030384169232350832
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8445378151260504,
"acc_stderr": 0.023536818625398897,
"acc_norm": 0.8445378151260504,
"acc_norm_stderr": 0.023536818625398897
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5165562913907285,
"acc_stderr": 0.04080244185628972,
"acc_norm": 0.5165562913907285,
"acc_norm_stderr": 0.04080244185628972
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9229357798165138,
"acc_stderr": 0.011434381698911096,
"acc_norm": 0.9229357798165138,
"acc_norm_stderr": 0.011434381698911096
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6620370370370371,
"acc_stderr": 0.03225941352631295,
"acc_norm": 0.6620370370370371,
"acc_norm_stderr": 0.03225941352631295
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9264705882352942,
"acc_stderr": 0.018318855850089678,
"acc_norm": 0.9264705882352942,
"acc_norm_stderr": 0.018318855850089678
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9113924050632911,
"acc_stderr": 0.018498315206865384,
"acc_norm": 0.9113924050632911,
"acc_norm_stderr": 0.018498315206865384
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8026905829596412,
"acc_stderr": 0.02670985334496796,
"acc_norm": 0.8026905829596412,
"acc_norm_stderr": 0.02670985334496796
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8702290076335878,
"acc_stderr": 0.029473649496907065,
"acc_norm": 0.8702290076335878,
"acc_norm_stderr": 0.029473649496907065
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035202,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035202
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8981481481481481,
"acc_stderr": 0.02923927267563275,
"acc_norm": 0.8981481481481481,
"acc_norm_stderr": 0.02923927267563275
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8711656441717791,
"acc_stderr": 0.026321383198783674,
"acc_norm": 0.8711656441717791,
"acc_norm_stderr": 0.026321383198783674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5446428571428571,
"acc_stderr": 0.04726835553719098,
"acc_norm": 0.5446428571428571,
"acc_norm_stderr": 0.04726835553719098
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
"acc_stderr": 0.0339329572976101,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.0339329572976101
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9444444444444444,
"acc_stderr": 0.01500631280644693,
"acc_norm": 0.9444444444444444,
"acc_norm_stderr": 0.01500631280644693
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.9157088122605364,
"acc_stderr": 0.009934966499513791,
"acc_norm": 0.9157088122605364,
"acc_norm_stderr": 0.009934966499513791
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8323699421965318,
"acc_stderr": 0.020110579919734847,
"acc_norm": 0.8323699421965318,
"acc_norm_stderr": 0.020110579919734847
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.8,
"acc_stderr": 0.013378001241813072,
"acc_norm": 0.8,
"acc_norm_stderr": 0.013378001241813072
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02082375883758091,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02082375883758091
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.8006430868167203,
"acc_stderr": 0.022691033780549656,
"acc_norm": 0.8006430868167203,
"acc_norm_stderr": 0.022691033780549656
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8672839506172839,
"acc_stderr": 0.018877353839571842,
"acc_norm": 0.8672839506172839,
"acc_norm_stderr": 0.018877353839571842
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.648936170212766,
"acc_stderr": 0.028473501272963758,
"acc_norm": 0.648936170212766,
"acc_norm_stderr": 0.028473501272963758
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5912646675358539,
"acc_stderr": 0.01255570134670338,
"acc_norm": 0.5912646675358539,
"acc_norm_stderr": 0.01255570134670338
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8382352941176471,
"acc_stderr": 0.022368672562886747,
"acc_norm": 0.8382352941176471,
"acc_norm_stderr": 0.022368672562886747
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.815359477124183,
"acc_stderr": 0.015697029240757773,
"acc_norm": 0.815359477124183,
"acc_norm_stderr": 0.015697029240757773
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940589,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8489795918367347,
"acc_stderr": 0.022923004094736847,
"acc_norm": 0.8489795918367347,
"acc_norm_stderr": 0.022923004094736847
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.9104477611940298,
"acc_stderr": 0.02019067053502792,
"acc_norm": 0.9104477611940298,
"acc_norm_stderr": 0.02019067053502792
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.0272659924344291,
"acc_norm": 0.92,
"acc_norm_stderr": 0.0272659924344291
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
"acc_stderr": 0.03836722176598053,
"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598053
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015577,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015577
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5458996328029376,
"mc1_stderr": 0.017429593091323522,
"mc2": 0.7131962651033679,
"mc2_stderr": 0.014139525056193024
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.01045089954537063
},
"harness|gsm8k|5": {
"acc": 0.7293404094010614,
"acc_stderr": 0.012238245006183411
}
}
```
## 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] |
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