datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
LiveEvil/autotrain-data-testtextexists | ---
language:
- en
task_categories:
- text-scoring
---
# AutoTrain Dataset for project: testtextexists
## Dataset Description
This dataset has been automatically processed by AutoTrain for project testtextexists.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "According to the National Soft Drink Association, the annual consumption of soda by the U.S. citizens is 600 cans",
"target": 66.0
},
{
"text": "Experts say new vaccines are fake!",
"target": 50.0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='float32', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 19 |
| valid | 18 |
|
iMperria/hakaton_nto | ---
license: openrail
---
|
tr416/_dataset_20231007_141512 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 74307
dataset_size: 770400.0
---
# Dataset Card for "_dataset_20231007_141512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
spdenisov/udtrees | ---
dataset_info:
features:
- name: language
dtype: string
- name: sentence
dtype: string
- name: conllu
dtype: string
splits:
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num_examples: 4005
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num_examples: 1379
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num_examples: 26196
download_size: 335579995
dataset_size: 1045535496
---
# Dataset Card for "udtrees"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qgallouedec/prj_gia_dataset_metaworld_plate_slide_back_side_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the plate-slide-back-side-v2 environment, sample for the policy plate-slide-back-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_plate_slide_back_side_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_plate_slide_back_side_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
5w4n/processed_oscar_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 250351200.0
num_examples: 69542
download_size: 85253912
dataset_size: 250351200.0
---
# Dataset Card for "processed_oscar_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seongill/Trivia_missing_5 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: has_answer
dtype: bool
splits:
- name: train
num_bytes: 41021199
num_examples: 11313
download_size: 24823157
dataset_size: 41021199
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
fsicoli/common_voice_17_0 | ---
license: cc0-1.0
language:
- ab
- af
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- gn
- ha
- he
- hi
- hsb
- hu
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nl
- oc
- or
- pl
- ps
- pt
- quy
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sq
- sr
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- za
- zgh
- zh
- yo
task_categories:
- automatic-speech-recognition
pretty_name: Common Voice Corpus 17.0
size_categories:
- 100B<n<1T
tags:
- mozilla
- foundation
---
# Dataset Card for Common Voice Corpus 17.0
<!-- Provide a quick summary of the dataset. -->
This dataset is an unofficial version of the Mozilla Common Voice Corpus 17. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
## Languages
```
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):
```
from datasets import load_dataset
cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```
from datasets import load_dataset
cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train", streaming=True)
print(next(iter(cv_17)))
```
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
```
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_17), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_17, batch_sampler=batch_sampler)
```
### Streaming
```
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_17 = load_dataset("fsicoli/common_voice_17_0", "pt", split="train")
dataloader = DataLoader(cv_17, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
### Dataset Structure
Data Instances
A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
### Licensing Information
Public Domain, CC-0
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
---
|
hezarai/lscp-pos-500k | ---
task_categories:
- token-classification
language:
- fa
pretty_name: LSCP Dataset (500k samples version)
---
This is a 500 thousand sample version of the original [LSCP dataset](https://iasbs.ac.ir/~ansari/lscp/) that only contains the text and part-of-speech tags and is used for sequence labeling.
### Citation
```bibtex
@InProceedings{abdikhojasteh:2020:LREC,
author = {Abdi Khojasteh, Hadi and Ansari, Ebrahim and Bohlouli, Mahdi},
title = {LSCP: Enhanced Large Scale Colloquial Persian Language Understanding},
booktitle = {Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020)},
year = {2020}
address = {Marseille, France},
publisher = {European Language Resources Association}
pages = {6323--6327},
url = {https://www.aclweb.org/anthology/2020.lrec-1.776}
}
``` |
TawyeebOS/llama-2-7b-roleplay-script | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 277533
num_examples: 570
download_size: 166147
dataset_size: 277533
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0 | ---
pretty_name: Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vicgalle/SOLAR-13B-Instruct-v1.0](https://huggingface.co/vicgalle/SOLAR-13B-Instruct-v1.0)\
\ 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_vicgalle__SOLAR-13B-Instruct-v1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-13T23:03:16.622437](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0/blob/main/results_2024-01-13T23-03-16.622437.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\": 0.5538159165724174,\n\
\ \"acc_stderr\": 0.03403197325352318,\n \"acc_norm\": 0.5615645038041155,\n\
\ \"acc_norm_stderr\": 0.03477929396757003,\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.01741294198611531,\n \"mc2\": 0.619920564120794,\n\
\ \"mc2_stderr\": 0.01593484036504592\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5435153583617748,\n \"acc_stderr\": 0.01455594976049644,\n\
\ \"acc_norm\": 0.5725255972696246,\n \"acc_norm_stderr\": 0.014456862944650647\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5913164708225453,\n\
\ \"acc_stderr\": 0.004905859114942291,\n \"acc_norm\": 0.7803226448914559,\n\
\ \"acc_norm_stderr\": 0.004131818797713876\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\": 0.4962962962962963,\n\
\ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n\
\ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\
\ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791194,\n\
\ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791194\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\
\ \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.5694444444444444,\n\
\ \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n\
\ \"acc_stderr\": 0.03772446857518026,\n \"acc_norm\": 0.5722543352601156,\n\
\ \"acc_norm_stderr\": 0.03772446857518026\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.03266204299064678,\n\
\ \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.03266204299064678\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\
\ \"acc_stderr\": 0.044895393502706986,\n \"acc_norm\": 0.3508771929824561,\n\
\ \"acc_norm_stderr\": 0.044895393502706986\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36243386243386244,\n \"acc_stderr\": 0.024757473902752042,\n \"\
acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.024757473902752042\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n\
\ \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n\
\ \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6387096774193548,\n\
\ \"acc_stderr\": 0.027327548447957546,\n \"acc_norm\": 0.6387096774193548,\n\
\ \"acc_norm_stderr\": 0.027327548447957546\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.034653044884067945,\n\
\ \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.034653044884067945\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6767676767676768,\n \"acc_stderr\": 0.03332299921070644,\n \"\
acc_norm\": 0.6767676767676768,\n \"acc_norm_stderr\": 0.03332299921070644\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.03027690994517826,\n\
\ \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.03027690994517826\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5025641025641026,\n \"acc_stderr\": 0.025350672979412188,\n\
\ \"acc_norm\": 0.5025641025641026,\n \"acc_norm_stderr\": 0.025350672979412188\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \
\ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03242225027115006,\n \
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03242225027115006\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7504587155963303,\n \"acc_stderr\": 0.01855389762950163,\n \"\
acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.01855389762950163\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4351851851851852,\n \"acc_stderr\": 0.03381200005643524,\n \"\
acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.03381200005643524\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7303921568627451,\n \"acc_stderr\": 0.031145570659486782,\n \"\
acc_norm\": 0.7303921568627451,\n \"acc_norm_stderr\": 0.031145570659486782\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \
\ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6233183856502242,\n\
\ \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.6233183856502242,\n\
\ \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6030534351145038,\n \"acc_stderr\": 0.04291135671009225,\n\
\ \"acc_norm\": 0.6030534351145038,\n \"acc_norm_stderr\": 0.04291135671009225\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6018518518518519,\n\
\ \"acc_stderr\": 0.04732332615978813,\n \"acc_norm\": 0.6018518518518519,\n\
\ \"acc_norm_stderr\": 0.04732332615978813\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6134969325153374,\n \"acc_stderr\": 0.03825825548848607,\n\
\ \"acc_norm\": 0.6134969325153374,\n \"acc_norm_stderr\": 0.03825825548848607\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\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7484035759897829,\n\
\ \"acc_stderr\": 0.01551732236552963,\n \"acc_norm\": 0.7484035759897829,\n\
\ \"acc_norm_stderr\": 0.01551732236552963\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.026483392042098174,\n\
\ \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.026483392042098174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2782122905027933,\n\
\ \"acc_stderr\": 0.014987325439963551,\n \"acc_norm\": 0.2782122905027933,\n\
\ \"acc_norm_stderr\": 0.014987325439963551\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5849673202614379,\n \"acc_stderr\": 0.028213504177824103,\n\
\ \"acc_norm\": 0.5849673202614379,\n \"acc_norm_stderr\": 0.028213504177824103\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6109324758842444,\n\
\ \"acc_stderr\": 0.027690337536485372,\n \"acc_norm\": 0.6109324758842444,\n\
\ \"acc_norm_stderr\": 0.027690337536485372\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6635802469135802,\n \"acc_stderr\": 0.026289734945952922,\n\
\ \"acc_norm\": 0.6635802469135802,\n \"acc_norm_stderr\": 0.026289734945952922\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.41843971631205673,\n \"acc_stderr\": 0.029427994039419998,\n \
\ \"acc_norm\": 0.41843971631205673,\n \"acc_norm_stderr\": 0.029427994039419998\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4172099087353325,\n\
\ \"acc_stderr\": 0.012593959992906429,\n \"acc_norm\": 0.4172099087353325,\n\
\ \"acc_norm_stderr\": 0.012593959992906429\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.029972807170464622,\n\
\ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.029972807170464622\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5424836601307189,\n \"acc_stderr\": 0.02015468571259089,\n \
\ \"acc_norm\": 0.5424836601307189,\n \"acc_norm_stderr\": 0.02015468571259089\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\
\ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\
\ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893783,\n\
\ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893783\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7114427860696517,\n\
\ \"acc_stderr\": 0.03203841040213321,\n \"acc_norm\": 0.7114427860696517,\n\
\ \"acc_norm_stderr\": 0.03203841040213321\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.01741294198611531,\n \"mc2\": 0.619920564120794,\n\
\ \"mc2_stderr\": 0.01593484036504592\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7024467245461721,\n \"acc_stderr\": 0.012849085254614654\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.16603487490523122,\n \
\ \"acc_stderr\": 0.01024981199059352\n }\n}\n```"
repo_url: https://huggingface.co/vicgalle/SOLAR-13B-Instruct-v1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|arc:challenge|25_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|gsm8k|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hellaswag|10_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-13T23-03-16.622437.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- '**/details_harness|winogrande|5_2024-01-13T23-03-16.622437.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-13T23-03-16.622437.parquet'
- config_name: results
data_files:
- split: 2024_01_13T23_03_16.622437
path:
- results_2024-01-13T23-03-16.622437.parquet
- split: latest
path:
- results_2024-01-13T23-03-16.622437.parquet
---
# Dataset Card for Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [vicgalle/SOLAR-13B-Instruct-v1.0](https://huggingface.co/vicgalle/SOLAR-13B-Instruct-v1.0) 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_vicgalle__SOLAR-13B-Instruct-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T23:03:16.622437](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0/blob/main/results_2024-01-13T23-03-16.622437.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": 0.5538159165724174,
"acc_stderr": 0.03403197325352318,
"acc_norm": 0.5615645038041155,
"acc_norm_stderr": 0.03477929396757003,
"mc1": 0.44920440636474906,
"mc1_stderr": 0.01741294198611531,
"mc2": 0.619920564120794,
"mc2_stderr": 0.01593484036504592
},
"harness|arc:challenge|25": {
"acc": 0.5435153583617748,
"acc_stderr": 0.01455594976049644,
"acc_norm": 0.5725255972696246,
"acc_norm_stderr": 0.014456862944650647
},
"harness|hellaswag|10": {
"acc": 0.5913164708225453,
"acc_stderr": 0.004905859114942291,
"acc_norm": 0.7803226448914559,
"acc_norm_stderr": 0.004131818797713876
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4962962962962963,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.4962962962962963,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6052631578947368,
"acc_stderr": 0.039777499346220734,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.039777499346220734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6037735849056604,
"acc_stderr": 0.030102793781791194,
"acc_norm": 0.6037735849056604,
"acc_norm_stderr": 0.030102793781791194
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5722543352601156,
"acc_stderr": 0.03772446857518026,
"acc_norm": 0.5722543352601156,
"acc_norm_stderr": 0.03772446857518026
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4808510638297872,
"acc_stderr": 0.03266204299064678,
"acc_norm": 0.4808510638297872,
"acc_norm_stderr": 0.03266204299064678
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3508771929824561,
"acc_stderr": 0.044895393502706986,
"acc_norm": 0.3508771929824561,
"acc_norm_stderr": 0.044895393502706986
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878151,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878151
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36243386243386244,
"acc_stderr": 0.024757473902752042,
"acc_norm": 0.36243386243386244,
"acc_norm_stderr": 0.024757473902752042
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3492063492063492,
"acc_stderr": 0.04263906892795132,
"acc_norm": 0.3492063492063492,
"acc_norm_stderr": 0.04263906892795132
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6387096774193548,
"acc_stderr": 0.027327548447957546,
"acc_norm": 0.6387096774193548,
"acc_norm_stderr": 0.027327548447957546
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.41379310344827586,
"acc_stderr": 0.034653044884067945,
"acc_norm": 0.41379310344827586,
"acc_norm_stderr": 0.034653044884067945
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885415,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885415
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6767676767676768,
"acc_stderr": 0.03332299921070644,
"acc_norm": 0.6767676767676768,
"acc_norm_stderr": 0.03332299921070644
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.772020725388601,
"acc_stderr": 0.03027690994517826,
"acc_norm": 0.772020725388601,
"acc_norm_stderr": 0.03027690994517826
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5025641025641026,
"acc_stderr": 0.025350672979412188,
"acc_norm": 0.5025641025641026,
"acc_norm_stderr": 0.025350672979412188
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2740740740740741,
"acc_stderr": 0.027195934804085622,
"acc_norm": 0.2740740740740741,
"acc_norm_stderr": 0.027195934804085622
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.03242225027115006,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.03242225027115006
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7504587155963303,
"acc_stderr": 0.01855389762950163,
"acc_norm": 0.7504587155963303,
"acc_norm_stderr": 0.01855389762950163
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4351851851851852,
"acc_stderr": 0.03381200005643524,
"acc_norm": 0.4351851851851852,
"acc_norm_stderr": 0.03381200005643524
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7303921568627451,
"acc_stderr": 0.031145570659486782,
"acc_norm": 0.7303921568627451,
"acc_norm_stderr": 0.031145570659486782
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7341772151898734,
"acc_stderr": 0.02875679962965834,
"acc_norm": 0.7341772151898734,
"acc_norm_stderr": 0.02875679962965834
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6233183856502242,
"acc_stderr": 0.03252113489929188,
"acc_norm": 0.6233183856502242,
"acc_norm_stderr": 0.03252113489929188
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6030534351145038,
"acc_stderr": 0.04291135671009225,
"acc_norm": 0.6030534351145038,
"acc_norm_stderr": 0.04291135671009225
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.71900826446281,
"acc_stderr": 0.04103203830514512,
"acc_norm": 0.71900826446281,
"acc_norm_stderr": 0.04103203830514512
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6018518518518519,
"acc_stderr": 0.04732332615978813,
"acc_norm": 0.6018518518518519,
"acc_norm_stderr": 0.04732332615978813
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6134969325153374,
"acc_stderr": 0.03825825548848607,
"acc_norm": 0.6134969325153374,
"acc_norm_stderr": 0.03825825548848607
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.7961165048543689,
"acc_stderr": 0.039891398595317706,
"acc_norm": 0.7961165048543689,
"acc_norm_stderr": 0.039891398595317706
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.027236013946196697,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.027236013946196697
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7484035759897829,
"acc_stderr": 0.01551732236552963,
"acc_norm": 0.7484035759897829,
"acc_norm_stderr": 0.01551732236552963
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.026483392042098174,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.026483392042098174
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2782122905027933,
"acc_stderr": 0.014987325439963551,
"acc_norm": 0.2782122905027933,
"acc_norm_stderr": 0.014987325439963551
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5849673202614379,
"acc_stderr": 0.028213504177824103,
"acc_norm": 0.5849673202614379,
"acc_norm_stderr": 0.028213504177824103
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6109324758842444,
"acc_stderr": 0.027690337536485372,
"acc_norm": 0.6109324758842444,
"acc_norm_stderr": 0.027690337536485372
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6635802469135802,
"acc_stderr": 0.026289734945952922,
"acc_norm": 0.6635802469135802,
"acc_norm_stderr": 0.026289734945952922
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.41843971631205673,
"acc_stderr": 0.029427994039419998,
"acc_norm": 0.41843971631205673,
"acc_norm_stderr": 0.029427994039419998
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4172099087353325,
"acc_stderr": 0.012593959992906429,
"acc_norm": 0.4172099087353325,
"acc_norm_stderr": 0.012593959992906429
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5808823529411765,
"acc_stderr": 0.029972807170464622,
"acc_norm": 0.5808823529411765,
"acc_norm_stderr": 0.029972807170464622
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5424836601307189,
"acc_stderr": 0.02015468571259089,
"acc_norm": 0.5424836601307189,
"acc_norm_stderr": 0.02015468571259089
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5909090909090909,
"acc_stderr": 0.04709306978661896,
"acc_norm": 0.5909090909090909,
"acc_norm_stderr": 0.04709306978661896
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5102040816326531,
"acc_stderr": 0.03200255347893783,
"acc_norm": 0.5102040816326531,
"acc_norm_stderr": 0.03200255347893783
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7114427860696517,
"acc_stderr": 0.03203841040213321,
"acc_norm": 0.7114427860696517,
"acc_norm_stderr": 0.03203841040213321
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-virology|5": {
"acc": 0.45180722891566266,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.45180722891566266,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7426900584795322,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.7426900584795322,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.44920440636474906,
"mc1_stderr": 0.01741294198611531,
"mc2": 0.619920564120794,
"mc2_stderr": 0.01593484036504592
},
"harness|winogrande|5": {
"acc": 0.7024467245461721,
"acc_stderr": 0.012849085254614654
},
"harness|gsm8k|5": {
"acc": 0.16603487490523122,
"acc_stderr": 0.01024981199059352
}
}
```
## 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. -->
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<!-- 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. -->
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### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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open-llm-leaderboard/details_Chickaboo__ChickaQ-V2-Large-Beta | ---
pretty_name: Evaluation run of Chickaboo/ChickaQ-V2-Large-Beta
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Chickaboo/ChickaQ-V2-Large-Beta](https://huggingface.co/Chickaboo/ChickaQ-V2-Large-Beta)\
\ 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_Chickaboo__ChickaQ-V2-Large-Beta\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T14:33:44.417286](https://huggingface.co/datasets/open-llm-leaderboard/details_Chickaboo__ChickaQ-V2-Large-Beta/blob/main/results_2024-03-21T14-33-44.417286.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\": 0.42089916044598374,\n\
\ \"acc_stderr\": 0.0341321557408357,\n \"acc_norm\": 0.42455256852819306,\n\
\ \"acc_norm_stderr\": 0.03487524247111623,\n \"mc1\": 0.2937576499388005,\n\
\ \"mc1_stderr\": 0.015945068581236614,\n \"mc2\": 0.4385308811464827,\n\
\ \"mc2_stderr\": 0.015284275668463259\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3361774744027304,\n \"acc_stderr\": 0.013804855026205761,\n\
\ \"acc_norm\": 0.3430034129692833,\n \"acc_norm_stderr\": 0.013872423223718167\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4386576379207329,\n\
\ \"acc_stderr\": 0.004952087083128896,\n \"acc_norm\": 0.5786695877315275,\n\
\ \"acc_norm_stderr\": 0.004927631806477557\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3851851851851852,\n\
\ \"acc_stderr\": 0.042039210401562783,\n \"acc_norm\": 0.3851851851851852,\n\
\ \"acc_norm_stderr\": 0.042039210401562783\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\
\ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.47547169811320755,\n \"acc_stderr\": 0.030735822206205608,\n\
\ \"acc_norm\": 0.47547169811320755,\n \"acc_norm_stderr\": 0.030735822206205608\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4027777777777778,\n\
\ \"acc_stderr\": 0.04101405519842425,\n \"acc_norm\": 0.4027777777777778,\n\
\ \"acc_norm_stderr\": 0.04101405519842425\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n\
\ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.43352601156069365,\n\
\ \"acc_stderr\": 0.037786210790920545,\n \"acc_norm\": 0.43352601156069365,\n\
\ \"acc_norm_stderr\": 0.037786210790920545\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.043898699568087785,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.043898699568087785\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.3574468085106383,\n \"acc_stderr\": 0.03132941789476425,\n\
\ \"acc_norm\": 0.3574468085106383,\n \"acc_norm_stderr\": 0.03132941789476425\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.041227371113703316,\n\
\ \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.041227371113703316\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.291005291005291,\n \"acc_stderr\": 0.023393826500484865,\n \"\
acc_norm\": 0.291005291005291,\n \"acc_norm_stderr\": 0.023393826500484865\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\
\ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\
\ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.43870967741935485,\n \"acc_stderr\": 0.02822949732031722,\n \"\
acc_norm\": 0.43870967741935485,\n \"acc_norm_stderr\": 0.02822949732031722\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3054187192118227,\n \"acc_stderr\": 0.03240661565868408,\n \"\
acc_norm\": 0.3054187192118227,\n \"acc_norm_stderr\": 0.03240661565868408\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.593939393939394,\n \"acc_stderr\": 0.03834816355401181,\n\
\ \"acc_norm\": 0.593939393939394,\n \"acc_norm_stderr\": 0.03834816355401181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5404040404040404,\n \"acc_stderr\": 0.035507024651313425,\n \"\
acc_norm\": 0.5404040404040404,\n \"acc_norm_stderr\": 0.035507024651313425\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.5595854922279793,\n \"acc_stderr\": 0.03582724530036094,\n\
\ \"acc_norm\": 0.5595854922279793,\n \"acc_norm_stderr\": 0.03582724530036094\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.30512820512820515,\n \"acc_stderr\": 0.023346335293325887,\n\
\ \"acc_norm\": 0.30512820512820515,\n \"acc_norm_stderr\": 0.023346335293325887\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2222222222222222,\n \"acc_stderr\": 0.025348097468097828,\n \
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.025348097468097828\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.030388353551886838,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.030388353551886838\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2119205298013245,\n \"acc_stderr\": 0.033367670865679766,\n \"\
acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.033367670865679766\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5064220183486239,\n \"acc_stderr\": 0.021435554820013077,\n \"\
acc_norm\": 0.5064220183486239,\n \"acc_norm_stderr\": 0.021435554820013077\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2037037037037037,\n \"acc_stderr\": 0.027467401804058,\n \"acc_norm\"\
: 0.2037037037037037,\n \"acc_norm_stderr\": 0.027467401804058\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.47549019607843135,\n\
\ \"acc_stderr\": 0.03505093194348798,\n \"acc_norm\": 0.47549019607843135,\n\
\ \"acc_norm_stderr\": 0.03505093194348798\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.620253164556962,\n \"acc_stderr\": 0.031591887529658504,\n\
\ \"acc_norm\": 0.620253164556962,\n \"acc_norm_stderr\": 0.031591887529658504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.47085201793721976,\n\
\ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.47085201793721976,\n\
\ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n\
\ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.044492703500683836,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.044492703500683836\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.49074074074074076,\n\
\ \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n\
\ \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3987730061349693,\n \"acc_stderr\": 0.03847021420456025,\n\
\ \"acc_norm\": 0.3987730061349693,\n \"acc_norm_stderr\": 0.03847021420456025\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6213592233009708,\n \"acc_stderr\": 0.04802694698258973,\n\
\ \"acc_norm\": 0.6213592233009708,\n \"acc_norm_stderr\": 0.04802694698258973\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7051282051282052,\n\
\ \"acc_stderr\": 0.02987257770889119,\n \"acc_norm\": 0.7051282051282052,\n\
\ \"acc_norm_stderr\": 0.02987257770889119\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5389527458492975,\n\
\ \"acc_stderr\": 0.017825621793239012,\n \"acc_norm\": 0.5389527458492975,\n\
\ \"acc_norm_stderr\": 0.017825621793239012\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.026918645383239004,\n\
\ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.026918645383239004\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.028629916715693413,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.028629916715693413\n \
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3954983922829582,\n\
\ \"acc_stderr\": 0.027770918531427834,\n \"acc_norm\": 0.3954983922829582,\n\
\ \"acc_norm_stderr\": 0.027770918531427834\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.02774431344337654,\n\
\ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.02774431344337654\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2872340425531915,\n \"acc_stderr\": 0.026992199173064356,\n \
\ \"acc_norm\": 0.2872340425531915,\n \"acc_norm_stderr\": 0.026992199173064356\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.33572359843546284,\n\
\ \"acc_stderr\": 0.01206130415766461,\n \"acc_norm\": 0.33572359843546284,\n\
\ \"acc_norm_stderr\": 0.01206130415766461\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3161764705882353,\n \"acc_stderr\": 0.028245687391462923,\n\
\ \"acc_norm\": 0.3161764705882353,\n \"acc_norm_stderr\": 0.028245687391462923\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.39215686274509803,\n \"acc_stderr\": 0.01975172650876263,\n \
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.01975172650876263\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5454545454545454,\n\
\ \"acc_stderr\": 0.04769300568972745,\n \"acc_norm\": 0.5454545454545454,\n\
\ \"acc_norm_stderr\": 0.04769300568972745\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.4530612244897959,\n \"acc_stderr\": 0.03186785930004129,\n\
\ \"acc_norm\": 0.4530612244897959,\n \"acc_norm_stderr\": 0.03186785930004129\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6119402985074627,\n\
\ \"acc_stderr\": 0.034457899643627506,\n \"acc_norm\": 0.6119402985074627,\n\
\ \"acc_norm_stderr\": 0.034457899643627506\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39759036144578314,\n\
\ \"acc_stderr\": 0.03809973084540219,\n \"acc_norm\": 0.39759036144578314,\n\
\ \"acc_norm_stderr\": 0.03809973084540219\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.52046783625731,\n \"acc_stderr\": 0.0383161053282193,\n\
\ \"acc_norm\": 0.52046783625731,\n \"acc_norm_stderr\": 0.0383161053282193\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2937576499388005,\n\
\ \"mc1_stderr\": 0.015945068581236614,\n \"mc2\": 0.4385308811464827,\n\
\ \"mc2_stderr\": 0.015284275668463259\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.590370955011839,\n \"acc_stderr\": 0.013821049109655465\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18271417740712662,\n \
\ \"acc_stderr\": 0.010644258206326244\n }\n}\n```"
repo_url: https://huggingface.co/Chickaboo/ChickaQ-V2-Large-Beta
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|arc:challenge|25_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|gsm8k|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hellaswag|10_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-33-44.417286.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T14-33-44.417286.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- '**/details_harness|winogrande|5_2024-03-21T14-33-44.417286.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T14-33-44.417286.parquet'
- config_name: results
data_files:
- split: 2024_03_21T14_33_44.417286
path:
- results_2024-03-21T14-33-44.417286.parquet
- split: latest
path:
- results_2024-03-21T14-33-44.417286.parquet
---
# Dataset Card for Evaluation run of Chickaboo/ChickaQ-V2-Large-Beta
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Chickaboo/ChickaQ-V2-Large-Beta](https://huggingface.co/Chickaboo/ChickaQ-V2-Large-Beta) 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_Chickaboo__ChickaQ-V2-Large-Beta",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T14:33:44.417286](https://huggingface.co/datasets/open-llm-leaderboard/details_Chickaboo__ChickaQ-V2-Large-Beta/blob/main/results_2024-03-21T14-33-44.417286.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": 0.42089916044598374,
"acc_stderr": 0.0341321557408357,
"acc_norm": 0.42455256852819306,
"acc_norm_stderr": 0.03487524247111623,
"mc1": 0.2937576499388005,
"mc1_stderr": 0.015945068581236614,
"mc2": 0.4385308811464827,
"mc2_stderr": 0.015284275668463259
},
"harness|arc:challenge|25": {
"acc": 0.3361774744027304,
"acc_stderr": 0.013804855026205761,
"acc_norm": 0.3430034129692833,
"acc_norm_stderr": 0.013872423223718167
},
"harness|hellaswag|10": {
"acc": 0.4386576379207329,
"acc_stderr": 0.004952087083128896,
"acc_norm": 0.5786695877315275,
"acc_norm_stderr": 0.004927631806477557
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3851851851851852,
"acc_stderr": 0.042039210401562783,
"acc_norm": 0.3851851851851852,
"acc_norm_stderr": 0.042039210401562783
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.47547169811320755,
"acc_stderr": 0.030735822206205608,
"acc_norm": 0.47547169811320755,
"acc_norm_stderr": 0.030735822206205608
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4027777777777778,
"acc_stderr": 0.04101405519842425,
"acc_norm": 0.4027777777777778,
"acc_norm_stderr": 0.04101405519842425
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932269,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.43352601156069365,
"acc_stderr": 0.037786210790920545,
"acc_norm": 0.43352601156069365,
"acc_norm_stderr": 0.037786210790920545
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.043898699568087785,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.043898699568087785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3574468085106383,
"acc_stderr": 0.03132941789476425,
"acc_norm": 0.3574468085106383,
"acc_norm_stderr": 0.03132941789476425
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.04142439719489362,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.04142439719489362
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.42758620689655175,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.42758620689655175,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.291005291005291,
"acc_stderr": 0.023393826500484865,
"acc_norm": 0.291005291005291,
"acc_norm_stderr": 0.023393826500484865
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.04104947269903394,
"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.04104947269903394
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.43870967741935485,
"acc_stderr": 0.02822949732031722,
"acc_norm": 0.43870967741935485,
"acc_norm_stderr": 0.02822949732031722
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3054187192118227,
"acc_stderr": 0.03240661565868408,
"acc_norm": 0.3054187192118227,
"acc_norm_stderr": 0.03240661565868408
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.593939393939394,
"acc_stderr": 0.03834816355401181,
"acc_norm": 0.593939393939394,
"acc_norm_stderr": 0.03834816355401181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5404040404040404,
"acc_stderr": 0.035507024651313425,
"acc_norm": 0.5404040404040404,
"acc_norm_stderr": 0.035507024651313425
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5595854922279793,
"acc_stderr": 0.03582724530036094,
"acc_norm": 0.5595854922279793,
"acc_norm_stderr": 0.03582724530036094
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.30512820512820515,
"acc_stderr": 0.023346335293325887,
"acc_norm": 0.30512820512820515,
"acc_norm_stderr": 0.023346335293325887
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.025348097468097828,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.025348097468097828
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.030388353551886838,
"acc_norm": 0.3235294117647059,
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```
## Dataset Details
### Dataset Description
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### Source Data
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Pacoch/postglacial-shaded-relief | ---
license: mit
task_categories:
- image-classification
- feature-extraction
tags:
- geomorphology
- image
- png
pretty_name: >-
Shaded relief image dataset for geomorphological studies of Polish postglacial
landscape
size_categories:
- 1M<n<10M
---
## Shaded relief image dataset for geomorphological studies of Polish postglacial landscape
This dataset contains a list of 138 png images of shaded relief cut into the 128x128 arrays. The area that the dataset covers is compacted within the
two main geomorphological spheres in Poland - postglacial denuded and nondenuded landscape. Arrays representing one of two categories are labeled accordingly.
Shaded relief scene has been calculated with exposition and sunlight paramiters set to direct south (thus, in this case - 180 degrees). |
scholl99/absa-restaurant-processed-v1 | ---
dataset_info:
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dataset_size: 3405084
configs:
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data_files:
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path: data/train-*
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path: data/test-*
---
|
eurecom-ds/scoresdeve_activations_multi_dsprites | ---
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---
|
vishnu42574/mahesh_21images | ---
dataset_info:
features:
- name: image
dtype: image
- name: ' text'
dtype: string
splits:
- name: train
num_bytes: 1719536.0
num_examples: 21
download_size: 1521332
dataset_size: 1719536.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Graphcore/gqa-lxmert | ---
language:
- en
license:
- cc-by-4.0
---
|
Nadav/pixel_glue_qnli_low_noise | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: validation
num_bytes: 198727706.125
num_examples: 5463
download_size: 198486814
dataset_size: 198727706.125
---
# Dataset Card for "pixel_glue_qnli_low_noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChuGyouk/openorca_niv_filtered | ---
dataset_info:
features:
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dtype: string
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dtype: string
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splits:
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num_bytes: 435523340.7814285
num_examples: 292532
download_size: 214383098
dataset_size: 435523340.7814285
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nyaa97/art_sr_vc1_test | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: id1
dtype: string
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dtype: int64
splits:
- name: train
num_bytes: 17394964513.28
num_examples: 37611
download_size: 4146347060
dataset_size: 17394964513.28
---
|
Aerobotics/belly-angle-selection-in-office-apples | ---
dataset_info:
features:
- name: image
dtype: image
- name: Index
dtype: int64
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dtype: float64
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dtype: bool
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dtype: float64
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dtype: float64
splits:
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num_examples: 118
download_size: 1816276
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jonathan-roberts1/AID_MultiLabel | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
sequence:
class_label:
names:
'0': airplane
'1': bare soil
'2': buildings
'3': cars
'4': chaparral
'5': court
'6': dock
'7': field
'8': grass
'9': mobile home
'10': pavement
'11': sand
'12': sea
'13': ship
'14': tanks
'15': trees
'16': water
splits:
- name: train
num_bytes: 278244208
num_examples: 3000
download_size: 278126146
dataset_size: 278244208
license: cc0-1.0
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "AID_MultiLabel"
## Dataset Description
- **Paper:** [AID: A benchmark data set for performance evaluation of aerial scene classification](https://ieeexplore.ieee.org/iel7/36/4358825/07907303.pdf)
- **Paper:** [Relation Network for Multi-label Aerial Image Classification]()
### Licensing Information
CC0: Public Domain
## Citation Information
Imagery:
[AID: A benchmark data set for performance evaluation of aerial scene classification](https://ieeexplore.ieee.org/iel7/36/4358825/07907303.pdf)
Multilabels:
[Relation Network for Multi-label Aerial Image Classification](https://ieeexplore.ieee.org/iel7/36/4358825/08986556.pdf)
```
@article{xia2017aid,
title = {AID: A benchmark data set for performance evaluation of aerial scene classification},
author = {Xia, Gui-Song and Hu, Jingwen and Hu, Fan and Shi, Baoguang and Bai, Xiang and Zhong, Yanfei and Zhang, Liangpei and Lu, Xiaoqiang},
year = 2017,
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
volume = 55,
number = 7,
pages = {3965--3981}
}
@article{hua2019relation,
title = {Relation Network for Multi-label Aerial Image Classification},
author = {Hua, Yuansheng and Mou, Lichao and Zhu, Xiao Xiang},
year = {DOI:10.1109/TGRS.2019.2963364},
journal = {IEEE Transactions on Geoscience and Remote Sensing}
}
``` |
0x7o/oasst2-best-ru | ---
dataset_info:
features:
- name: texts
dtype: string
splits:
- name: train
num_bytes: 3746950
num_examples: 1246
download_size: 1806207
dataset_size: 3746950
license: apache-2.0
task_categories:
- conversational
- text-generation
language:
- ru
size_categories:
- 1K<n<10K
---
# Dataset Card for "oasst2-best-ru"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ajibawa-2023/Education-College-Students | ---
license: apache-2.0
language:
- en
tags:
- Education
- College Students
- Students
- Knowledge
---
Details coming soon!! |
noahshinn/cifar100_2_to_100 | ---
configs:
- config_name: default
data_files:
- split: cifar100_2
path: data/cifar100_2-*
- split: cifar100_3
path: data/cifar100_3-*
- split: cifar100_4
path: data/cifar100_4-*
- split: cifar100_5
path: data/cifar100_5-*
- split: cifar100_6
path: data/cifar100_6-*
- split: cifar100_7
path: data/cifar100_7-*
- split: cifar100_8
path: data/cifar100_8-*
- split: cifar100_9
path: data/cifar100_9-*
- split: cifar100_10
path: data/cifar100_10-*
- split: cifar100_11
path: data/cifar100_11-*
- split: cifar100_12
path: data/cifar100_12-*
- split: cifar100_13
path: data/cifar100_13-*
- split: cifar100_14
path: data/cifar100_14-*
- split: cifar100_15
path: data/cifar100_15-*
- split: cifar100_16
path: data/cifar100_16-*
- split: cifar100_17
path: data/cifar100_17-*
- split: cifar100_18
path: data/cifar100_18-*
- split: cifar100_19
path: data/cifar100_19-*
- split: cifar100_20
path: data/cifar100_20-*
- split: cifar100_21
path: data/cifar100_21-*
- split: cifar100_22
path: data/cifar100_22-*
- split: cifar100_23
path: data/cifar100_23-*
- split: cifar100_24
path: data/cifar100_24-*
- split: cifar100_25
path: data/cifar100_25-*
- split: cifar100_26
path: data/cifar100_26-*
- split: cifar100_27
path: data/cifar100_27-*
- split: cifar100_28
path: data/cifar100_28-*
- split: cifar100_29
path: data/cifar100_29-*
- split: cifar100_30
path: data/cifar100_30-*
- split: cifar100_31
path: data/cifar100_31-*
- split: cifar100_32
path: data/cifar100_32-*
- split: cifar100_33
path: data/cifar100_33-*
- split: cifar100_34
path: data/cifar100_34-*
- split: cifar100_35
path: data/cifar100_35-*
- split: cifar100_36
path: data/cifar100_36-*
- split: cifar100_37
path: data/cifar100_37-*
- split: cifar100_38
path: data/cifar100_38-*
- split: cifar100_39
path: data/cifar100_39-*
- split: cifar100_40
path: data/cifar100_40-*
- split: cifar100_41
path: data/cifar100_41-*
- split: cifar100_42
path: data/cifar100_42-*
- split: cifar100_43
path: data/cifar100_43-*
- split: cifar100_44
path: data/cifar100_44-*
- split: cifar100_45
path: data/cifar100_45-*
- split: cifar100_46
path: data/cifar100_46-*
- split: cifar100_47
path: data/cifar100_47-*
- split: cifar100_48
path: data/cifar100_48-*
- split: cifar100_49
path: data/cifar100_49-*
- split: cifar100_50
path: data/cifar100_50-*
- split: cifar100_51
path: data/cifar100_51-*
- split: cifar100_52
path: data/cifar100_52-*
- split: cifar100_53
path: data/cifar100_53-*
- split: cifar100_54
path: data/cifar100_54-*
- split: cifar100_55
path: data/cifar100_55-*
- split: cifar100_56
path: data/cifar100_56-*
- split: cifar100_57
path: data/cifar100_57-*
- split: cifar100_58
path: data/cifar100_58-*
- split: cifar100_59
path: data/cifar100_59-*
- split: cifar100_60
path: data/cifar100_60-*
- split: cifar100_61
path: data/cifar100_61-*
- split: cifar100_62
path: data/cifar100_62-*
- split: cifar100_63
path: data/cifar100_63-*
- split: cifar100_64
path: data/cifar100_64-*
- split: cifar100_65
path: data/cifar100_65-*
- split: cifar100_66
path: data/cifar100_66-*
- split: cifar100_67
path: data/cifar100_67-*
- split: cifar100_68
path: data/cifar100_68-*
- split: cifar100_69
path: data/cifar100_69-*
- split: cifar100_70
path: data/cifar100_70-*
- split: cifar100_71
path: data/cifar100_71-*
- split: cifar100_72
path: data/cifar100_72-*
- split: cifar100_73
path: data/cifar100_73-*
- split: cifar100_74
path: data/cifar100_74-*
- split: cifar100_75
path: data/cifar100_75-*
- split: cifar100_76
path: data/cifar100_76-*
- split: cifar100_77
path: data/cifar100_77-*
- split: cifar100_78
path: data/cifar100_78-*
- split: cifar100_79
path: data/cifar100_79-*
- split: cifar100_80
path: data/cifar100_80-*
- split: cifar100_81
path: data/cifar100_81-*
- split: cifar100_82
path: data/cifar100_82-*
- split: cifar100_83
path: data/cifar100_83-*
- split: cifar100_84
path: data/cifar100_84-*
- split: cifar100_85
path: data/cifar100_85-*
- split: cifar100_86
path: data/cifar100_86-*
- split: cifar100_87
path: data/cifar100_87-*
- split: cifar100_88
path: data/cifar100_88-*
- split: cifar100_89
path: data/cifar100_89-*
- split: cifar100_90
path: data/cifar100_90-*
- split: cifar100_91
path: data/cifar100_91-*
- split: cifar100_92
path: data/cifar100_92-*
- split: cifar100_93
path: data/cifar100_93-*
- split: cifar100_94
path: data/cifar100_94-*
- split: cifar100_95
path: data/cifar100_95-*
- split: cifar100_96
path: data/cifar100_96-*
- split: cifar100_97
path: data/cifar100_97-*
- split: cifar100_98
path: data/cifar100_98-*
- split: cifar100_99
path: data/cifar100_99-*
- split: cifar100_100
path: data/cifar100_100-*
dataset_info:
features:
- name: img
dtype: image
- name: fine_label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
'7': beetle
'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': cra
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
'47': maple_tree
'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
'62': poppy
'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
'74': shrew
'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
- name: coarse_label
dtype:
class_label:
names:
'0': aquatic_mammals
'1': fish
'2': flowers
'3': food_containers
'4': fruit_and_vegetables
'5': household_electrical_devices
'6': household_furniture
'7': insects
'8': large_carnivores
'9': large_man-made_outdoor_things
'10': large_natural_outdoor_scenes
'11': large_omnivores_and_herbivores
'12': medium_mammals
'13': non-insect_invertebrates
'14': people
'15': reptiles
'16': small_mammals
'17': trees
'18': vehicles_1
'19': vehicles_2
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---
# Dataset Card for "cifar100_2_to_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
royboy0416/ko-alpaca | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- ko
---
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json |
henryscheible/coco_val2014_tiny | ---
dataset_info:
features:
- name: image
dtype: image
- name: captions
dtype: string
splits:
- name: validation
num_bytes: 5874023.0
num_examples: 40
download_size: 5861043
dataset_size: 5874023.0
---
# Dataset Card for "coco_val2014_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EdBianchi/SmokeFire | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fire
'1': Normal
'2': Smoke
splits:
- name: train
num_bytes: 166216842.46
num_examples: 6060
- name: test
num_bytes: 89193578.0
num_examples: 759
- name: validation
num_bytes: 75838884.0
num_examples: 756
download_size: 890673915
dataset_size: 331249304.46000004
---
# Dataset Card for "SmokeFire"
Wildfires or forest fires are unpredictable catastrophic and destructive events that affect rural areas. The impact of these events affects both vegetation and wildlife.
This dataset can be used to train networks able to detect smoke and/or fire in forest environments.
## Data Sources & Description
- **This dataset consist of sample from two datasets hosted on Kaggle:**
- [Forest Fire](https://www.kaggle.com/datasets/kutaykutlu/forest-fire?select=train_fire)
- [Forest Fire Images](https://www.kaggle.com/datasets/mohnishsaiprasad/forest-fire-images)
- **The datasets consist of:**
- 2525 **Fire** samples
- 2525 **Smoke** samples
- 2525 **Normal** samples
- **The dataset is splitted into:**
- Train Set -> 6060 samples
- Validation Set -> 756 samples
- Test Set -> 759 samples
|
Njojo/roop | ---
license: llama2
---
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-73237a-43943145136 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: mrm8488/flan-t5-large-finetuned-openai-summarize_from_feedback
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: mrm8488/flan-t5-large-finetuned-openai-summarize_from_feedback
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jayeeap](https://huggingface.co/jayeeap) for evaluating this model. |
RIW/small_coco_test_50_1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: url
dtype: string
- name: key
dtype: string
- name: status
dtype: string
- name: error_message
dtype: 'null'
- name: width
dtype: int64
- name: height
dtype: int64
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: exif
dtype: string
- name: sha256
dtype: string
- name: watermark
dtype: bool
splits:
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num_bytes: 778069380.76
num_examples: 9485
- name: validation
num_bytes: 885003521.915
num_examples: 8965
download_size: 368439602
dataset_size: 1663072902.675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
kenhktsui/simple_wikipedia_LM_quality_score_v1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: quality_score_v1
dtype: float64
splits:
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num_bytes: 228625682
num_examples: 225984
- name: test
num_bytes: 5815940
num_examples: 5943
- name: validation
num_bytes: 6369557
num_examples: 5949
download_size: 140637963
dataset_size: 240811179
task_categories:
- text-generation
language:
- en
---
# Dataset Card for "simple_wikipedia_LM_quality_score_v1"
Adding quality score v1 to [pszemraj/simple_wikipedia_LM](https://huggingface.co/datasets/pszemraj/simple_wikipedia_LM)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_OpenPipe__mistral-ft-optimized-1218 | ---
pretty_name: Evaluation run of OpenPipe/mistral-ft-optimized-1218
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)\
\ 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_OpenPipe__mistral-ft-optimized-1218\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-23T16:59:03.056117](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenPipe__mistral-ft-optimized-1218/blob/main/results_2023-12-23T16-59-03.056117.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\": 0.6540752717223282,\n\
\ \"acc_stderr\": 0.03195973524820356,\n \"acc_norm\": 0.6539909026028121,\n\
\ \"acc_norm_stderr\": 0.03262037928018462,\n \"mc1\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.5947867444067919,\n\
\ \"mc2_stderr\": 0.015138536405992413\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6518771331058021,\n \"acc_stderr\": 0.01392100859517934,\n\
\ \"acc_norm\": 0.6791808873720137,\n \"acc_norm_stderr\": 0.013640943091946533\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6730730930093607,\n\
\ \"acc_stderr\": 0.004681316064444416,\n \"acc_norm\": 0.8625771758613822,\n\
\ \"acc_norm_stderr\": 0.0034358953866922546\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n\
\ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\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\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\
\ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n\
\ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n\
\ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782655,\n \"\
acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782655\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\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\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\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\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8587155963302753,\n \"acc_stderr\": 0.014933868987028075,\n \"\
acc_norm\": 0.8587155963302753,\n \"acc_norm_stderr\": 0.014933868987028075\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\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\": 0.025530100460233494,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\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\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\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\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3854748603351955,\n\
\ \"acc_stderr\": 0.01627792703963819,\n \"acc_norm\": 0.3854748603351955,\n\
\ \"acc_norm_stderr\": 0.01627792703963819\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.024954184324879905,\n\
\ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.024954184324879905\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\
\ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4706649282920469,\n\
\ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\
\ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\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\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.43084455324357407,\n\
\ \"mc1_stderr\": 0.017335272475332366,\n \"mc2\": 0.5947867444067919,\n\
\ \"mc2_stderr\": 0.015138536405992413\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8074191002367798,\n \"acc_stderr\": 0.011082538847491906\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7225170583775588,\n \
\ \"acc_stderr\": 0.01233344758104755\n }\n}\n```"
repo_url: https://huggingface.co/OpenPipe/mistral-ft-optimized-1218
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|arc:challenge|25_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|gsm8k|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hellaswag|10_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T16-59-03.056117.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-23T16-59-03.056117.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- '**/details_harness|winogrande|5_2023-12-23T16-59-03.056117.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-23T16-59-03.056117.parquet'
- config_name: results
data_files:
- split: 2023_12_23T16_59_03.056117
path:
- results_2023-12-23T16-59-03.056117.parquet
- split: latest
path:
- results_2023-12-23T16-59-03.056117.parquet
---
# Dataset Card for Evaluation run of OpenPipe/mistral-ft-optimized-1218
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) 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_OpenPipe__mistral-ft-optimized-1218",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-23T16:59:03.056117](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenPipe__mistral-ft-optimized-1218/blob/main/results_2023-12-23T16-59-03.056117.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": 0.6540752717223282,
"acc_stderr": 0.03195973524820356,
"acc_norm": 0.6539909026028121,
"acc_norm_stderr": 0.03262037928018462,
"mc1": 0.43084455324357407,
"mc1_stderr": 0.017335272475332366,
"mc2": 0.5947867444067919,
"mc2_stderr": 0.015138536405992413
},
"harness|arc:challenge|25": {
"acc": 0.6518771331058021,
"acc_stderr": 0.01392100859517934,
"acc_norm": 0.6791808873720137,
"acc_norm_stderr": 0.013640943091946533
},
"harness|hellaswag|10": {
"acc": 0.6730730930093607,
"acc_stderr": 0.004681316064444416,
"acc_norm": 0.8625771758613822,
"acc_norm_stderr": 0.0034358953866922546
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7236842105263158,
"acc_stderr": 0.03639057569952928,
"acc_norm": 0.7236842105263158,
"acc_norm_stderr": 0.03639057569952928
},
"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": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7916666666666666,
"acc_stderr": 0.033961162058453336,
"acc_norm": 0.7916666666666666,
"acc_norm_stderr": 0.033961162058453336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.032081157507886836,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.032081157507886836
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.02548718714785938,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.02548718714785938
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782655,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782655
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.02886977846026705,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.02886977846026705
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033456,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6932773109243697,
"acc_stderr": 0.02995382389188704,
"acc_norm": 0.6932773109243697,
"acc_norm_stderr": 0.02995382389188704
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8587155963302753,
"acc_stderr": 0.014933868987028075,
"acc_norm": 0.8587155963302753,
"acc_norm_stderr": 0.014933868987028075
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.03407632093854051,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.03407632093854051
},
"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": 0.810126582278481,
"acc_stderr": 0.025530100460233494,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233494
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.03498149385462472,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
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"acc_norm_stderr": 0.03695980128098824
},
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"acc": 0.7962962962962963,
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"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
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"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7450980392156863,
"acc_norm_stderr": 0.024954184324879905
},
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"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.012748238397365549
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"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.02833295951403121
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.01885008469646872
},
"harness|hendrycksTest-public_relations|5": {
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"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"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": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.029913127232368036,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.029913127232368036
},
"harness|truthfulqa:mc|0": {
"mc1": 0.43084455324357407,
"mc1_stderr": 0.017335272475332366,
"mc2": 0.5947867444067919,
"mc2_stderr": 0.015138536405992413
},
"harness|winogrande|5": {
"acc": 0.8074191002367798,
"acc_stderr": 0.011082538847491906
},
"harness|gsm8k|5": {
"acc": 0.7225170583775588,
"acc_stderr": 0.01233344758104755
}
}
```
## 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
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[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. -->
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### 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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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mweiss/mnist_ambiguous | ---
license: cc-by-sa-3.0
task_categories:
- image-classification
language:
- en
pretty_name: mnist_ambigous
size_categories:
- 10K<n<100K
source_datasets:
- extended|mnist
annotations_creators:
- machine-generated
---
# Mnist-Ambiguous
This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
Additionally, the following features are exposed for your convenience:
- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(0)=0.54, p(5)=0.46`
- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- `test`: 10'000 ambiguous images
- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal mnist test set by LeCun et. al.,
- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`.
Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset,
including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
### Paper
Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
Citation:
```
@misc{https://doi.org/10.48550/arxiv.2207.10495,
doi = {10.48550/ARXIV.2207.10495},
url = {https://arxiv.org/abs/2207.10495},
author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
publisher = {arXiv},
year = {2022}
}
```
### License
As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
|
Deysi/spanish-chinese | ---
dataset_info:
features:
- name: spanish
dtype: string
- name: chinese
dtype: string
splits:
- name: train
num_bytes: 3048111118.5537825
num_examples: 9092567
- name: test
num_bytes: 762027863.4462174
num_examples: 2273142
download_size: 2473454462
dataset_size: 3810138982
license: apache-2.0
task_categories:
- translation
language:
- es
- zh
tags:
- language
- translation
- traducción
- idiomas
- chino
- chinese
- español
- spanish
- Universidad de La Rioja
pretty_name: Spanish and Chinese aligned sentences
size_categories:
- 10M<n<100M
---
# Dataset Card for "spanish-chinese"
All sensences extracted from the United Nations Parallel Corpus v1.0.
The parallel corpus consists of manually translated United Nations documents for the six
official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
The corpus is freely available for download at https://conferences.unite.un.org/UNCorpus
under the terms of use outlined in the attached DISCLAIMER.
The original individual documents are available at the United Nations Official Document
System (ODS) at http://ods.un.org.
Reference:
Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel
Corpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016. |
Cris1907/marIA-UG | ---
license: apache-2.0
---
|
Kalamazooter/GeminiPhiDutch | ---
dataset_info:
features:
- name: type
dtype: string
- name: text
dtype: string
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- nl
tags:
- synthetic
- textbooks
- dutch
---
# Dataset Card
This dataset consists of synthetic Dutch data, in multiple styles/augmentation methods, categorized by the "type" row, this data has been filtered using [Kalamazooter/DutchDatasetCleaner_Bertje](https://huggingface.co/Kalamazooter/DutchDatasetCleaner_Bertje).
The main motivation for creating this dataset is the lack of high-quality Dutch datasets, and the fact that existing Dutch datasets have a much smaller amount of code included compared to their English/Multilingual counterparts.
### Dataset Description
- **Curated by:** Kalamazooter
- **Language(s) (NLP):** Dutch
- **License:** cc-by-nc-4.0
### Direct Use
The dataset could be used for pretraining a (rather small) Dutch model, [Kalamazooter/KleineGeitje_Alpha](https://huggingface.co/Kalamazooter/KleineGeitje_Alpha) for example was trained on a very early and much smaller version of this dataset as a test run. From scratch using the [yhavinga/dutch-llama-tokenizer](https://huggingface.co/yhavinga/dutch-llama-tokenizer) from yhavinga, I am currently working on training a slightly larger model on the entire dataset as an experiment.
Smaller subsets, like the translated math_orca or Syllabus could be used to tune an existing model.
### Out-of-Scope Use
This dataset might have some biases, either from Google or for example transcripts that have been used, also finetuning already finetuned models could become messy as Gemini's go-to formatting is way more trigger happy on using newlines and markdown than other models, which is also reflected in the generated text in the dataset.
### Data Instances
Some examples of the formats often found in the dataset:
**Dialog:**
```text
**Tim:** Laura, ik heb erover nagedacht om een kluizenaarsleven te leiden.
**Laura:** Een kluizenaarsleven? Maar waarom?
**Tim:** Ik ben moe van de drukte en het lawaai van de stad. Ik wil een plek waar ik in vrede kan zijn, omringd door de natuur.
**Laura:** Ik begrijp het. Het leven kan overweldigend zijn. Maar ben je er zeker van dat een kluizenaarsleven de juiste beslissing is?
**Tim:** Ja, ik denk het wel. Ik heb altijd al van eenvoud gehouden. Ik wil me richten op het essentiële in het leven, zonder de afleidingen van de moderne wereld.
**Laura:** Maar wat met je vrienden en familie? Ga je ze niet missen?
**Tim:** Natuurlijk, maar ik denk dat ze me zullen begrijpen. Ze weten dat ik altijd op zoek ben naar innerlijke rust.
**Laura:** Maar hoe ga je overleven? Heb je genoeg vaardigheden om voor jezelf te zorgen?
**Tim:** Ik heb al wat onderzoek gedaan. Ik kan leren jagen, vissen en een moestuin aanleggen. Ik heb ook wat geld gespaard, dus ik kan in het begin wat benodigdheden kopen.
**Laura:** Ik maak me nog steeds zorgen over je, Tim. Een kluizenaarsleven kan eenzaam en gevaarlijk zijn.
**Tim:** Ik waardeer je bezorgdheid, Laura. Maar ik ben vastbesloten om dit te doen. Ik denk dat het me de vrede en voldoening zal geven waar ik naar op zoek ben.
**Laura:** Nou, als je zeker bent, dan steun ik je. Maar beloof me dat je me op de hoogte houdt.
**Tim:** Dat zal ik zeker doen, Laura. Bedankt voor je begrip.
```
**Syllabus:**:
```text
## Indicaties voor dermatologische chirurgie
Dermatologische chirurgie is een specialisatie binnen de dermatologie die zich richt op de chirurgische behandeling van huidaandoeningen. Dermatologische chirurgen kunnen verschillende soorten operaties uitvoeren, waaronder:
* **Excisies:** Het verwijderen van goedaardige of kwaadaardige huidtumoren, zoals basaliomen, plaveiselcelcarcinomen en melanomen.
* **Debridement:** Het verwijderen van dood of geïnfecteerd weefsel van de huid.
* **Transplantaties:** Het overbrengen van huid van een gezond deel van het lichaam naar een beschadigd deel van de huid.
* **Laserchirurgie:** Het gebruik van een laser om huidaandoeningen te behandelen, zoals acne, littekens en tatoeages.
* **Cryochirurgie:** Het gebruik van vloeibare stikstof om huidaandoeningen te behandelen, zoals wratten en actinische keratosen.
## Voorbeelden van huidaandoeningen die met dermatologische chirurgie kunnen worden behandeld:
* **Huidkanker:** Dermatologische chirurgen kunnen verschillende soorten huidkanker behandelen, waaronder basaliomen, plaveiselcelcarcinomen en melanomen.
* **Benigne huidtumoren:** Dermatologische chirurgen kunnen verschillende soorten benigne huidtumoren behandelen, waaronder lipomen, wratten en cysten.
* **Infecties van de huid:** Dermatologische chirurgen kunnen verschillende soorten infecties van de huid behandelen, waaronder abcessen, cellulitis en erysipelas.
* **Littekens:** Dermatologische chirurgen kunnen verschillende soorten littekens behandelen, waaronder hypertrofische littekens, keloïden en acne littekens.
* **Tatoeages:** Dermatologische chirurgen kunnen tatoeages verwijderen met behulp van laserchirurgie of dermabrasie.
## Indicaties voor dermatologische chirurgie:
Er zijn verschillende indicaties voor dermatologische chirurgie, waaronder:
* De huidziekte is niet te behandelen met niet-chirurgische methoden.
* De huidziekte is cosmetisch ontsierend.
* De huidziekte veroorzaakt pijn of ongemak.
* De huidziekte is een risico voor de gezondheid.
## Voorbeelden van indicaties voor dermatologische chirurgie:
* Een basaalcelcarcinoom dat te groot is om te behandelen met cryochirurgie of elektrochirurgie.
* Een lipoom dat cosmetisch ontsierend is.
* Een wrat die pijn of ongemak veroorzaakt.
* Een abces dat niet reageert op antibiotica.
* Een litteken dat cosmetisch ontsierend is.
* Een tatoeage die de patiënt niet meer wenst.
## Contra-indicaties voor dermatologische chirurgie:
Er zijn ook een aantal contra-indicaties voor dermatologische chirurgie, waaronder:
* De patiënt heeft een slechte algemene gezondheid.
* De patiënt heeft een bloedstollingsstoornis.
* De patiënt heeft een allergie voor verdovingsmiddelen.
* De huidziekte is located in een gebied dat moeilijk te opereren is.
## Voorbeelden van contra-indicaties voor dermatologische chirurgie:
* Een patiënt met een slechte algemene gezondheid, zoals een patiënt met hartfalen of diabetes.
* Een patiënt met een bloedstollingsstoornis, zoals een patiënt met hemofilie.
* Een patiënt met een allergie voor verdovingsmiddelen, zoals een patiënt met een allergie voor lidocaïne.
* Een huidziekte die located is in een gebied dat moeilijk te opereren is, zoals een huidziekte op de oogleden of in de neus.
## Aanvullende informatie:
* Dermatologische chirurgie wordt meestal uitgevoerd onder plaatselijke verdoving.
* De meeste dermatologische chirurgische procedures zijn poliklinisch.
* De hersteltijd na dermatologische chirurgie is meestal kort.
* Dermatologische chirurgie is een veilige en effectieve manier om verschillende soorten huidaandoeningen te behandelen.
## Relevante vakkennis:
* Anatomie: Dermatologische chirurgen moeten een goede kennis hebben van de anatomie van de huid.
* Pathologie: Dermatologische chirurgen moeten een goede kennis hebben van de pathologie van huidaandoeningen.
* Farmacologie: Dermatologische chirurgen moeten een goede kennis hebben van de farmacologie van verdovingsmiddelen en antibiotica.
* Chirurgie: Dermatologische chirurgen moeten een goede kennis hebben van de principes van chirurgie.
``` |
JWBickel/bible_dictionary_unified | ---
language:
- en
pretty_name: Bible Dictionary - Unified
size_categories:
- 1K<n<10K
---
These 4 Bible dictionaries are combined:
-Easton's Bible Dictionary
-Hitchcock's Bible Names Dictionary
-Smith's Bible Dictionary
-Torrey's Topical Textbook
|
alvarobartt/Anthropic_HH_Golden_Formatted | ---
dataset_info:
features:
- name: prompt_id
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 65325008
num_examples: 42537
- name: test
num_bytes: 3651096
num_examples: 2312
download_size: 39481598
dataset_size: 68976104
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
pretty_name: Anthropic HH Golden Formatted
size_categories:
- 10K<n<100K
---
## Dataset Card for Anthropic_HH_Golden_Formatted
As per the original dataset: `This dataset is constructed to test the **ULMA** technique as mentioned in the paper
*Unified Language Model Alignment with Demonstration and Point-wise Human Preference*. They show that replacing the
positive samples in a preference dataset by high-quality demonstration data (golden data) greatly improves the
performance of various alignment methods (RLHF, DPO, ULMA). In particular, the ULMA method exploits the high-quality
demonstration data in the preference dataset by treating the positive and negative samples differently, and boosting
the performance by removing the KL regularizer for positive samples.`
For more information please see the original dataset at [Unified-Language-Model-Alignment/Anthropic_HH_Golden](https://huggingface.co/datasets/Unified-Language-Model-Alignment/Anthropic_HH_Golden).
### Formatting
Since the [Unified-Language-Model-Alignment/Anthropic_HH_Golden](https://huggingface.co/datasets/Unified-Language-Model-Alignment/Anthropic_HH_Golden) comes
in raw format, in order to ease the usage of this dataset, the following formatting has been applied:
* Separate `prompt` from `chosen` and `rejected` columns to have an overview of the prompts, as those are shared by both `chosen` and `rejected`
within the same rows.
* Add a `prompt_id` which is a SHA-256 encoding of the `prompt`
* Turn the raw conversations in `chosen` and `rejected` from `Human: ... Assistant: ... ...` to a chat-compliant format as a list of `{"role": "user" | "assistant", "content": "..."}`
Also note that using this format leads to a way better integration with [`huggingface/alignment-handbook](https://github.com/huggingface/alignment-handbook), providing an
straight forward way to fine-tune 7B LLMs using DPO, thanks to the awesome work done by [HuggingFaceH4](https://huggingface.co/HuggingFaceH4).
### Usage
Use it directly via 🤗`datasets`:
```python
from datasets import load_dataset
dataset = load_dataset("alvarobartt/Anthropic_HH_Golden_Formatted")
```
### Disclaimer
This dataset is only a copy of the original one, but with a clearer and easy to use formatting, but all
the credits go to the original authors at [Unified-Language-Model-Alignment](https://huggingface.co/Unified-Language-Model-Alignment). |
jakartaresearch/google-play-review | ---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonesian Google Play Review
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- sentiment
- google-play
- indonesian
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Indonesian Google Play Review
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Scrapped from e-commerce app on Google Play.
### Supported Tasks and Leaderboards
Sentiment Analysis
### Languages
Indonesian
## 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
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
hlt-lab/personachatsample-negate_previous_utterance | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: reference
dtype: string
splits:
- name: train
num_bytes: 35605
num_examples: 100
download_size: 27177
dataset_size: 35605
---
# Dataset Card for "personachatsample-negate_previous_utterance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyPixel/orca_minis | ---
language: en
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: system
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 164518588
num_examples: 104179
download_size: 79528616
dataset_size: 164518588
---
# Dataset Card for "orca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/street-work | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': street-work-items
'1': Cone
'2': Face_Shield
'3': Gloves
'4': Goggles
'5': Head
'6': Helmet
'7': No glasses
'8': No gloves
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: street-work
tags:
- rf100
---
# Dataset Card for street-work
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/street-work
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
street-work
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/street-work
### Citation Information
```
@misc{ street-work,
title = { street work Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/street-work } },
url = { https://universe.roboflow.com/object-detection/street-work },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
guynich/common_voice_13_0_hi_pseudo_labelled | ---
dataset_info:
config_name: hi
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 133145055.934
num_examples: 4479
- name: validation
num_bytes: 67167175.935
num_examples: 2281
- name: test
num_bytes: 102479336.039
num_examples: 2947
download_size: 269386085
dataset_size: 302791567.908
configs:
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
---
|
open-llm-leaderboard/details_Kukedlc__NeuralSynthesis-7B-v0.1 | ---
pretty_name: Evaluation run of Kukedlc/NeuralSynthesis-7B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Kukedlc/NeuralSynthesis-7B-v0.1](https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1)\
\ 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_Kukedlc__NeuralSynthesis-7B-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-06T05:11:09.006379](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralSynthesis-7B-v0.1/blob/main/results_2024-04-06T05-11-09.006379.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\": 0.6505229262507509,\n\
\ \"acc_stderr\": 0.03207183179797235,\n \"acc_norm\": 0.6493458270630847,\n\
\ \"acc_norm_stderr\": 0.032750381989947404,\n \"mc1\": 0.6328029375764994,\n\
\ \"mc1_stderr\": 0.016874805001453184,\n \"mc2\": 0.7815481859590259,\n\
\ \"mc2_stderr\": 0.013644095233081731\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7150170648464164,\n \"acc_stderr\": 0.013191348179838793,\n\
\ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869148\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7170882294363673,\n\
\ \"acc_stderr\": 0.004494934025462337,\n \"acc_norm\": 0.8917546305516829,\n\
\ \"acc_norm_stderr\": 0.003100550908916199\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\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\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\
: 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\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\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.0303883535518868,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.0303883535518868\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\
\ \"acc_stderr\": 0.013664230995834845,\n \"acc_norm\": 0.822477650063857,\n\
\ \"acc_norm_stderr\": 0.013664230995834845\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546836,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546836\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4324022346368715,\n\
\ \"acc_stderr\": 0.01656897123354861,\n \"acc_norm\": 0.4324022346368715,\n\
\ \"acc_norm_stderr\": 0.01656897123354861\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\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4745762711864407,\n\
\ \"acc_stderr\": 0.012753716929101006,\n \"acc_norm\": 0.4745762711864407,\n\
\ \"acc_norm_stderr\": 0.012753716929101006\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146292,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146292\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\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\": 0.6328029375764994,\n\
\ \"mc1_stderr\": 0.016874805001453184,\n \"mc2\": 0.7815481859590259,\n\
\ \"mc2_stderr\": 0.013644095233081731\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8524072612470402,\n \"acc_stderr\": 0.009968715765479648\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7081122062168309,\n \
\ \"acc_stderr\": 0.012522795894420867\n }\n}\n```"
repo_url: https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1
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_06T05_11_09.006379
path:
- '**/details_harness|arc:challenge|25_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|gsm8k|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hellaswag|10_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-06T05-11-09.006379.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-06T05-11-09.006379.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- '**/details_harness|winogrande|5_2024-04-06T05-11-09.006379.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-06T05-11-09.006379.parquet'
- config_name: results
data_files:
- split: 2024_04_06T05_11_09.006379
path:
- results_2024-04-06T05-11-09.006379.parquet
- split: latest
path:
- results_2024-04-06T05-11-09.006379.parquet
---
# Dataset Card for Evaluation run of Kukedlc/NeuralSynthesis-7B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Kukedlc/NeuralSynthesis-7B-v0.1](https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1) 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_Kukedlc__NeuralSynthesis-7B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-06T05:11:09.006379](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralSynthesis-7B-v0.1/blob/main/results_2024-04-06T05-11-09.006379.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": 0.6505229262507509,
"acc_stderr": 0.03207183179797235,
"acc_norm": 0.6493458270630847,
"acc_norm_stderr": 0.032750381989947404,
"mc1": 0.6328029375764994,
"mc1_stderr": 0.016874805001453184,
"mc2": 0.7815481859590259,
"mc2_stderr": 0.013644095233081731
},
"harness|arc:challenge|25": {
"acc": 0.7150170648464164,
"acc_stderr": 0.013191348179838793,
"acc_norm": 0.7303754266211604,
"acc_norm_stderr": 0.012968040686869148
},
"harness|hellaswag|10": {
"acc": 0.7170882294363673,
"acc_stderr": 0.004494934025462337,
"acc_norm": 0.8917546305516829,
"acc_norm_stderr": 0.003100550908916199
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"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": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
li-ping/test_1028_v1 | ---
dataset_info:
features:
- name: set
struct:
- name: neg
sequence: string
- name: pos
sequence: string
- name: query
dtype: string
splits:
- name: train
num_bytes: 2593205
num_examples: 1848
download_size: 120725
dataset_size: 2593205
---
# Dataset Card for "test_1028_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MaryLux/sentiment-banking-2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
dtype: 'null'
- name: annotation_agent
dtype: 'null'
- name: vectors
dtype: 'null'
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
struct:
- name: category
dtype: int64
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
dtype: 'null'
splits:
- name: train
num_bytes: 1445808
num_examples: 5001
download_size: 671410
dataset_size: 1445808
---
# Dataset Card for "sentiment-banking-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gsstein/0-percent-human-dataset-og | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 86079891
num_examples: 15326
- name: test
num_bytes: 3056853
num_examples: 576
- name: validation
num_bytes: 3254755
num_examples: 576
download_size: 57138075
dataset_size: 92391499
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
qgallouedec/prj_gia_dataset_metaworld_pick_place_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the pick-place-v2 environment, sample for the policy pick-place-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_pick_place_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_pick_place_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
mpont/crowdsourced-calculator-demo | ---
license: openrail
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
|
XandaoViolao/vozbonita | ---
license: openrail++
---
|
RIW/small_coco_test_50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: url
dtype: string
- name: key
dtype: string
- name: status
dtype: string
- name: error_message
dtype: 'null'
- name: width
dtype: int64
- name: height
dtype: int64
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: exif
dtype: string
- name: sha256
dtype: string
- name: watermark
dtype: bool
splits:
- name: train
num_bytes: 778069380.76
num_examples: 9485
- name: validation
num_bytes: 885003521.915
num_examples: 8965
download_size: 368439602
dataset_size: 1663072902.675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
CyberHarem/kuro_neuralcloud | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kuro/クロ/卡萝 (Neural Cloud)
This is the dataset of kuro/クロ/卡萝 (Neural Cloud), containing 263 images and their tags.
The core tags of this character are `long_hair, multicolored_hair, streaked_hair, blue_eyes, pink_hair, grey_hair, bangs, heterochromia, pink_eyes, breasts, hat, beret, one_side_up`, 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 | 263 | 403.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuro_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 263 | 199.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuro_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 671 | 448.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuro_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 263 | 343.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuro_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 671 | 691.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuro_neuralcloud/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kuro_neuralcloud',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, black_headwear, gloves, jacket, looking_at_viewer, solo, holding_gun, rifle, black_pantyhose, long_sleeves, one_eye_closed, tactical_clothes, flip_phone, skirt, grin, sitting |
| 1 | 14 |  |  |  |  |  | 1girl, black_headwear, solo, flip_phone, looking_at_viewer, black_jacket, simple_background, upper_body, holding_phone, blush, long_sleeves, tactical_clothes, grey_gloves, grin, black_gloves, white_background |
| 2 | 8 |  |  |  |  |  | 1girl, black_gloves, black_jacket, looking_at_viewer, solo, blush, upper_body, black_headwear, grin, long_sleeves, selfie |
| 3 | 29 |  |  |  |  |  | 1girl, ponytail, looking_at_viewer, official_alternate_costume, solo, bare_shoulders, black_dress, cross_earrings, smile, black_gloves, blush, hair_ribbon, simple_background, white_background, white_hair, hair_bow, medium_breasts, fishnets, off_shoulder, phone |
| 4 | 17 |  |  |  |  |  | earrings, 1girl, official_alternate_costume, pointy_ears, solo, very_long_hair, double_bun, looking_at_viewer, smile, elbow_gloves, bare_shoulders, black_dress, black_gloves, fishnets, horns, fang, medium_breasts, open_mouth, spider_web_print, blush, ghost, low_wings, red_eyes, black_pantyhose, nail_polish, sidelocks, brown_pantyhose, white_background, black_nails, halloween, holding, simple_background, vampire, bat_print, chain, eyeball, multicolored_dress, tail |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_headwear | gloves | jacket | looking_at_viewer | solo | holding_gun | rifle | black_pantyhose | long_sleeves | one_eye_closed | tactical_clothes | flip_phone | skirt | grin | sitting | black_jacket | simple_background | upper_body | holding_phone | blush | grey_gloves | black_gloves | white_background | selfie | ponytail | official_alternate_costume | bare_shoulders | black_dress | cross_earrings | smile | hair_ribbon | white_hair | hair_bow | medium_breasts | fishnets | off_shoulder | phone | earrings | pointy_ears | very_long_hair | double_bun | elbow_gloves | horns | fang | open_mouth | spider_web_print | ghost | low_wings | red_eyes | nail_polish | sidelocks | brown_pantyhose | black_nails | halloween | holding | vampire | bat_print | chain | eyeball | multicolored_dress | tail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------|:---------|:--------------------|:-------|:--------------|:--------|:------------------|:---------------|:-----------------|:-------------------|:-------------|:--------|:-------|:----------|:---------------|:--------------------|:-------------|:----------------|:--------|:--------------|:---------------|:-------------------|:---------|:-----------|:-----------------------------|:-----------------|:--------------|:-----------------|:--------|:--------------|:-------------|:-----------|:-----------------|:-----------|:---------------|:--------|:-----------|:--------------|:-----------------|:-------------|:---------------|:--------|:-------|:-------------|:-------------------|:--------|:------------|:-----------|:--------------|:------------|:------------------|:--------------|:------------|:----------|:----------|:------------|:--------|:----------|:---------------------|:-------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | | | X | X | | | | X | | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | | | X | X | | | | X | | | | | X | | X | | X | | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 29 |  |  |  |  |  | X | | | | X | X | | | | | | | | | | | | X | | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 17 |  |  |  |  |  | X | | | | X | X | | | X | | | | | | | | | X | | | X | | X | X | | | X | X | X | | X | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
juancopi81/educatinayt | ---
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: CHANNEL_NAME
dtype: string
- name: URL
dtype: string
- name: TITLE
dtype: string
- name: DESCRIPTION
dtype: string
- name: TRANSCRIPTION
dtype: string
- name: SEGMENTS
dtype: string
splits:
- name: train
num_bytes: 12525875
num_examples: 884
download_size: 5024572
dataset_size: 12525875
tags:
- whisper
- whispering
- medium
---
# Dataset Card for "educatinayt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Swatermelon/yoci_monkey | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 594675.0
num_examples: 43
download_size: 0
dataset_size: 594675.0
---
# Dataset Card for "yoci_monkey"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yezhengli9/wmt20-en-de | ---
dataset_info:
features:
- name: id (string)
dtype: string
- name: translation (translation)
dtype: string
splits:
- name: train
num_bytes: 669275
num_examples: 1418
download_size: 420066
dataset_size: 669275
---
# Dataset Card for "wmt20-en-de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gsynb/work1 | ---
license: openrail
---
|
ovior/twitter_dataset_1713073343 | ---
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: 2391246
num_examples: 7426
download_size: 1344266
dataset_size: 2391246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
badmatr11x/hate-offensive-speech | ---
license: mit
language:
- en
size_categories:
- 10K<n<100K
source_dataset:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
dataset_info:
features:
- name: label
dtype: int64
- name: tweet
dtype: string
splits:
- name: train
num_bytes: 5045816.7990131285
num_examples: 51070
- name: test
num_bytes: 280301.1995065645
num_examples: 2837
- name: validation
num_bytes: 280400.0014803066
num_examples: 2838
download_size: 3879287
dataset_size: 5606517.999999999
---
# **Dataset Card for Hate-Offensive Speech**
This is the original dataset created by the user [badmatr11x](https://www.huggingface.co/badmatr11x/). Datasets contains the annotated tweets classifying into the three categories; **hate-speech**, **offensive-speech** and **neither**.
# **Dataset Structure**
Database Structure as follows:
```
{
"label": {
0: "hate-speech",
1: "offensive-speech",
2: "neither"
},
"tweet": <string>
}
```
### **Dataset Instances**
Examples from the datasets as follows:
Lable-0 (Hate Speech)
```
{
"label": 0,
"tweet": "@user @user @user we were? maybe you are-but don't you dare demonize innocent infants born with white skin, "
}
```
Label-1 (Offensive Speech)
```
{
"label": 1,
"tweet": "...and I'm goin back to school.. only for the hoes and a class or two"
}
```
Label-2 (Neither)
```
{
"label": 2,
"tweet": "@user @user are you guys going to take forever to bring the new gmc?"
}
```
# **Data Fields**
- `label`: a int64 value
- `tweet`: a string
# **Data Splits**
- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.
|
CyberHarem/neimi_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of neimi (Fire Emblem)
This is the dataset of neimi (Fire Emblem), containing 20 images and their tags.
The core tags of this character are `headband, pink_hair, short_hair, pink_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 | 14.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neimi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 20 | 10.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neimi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 32 | 16.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neimi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 20 | 14.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neimi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 32 | 20.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/neimi_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/neimi_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, fingerless_gloves, arrow_(projectile), elbow_gloves, simple_background, armor, bow_(weapon), capri_pants, hood, quiver, closed_mouth, looking_at_viewer, white_background, full_body, holding, smile, tears |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | fingerless_gloves | arrow_(projectile) | elbow_gloves | simple_background | armor | bow_(weapon) | capri_pants | hood | quiver | closed_mouth | looking_at_viewer | white_background | full_body | holding | smile | tears |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------------------|:---------------|:--------------------|:--------|:---------------|:--------------|:-------|:---------|:---------------|:--------------------|:-------------------|:------------|:----------|:--------|:--------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
StephanAkkerman/financial-tweets-stocks | ---
license: mit
---
|
Pravarved/test-dataset | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
CyberHarem/makima_nikke | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of makima/マキマ/玛奇玛/마키마 (Nikke: Goddess of Victory)
This is the dataset of makima/マキマ/玛奇玛/마키마 (Nikke: Goddess of Victory), containing 500 images and their tags.
The core tags of this character are `bangs, red_hair, ringed_eyes, yellow_eyes, long_hair, braid, braided_ponytail, breasts, sidelocks, 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 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/makima_nikke/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 486.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makima_nikke/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1256 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/makima_nikke/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 882.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makima_nikke/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1256 | 1.70 GiB | [Download](https://huggingface.co/datasets/CyberHarem/makima_nikke/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/makima_nikke',
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 | 6 |  |  |  |  |  | 1girl, cleavage, closed_mouth, collarbone, open_shirt, solo, white_shirt, long_sleeves, looking_at_viewer, navel, stomach, thighs, bare_shoulders, black_bra, black_panties, large_breasts, off_shoulder, smile, blush |
| 1 | 8 |  |  |  |  |  | 1girl, black_necktie, black_pants, collared_shirt, formal, looking_at_viewer, solo, white_shirt, business_suit, long_sleeves, shirt_tucked_in, smile, chain, closed_mouth, black_jacket, simple_background |
| 2 | 11 |  |  |  |  |  | 1girl, black_jacket, black_necktie, collared_shirt, formal, solo, upper_body, white_shirt, looking_at_viewer, closed_mouth, smile, long_sleeves, simple_background, medium_hair, business_suit |
| 3 | 5 |  |  |  |  |  | 1girl, arms_behind_back, black_necktie, black_pants, collared_shirt, long_sleeves, looking_at_viewer, smile, solo, white_shirt, closed_mouth, formal, shirt_tucked_in, cowboy_shot, standing |
| 4 | 10 |  |  |  |  |  | 1girl, black_necktie, collared_shirt, simple_background, solo, upper_body, white_shirt, looking_at_viewer, smile, medium_hair, white_background, black_background |
| 5 | 8 |  |  |  |  |  | 1girl, black_dress, halo, looking_at_viewer, solo, closed_mouth, smile, medium_hair, simple_background, hair_between_eyes, upper_body, arms_behind_back, black_background, chain, long_sleeves |
| 6 | 5 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, black_bra, black_panties, elbow_gloves, large_breasts, lingerie, smile, underwear_only, black_choker, black_gloves, garter_belt, garter_straps, simple_background, armpits, arms_up, bare_shoulders, black_background, black_thighhighs, closed_mouth, collarbone, cowboy_shot, holding_leash, navel, side-tie_panties, stomach |
| 7 | 5 |  |  |  |  |  | 1girl, elbow_gloves, garter_straps, looking_at_viewer, short_sleeves, smile, solo, white_dress, alternate_costume, brown_thighhighs, holding_syringe, nurse_cap, closed_mouth, collared_dress, full_body, sitting, bed_sheet, black_footwear, blush, cross, high_heels, large_breasts, short_dress, simple_background, thighs, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | closed_mouth | collarbone | open_shirt | solo | white_shirt | long_sleeves | looking_at_viewer | navel | stomach | thighs | bare_shoulders | black_bra | black_panties | large_breasts | off_shoulder | smile | blush | black_necktie | black_pants | collared_shirt | formal | business_suit | shirt_tucked_in | chain | black_jacket | simple_background | upper_body | medium_hair | arms_behind_back | cowboy_shot | standing | white_background | black_background | black_dress | halo | hair_between_eyes | elbow_gloves | lingerie | underwear_only | black_choker | black_gloves | garter_belt | garter_straps | armpits | arms_up | black_thighhighs | holding_leash | side-tie_panties | short_sleeves | white_dress | alternate_costume | brown_thighhighs | holding_syringe | nurse_cap | collared_dress | full_body | sitting | bed_sheet | black_footwear | cross | high_heels | short_dress |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------------|:-------------|:-------------|:-------|:--------------|:---------------|:--------------------|:--------|:----------|:---------|:-----------------|:------------|:----------------|:----------------|:---------------|:--------|:--------|:----------------|:--------------|:-----------------|:---------|:----------------|:------------------|:--------|:---------------|:--------------------|:-------------|:--------------|:-------------------|:--------------|:-----------|:-------------------|:-------------------|:--------------|:-------|:--------------------|:---------------|:-----------|:-----------------|:---------------|:---------------|:--------------|:----------------|:----------|:----------|:-------------------|:----------------|:-------------------|:----------------|:--------------|:--------------------|:-------------------|:------------------|:------------|:-----------------|:------------|:----------|:------------|:-----------------|:--------|:-------------|:--------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | X | | | X | X | X | X | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | | X | | | X | X | X | X | | | | | | | | | X | | X | | X | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | | X | X | X | X | | | | | | | | | X | | X | X | X | X | | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | | | | | | | | | | | | | | |
| 7 | 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 |
|
AhmedSSabir/Textual-Image-Caption-Dataset | ---
task_categories:
- image-to-text
- image-classification
- visual-question-answering
- sentence-similarity
language:
- en
tags:
- image captioning
- language grounding
- visual semantic
- semantic similarity
pretty_name: ' image captioning language grounding visual semantic '
---
#### Update: OCT-2023 ###
Add v2 with recent SoTA model **swinV2 classifier** for both soft/*hard-label* visual_caption_cosine_score_v2 with _person_ label (0.2, 0.3 and 0.4)
# Introduction
Modern image captaining relies heavily on extracting knowledge, from images such as objects,
to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset
for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information
about the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task,
such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.
Please refer to [project page](https://sabirdvd.github.io/project_page/Dataset_2022/index.html) and [Github](https://github.com/ahmedssabir/Visual-Semantic-Relatedness-Dataset-for-Image-Captioning) for more information. [](https://arxiv.org/abs/2301.08784) [](https://ahmed.jp/project_page/Dataset_2022/index.html)
For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic)
# Overview
We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP,
and Faster R-CNN to extract object information for each image. We use three filter approaches
to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier
is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects.
(3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong
relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then
we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage
of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)
to estimate the visual relatedness score.
<!--
## Dataset
(<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
### Sample
```
|---------------+--------------+---------+---------------------------------------------------|
| VC1 | VC2 | VC3 | human annoated caption |
| ------------- | ----------- | --------| ------------------------------------------------- |
| cheeseburger | plate | hotdog | a plate with a hamburger fries and tomatoes |
| bakery | dining table | website | a table having tea and a cake on it |
| gown | groom | apron | its time to cut the cake at this couples wedding |
|---------------+--------------+---------+---------------------------------------------------|
```
-->
### Download
0. [Dowload Raw data with ID and Visual context](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> original dataset with related ID caption [train2014](https://cocodataset.org/#download)
1. [Downlod Data with cosine score](https://www.dropbox.com/s/55sit8ow9tems4u/visual_caption_cosine_score.zip?dl=0)-> soft cosine lable with **th** 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1]
2. [Dowload Overlaping visual with caption](https://www.dropbox.com/s/br8nhnlf4k2czo8/COCO_overlaping_dataset.txt?dl=0)-> Overlap visual context and the human annotated caption
3. [Download Dataset (tsv file)](https://www.dropbox.com/s/dh38xibtjpohbeg/train_all.zip?dl=0) 0.0-> raw data with hard lable without cosine similairty and with **th**reshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4
4. [Download Dataset GenderBias](https://www.dropbox.com/s/1wki0b0d21078mj/gender%20natural.zip?dl=0)-> man/woman replaced with person class label
<!--
For future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by
employing unsupervised learning (i.e. contrastive learning). (work in progress)
#
1. [Download CC](https://www.dropbox.com/s/pc1uv2rf6nqdp57/CC_caption_40.txt.zip) -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions)
2. [Download CC+wiki](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> CC+1M-wiki 3M (3255928)
3. [Download CC+wiki+COCO](https://www.dropbox.com/s/k7oqwr9a1a0h8x1/CC_caption_40%2Bwiki%2BCOCO.txt.zip) -> CC+wiki+COCO-Caption 3.5M (366984)
4. [Download COCO-caption+wiki](https://www.dropbox.com/s/wc4k677wp24kzhh/COCO%2Bwiki.txt.zip) -> COCO-caption +wiki 1.4M (1413915)
5. [Download COCO-caption+wiki+CC+8Mwiki](https://www.dropbox.com/s/xhfx32sjy2z5bpa/11M_wiki_7M%2BCC%2BCOCO.txt.zip) -> COCO-caption+wiki+CC+8Mwiki 11M (11541667)
--->
## Citation
The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:
```bibtex
@article{sabir2023visual,
title={Visual Semantic Relatedness Dataset for Image Captioning},
author={Sabir, Ahmed and Moreno-Noguer, Francesc and Padr{\'o}, Llu{\'\i}s},
journal={arXiv preprint arXiv:2301.08784},
year={2023}
}
``` |
CyberHarem/type79_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of type79/79式/79式 (Girls' Frontline)
This is the dataset of type79/79式/79式 (Girls' Frontline), containing 54 images and their tags.
The core tags of this character are `brown_hair, hairband, long_hair, bangs, red_eyes, breasts, ribbon, hair_ribbon`, 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 | 54 | 78.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type79_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 54 | 40.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type79_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 144 | 91.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type79_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 54 | 66.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type79_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 144 | 129.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type79_girlsfrontline/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/type79_girlsfrontline',
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 | 13 |  |  |  |  |  | 1girl, solo, black_thighhighs, garter_straps, looking_at_viewer, single_mechanical_arm, black_gloves, simple_background, blue_jacket, holding, short_hair_with_long_locks, submachine_gun, black_leotard, blush, pouch, bag, orange_eyes, prosthetic_arm, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | black_thighhighs | garter_straps | looking_at_viewer | single_mechanical_arm | black_gloves | simple_background | blue_jacket | holding | short_hair_with_long_locks | submachine_gun | black_leotard | blush | pouch | bag | orange_eyes | prosthetic_arm | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------|:----------------|:--------------------|:------------------------|:---------------|:--------------------|:--------------|:----------|:-----------------------------|:-----------------|:----------------|:--------|:--------|:------|:--------------|:-----------------|:-------------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
rajistics/million-headlines | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Million Headlines
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories: []
task_ids: []
---
# Dataset Card for Million Headlines
## 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:** [Kaggle dataset](https://www.kaggle.com/datasets/therohk/million-headlines)
- **Point of Contact:** Rohit Kulkarni)
### Dataset Summary
This contains data of news headlines published over a period of eighteen years. Sourced from the reputable Australian news source ABC (Australian Broadcasting Corporation)
## Dataset Structure
### Data Instances
For each instance, there is a integer for the data, a string for news headline.
### Data Fields
- `publish date`: a integer that represents the data
- `headline`: a string for the news headline
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people that were in the headlines.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset represents one news service in Australia and should not be considered representative of all news or headlines.
### Discussion of Biases
News headlines may contain biases and should not be considered neutral.
### Licensing Information
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). |
prince-canuma/accentsDB-with-transcripts | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1885630770.23
num_examples: 12585
download_size: 728047519
dataset_size: 1885630770.23
---
# Dataset Card for "accentsDB-with-transcripts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-tweet_eval-offensive-f58805-30720144959 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- tweet_eval
eval_info:
task: multi_class_classification
model: elozano/tweet_offensive_eval
metrics: ['bertscore']
dataset_name: tweet_eval
dataset_config: offensive
dataset_split: train
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: elozano/tweet_offensive_eval
* Dataset: tweet_eval
* Config: offensive
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@fabeelaalirawther@gmail.com](https://huggingface.co/fabeelaalirawther@gmail.com) for evaluating this model. |
alesanm/chanel_long_descriptions | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 75650854.0
num_examples: 49
download_size: 75616606
dataset_size: 75650854.0
---
# Dataset Card for "chanel_long_descriptions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibivibiv/alpaca_tasksource16 | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 135362216
num_examples: 253970
download_size: 76901883
dataset_size: 135362216
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ighoshsubho/step_back_prompting_mistral_dataset | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 112347
num_examples: 83
download_size: 56657
dataset_size: 112347
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
loubnaelattar/dataset | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1644788
num_examples: 1000
download_size: 963885
dataset_size: 1644788
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alexredna/oasst2_dpo_pairs | ---
language:
- en
- de
- es
- fr
license: apache-2.0
dataset_info:
features:
- name: prompt_id
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
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: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: lang
dtype: string
splits:
- name: train
num_bytes: 38577779
num_examples: 10046
download_size: 23169558
dataset_size: 38577779
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oasst2_dpo_pairs"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Usage](#usage)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
Dataset transferred into the structure for trainig with DPO and can be used with the [Alignment Handbook](https://github.com/huggingface/alignment-handbook/tree/main)
The structure follows mostly the same scheme as [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
### Usage
To load the dataset, run:
```python
from datasets import load_dataset
ds = load_dataset("alexredna/oasst2_dpo_pairs")
```
### Languages
Base dataset filtered to only contain: German, English, Spanish and Frensh conversations.
## Dataset Creation
I used the following script for converting the oaast2 dataset:
```python
from datasets import Dataset, load_dataset
import pandas as pd
def build_tree(df):
tree = {}
message_dict = df.set_index('message_id').to_dict(orient='index')
for message_id, message in message_dict.items():
parent_id = message['parent_id']
if parent_id is None or pd.isna(parent_id):
tree[message_id] = message
tree[message_id]['replies'] = []
else:
if parent_id in message_dict:
if 'replies' not in message_dict[parent_id]:
message_dict[parent_id]['replies'] = []
message_dict[parent_id]['replies'].append(message)
return tree
def convert_for_dpo(entry):
example = dict()
example["system"] = ""
prompt_id = entry["message_tree_id"]
prompt = entry["text"]
chosen = []
rejected = []
chosen_reply = entry["replies"][0]
rejected_reply = entry["replies"][1]
score_chosen = len(entry["replies"]) - chosen_reply["rank"]
score_rejected = len(entry["replies"]) - rejected_reply["rank"]
chosen.append({"role": "user", "content": prompt})
chosen.append({"role": "assistant", "content": entry["replies"][0]["text"]})
rejected.append({"role": "user", "content": prompt})
rejected.append({"role": "assistant", "content": entry["replies"][1]["text"]})
return {"prompt_id": prompt_id, "prompt": prompt,"messages": chosen, "chosen": chosen, "rejected": rejected, "score_chosen": score_chosen, "score_rejected": score_rejected, "lang": entry["lang"]}
oasst2 = load_dataset("OpenAssistant/oasst2")
df = oasst2["train"].to_pandas()
df_multi = df.loc[df['lang'].isin(['en', 'de', 'es', 'fr'])]
tree = build_tree(df_multi)
transformed_for_dpo = []
for row in tree.values():
try:
transformed_for_dpo.append(convert_for_dpo(row))
except:
print("row does not contain chosen or rejected values")
df = pd.DataFrame.from_records(transformed_for_dpo)
ds = Dataset.from_pandas(df)
ds.push_to_hub("oasst2_dpo_pairs", token="<token>")
```
### Licensing Information
[Apache-2.0](https://huggingface.co/datasets?license=license%3Aapache-2.0)
### Citation Information
This dataset was converted from [OpenAssistant/oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2)
|
Hrishikesh332/autotrain-data-meme-classification | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: meme-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project meme-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<657x657 RGB PIL image>",
"target": 1
},
{
"image": "<1124x700 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['meme', 'not_meme'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 263 |
| valid | 67 |
|
alexandreteles/told_br_binary_sm | ---
license: cc-by-sa-4.0
language:
- pt
language_bcp47:
- pt-BR
multilinguality:
- monolingual
pretty_name: ToLD-Br-small
size_categories:
- 1K<n<10K
source_datasets:
- told-br
---
This dataset is a random 1/3 slice of the original [told-br](https://huggingface.co/datasets/told-br) |
jlbaker361/cyberpunk-lite-500-cropped | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: frame
dtype: int64
- name: title
dtype: string
splits:
- name: train
num_bytes: 5417030.0
num_examples: 24
download_size: 5421221
dataset_size: 5417030.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
qgyd2021/sentence_pair | ---
license: apache-2.0
task_categories:
- sentence-similarity
language:
- zh
- en
size_categories:
- 100M<n<1B
---
## 句子对数据集
数据集从网上收集整理如下:
| 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| ChineseSTS | 汉语 | [ChineseSTS](https://github.com/IAdmireu/ChineseSTS) | 24.7K | STS 中文文本语义相似度（这个数据集好像很多标签是错的，不建议使用。） | [ChineseSTS](https://huggingface.co/datasets/tiansz/ChineseSTS) |
| ccks2018_task3 | 汉语 | [BQ_corpus](http://icrc.hitsz.edu.cn/info/1037/1162.htm); [CCKS2018_3](https://www.biendata.xyz/competition/CCKS2018_3/data/) | TRAIN: 100K, VALID: 10K, TEST: 10K | CCKS 2018 微众银行智能客服问句匹配大赛 | [BQ_corpus](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/BQ_corpus) |
| DIAC2019 | 汉语 | [DIAC2019](https://www.biendata.xyz/competition/2019diac/data/) | 6K | 以问题组的形式提供，每组问句又分为等价部分和不等价部分，等价问句之间互相组合可以生成正样本，等价问句和不等价问句之间互相组合可以生成负样本。我们提供6000组问句的训练集。 | |
| LCQMC | 汉语 | [LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html); [LCQMC](https://www.luge.ai/#/luge/dataDetail?id=14); [C18-1166.pdf](https://aclanthology.org/C18-1166.pdf) | TRAIN: 238766, VALID: 8802, TEST: 12500 | 百度知道领域的中文问题匹配数据集，目的是为了解决在中文领域大规模问题匹配数据集的缺失。该数据集从百度知道不同领域的用户问题中抽取构建数据。| [lcqmc_data](https://github.com/xiaohai-AI/lcqmc_data) |
| AFQMC | 汉语 | [AFQMC](https://tianchi.aliyun.com/dataset/106411) | TRAIN: 34334, VALID: 4316, TEST: 3861 | 蚂蚁金融语义相似度数据集，用于问题相似度计算。即：给定客服里用户描述的两句话，用算法来判断是否表示了相同的语义。 | [ATEC](https://huggingface.co/datasets/shibing624/nli_zh); [ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC) |
| BUSTM | 汉语 | [BUSTM](https://tianchi.aliyun.com/competition/entrance/531851/information); [BUSTM](https://github.com/xiaobu-coai/BUSTM) | 总样本数为：177173，其中，匹配样本个数为：54805，不匹配样本个数为：122368 | 小布助手对话短文本语义匹配比赛数据集 | [BUSTM](https://github.com/CLUEbenchmark/FewCLUE/tree/main/datasets/bustm) |
| CHIP2019 | 汉语 | [CHIP2019](https://www.biendata.xyz/competition/chip2019/) | 2万 | 平安医疗科技疾病问答迁移学习比赛数据集（VALID 集没有 label） | |
| COVID-19 | 汉语 | [COVID-19](https://tianchi.aliyun.com/competition/entrance/231776/information) | | 天池新冠疫情相似句对判定大赛 | [COVID-19](https://gitee.com/liangzongchang/COVID-19-sentence-pair/) |
| Chinese-MNLI | 汉语 | [Chinese-MNLI](https://github.com/pluto-junzeng/CNSD) | TRAIN: 390K, VALID: 12K, TEST: 13K | 通过翻译加部分人工修正的方法，从英文原数据集生成（原数据是：蕴含，中性，冲突，的句子推理数据集，已转换为句子对）。 | |
| Chinese-SNLI | 汉语 | [Chinese-SNLI](https://github.com/pluto-junzeng/CNSD) | TRAIN: 550K, VALID: 10K, TEST: 10K | 通过翻译加部分人工修正的方法，从英文原数据集生成（原数据是：蕴含，中性，冲突，的句子推理数据集，已转换为句子对）。 | |
| OCNLI | 汉语 | [OCNLI](https://github.com/CLUEbenchmark/OCNLI) | TRAIN: 50K, VALID: 3K, TEST: 3K | 原生中文自然语言推理数据集，是第一个非翻译的、使用原生汉语的大型中文自然语言推理数据集。 | |
| STS-B | 汉语 | [STS-B](https://adapterhub.ml/explore/sts/sts-b/); [STS Benchmark](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) | TRAIN: 5749, VALID: 1500, TEST: 1379 | 语义文本相似性基准测试 | [STS-B](https://pan.baidu.com/s/10yfKfTtcmLQ70-jzHIln1A?pwd=gf8y#list/path=%2F); [STS-B](https://huggingface.co/datasets/shibing624/nli_zh/viewer/STS-B) |
| PAWSX-ZH | 汉语 | [PAWSX](https://paperswithcode.com/paper/paws-x-a-cross-lingual-adversarial-dataset/review/) | TRAIN: 49.4K, VALID: 2K, TEST: 2K | 从 PAWSX翻译成中文的数据集 | [PAWSX](https://pan.baidu.com/share/init?surl=ox0tJY3ZNbevHDeAqDBOPQ&pwd=mgjn); [PAWSX](https://huggingface.co/datasets/shibing624/nli_zh/viewer/PAWSX) |
## 样本示例
**ChineseSTS:** 这个数据集好像很多标签是错的，不建议使用。
```text
`穆斯林认为伊斯兰教的先知（`, `）是被真主挑选成为他的信使的人物。`, `1`
`咱俩谁跟谁呀。`, `我们俩谁跟谁呀。`, `1`
`咱俩谁跟谁呀。`, `咱俩关系很好。`, `0`
`他买了王教授一本书。`, `他买了王教授的书。`, `0`
```
**ccks2018_task3:**
```text
`用微信都6年，微信没有微粒贷功能`, `4。 号码来微粒贷`, `0`
`微信消费算吗`, `还有多少钱没还`, `0`
`为什么借款后一直没有给我回拨电话`, `怎么申请借款后没有打电话过来呢！`, `1`
`已经在银行换了新预留号码。`, `已经在银行换了新预留号码。`, `1`
```
**DIAC2019:** 这个数据集像是从分类数据集组合而来，有很多句子是重复的。
```text
`人民法院不予受理的民事案件有哪些情形？`, `民事诉讼什么情况下不能立案`, `0`
`民事诉讼中对哪些情形的起诉法院不予受理`, `人民法院不予受理的民事案件有哪些情形？`, `1`
`民事诉讼中对哪些情形的起诉法院不予受理`, `哪些案件会给开具民事诉讼不予立案通知书`, `0`
`民事诉讼中对哪些情形的起诉法院不予受理`, `哪些情形下，不予受理民事诉讼申请？`, `1`
```
**LCQMC:**
```text
`喜欢打篮球的男生喜欢什么样的女生`, `爱打篮球的男生喜欢什么样的女生`, `1`
`我手机丢了，我想换个手机`, `我想买个新手机，求推荐`, `1`
`大家觉得她好看吗`, `大家觉得跑男好看吗？`, `0`
`求秋色之空漫画全集`, `求秋色之空全集漫画`, `1`
```
**AFQMC:**
```text
`蚂蚁借呗等额还款可以换成先息后本吗`, `借呗有先息到期还本吗`, `0`
`蚂蚁花呗说我违约一次`, `蚂蚁花呗违约行为是什么`, `0`
`支付宝系统点我的里面没有花呗这一项`, `我下载支付宝怎么没有花呗的`, `1`
`花呗消费超过额度有什么影响吗`, `花呗额度成负数有啥影响吗`, `1`
```
**BUSTM:**
```text
`叫爸爸叫一声我听听`, `那你叫我一声爸爸`, `1`
`十亿韩元等于多少人民币`, `一百元人民币`, `0`
`我喜欢你那你喜欢我吗`, `你喜欢我不我也喜欢你`, `0`
`你晚上吃了什么`, `你晚上吃啥了`, `1`
```
**CHIP2019:** 这个数据集的 validation 子集没有标签。
```text
`艾滋病窗口期会出现腹泻症状吗`, `头疼腹泻四肢无力是不是艾滋病`, `0`
`由于糖尿病引起末梢神经炎，怎么根治？`, `糖尿病末梢神经炎的治疗方法`, `1`
`H型高血压，是通所说的高血脂？`, `高血压引起脑出血怎么抢救治疗`, `0`
`你好，我60岁，患高血压，80135，爱喝酸奶可以吗？`, `高血压糖尿病人可以喝牛奶吗？`, `1`
```
**COVID-19:**
```text
`剧烈运动后咯血,是怎么了?`, `剧烈运动后咯血是什么原因？`, `1`
`剧烈运动后咯血,是怎么了?`, `剧烈运动后为什么会咯血？`, `1`
`剧烈运动后咯血,是怎么了?`, `剧烈运动后咯血，应该怎么处理？`, `0`
`剧烈运动后咯血,是怎么了?`, `剧烈运动后咯血，需要就医吗？`, `0`
`剧烈运动后咯血,是怎么了?`, `剧烈运动后咯血，是否很严重？`, `0`
```
**Chinese-MNLI:**
```text
`从概念上讲，奶油略读有两个基本维度-产品和地理。`, `产品和地理位置是使奶油撇油起作用的原因。`, `0`
`我们的一个号码将执行您的指示。`, `我的一个队员会非常精确地执行你的命令。`, `1`
`怎么又知道了？这又是他们的信息。`, `这些信息属于他们。`, `1`
`同性恋。`, `异性恋者。`, `0`
```
**STS-B:** 这个数据集原本是 0-5 的相似度打分，我把它转换为 >=3 的为相似，其它为不相似。这可能会导致一些问题。
```text
`一架飞机要起飞了。`, `一架飞机正在起飞。`, `1`
`一个男人在吹一支大笛子。`, `一个人在吹长笛。`, `1`
`一个人正把切碎的奶酪撒在比萨饼上。`, `一个男人正在把切碎的奶酪撒在一块未煮好的比萨饼上。`, `1`
`三个人在下棋。`, `两个人在下棋。`, `0`
`一个男人在抽烟。`, `一个男人在滑冰。`, `0`
`一个女人在写作。`, `一个女人在游泳。`, `0`
```
**PAWSX-ZH:** PAWSX 是一个文本释义的数据集，感觉难度较大，可能不适合用于 FAQ 相似问匹配的任务。
```text
`1975年的NBA赛季 - 76赛季是全美篮球协会的第30个赛季。`, `1975-76赛季的全国篮球协会是NBA的第30个赛季。`, `1`
`当可以保持相当的流速时，结果很高。`, `当可以保持可比较的流速时，结果很高。`, `1`
`kBox有助于等长和同心收缩以及离心训练。`, `kBox有助于偏心以及同心收缩和等长训练。`, `0`
`例如，要输入长度为4厘米的垂直线，绘制就足够了：`, `例如，为了绘制4厘米长的垂直线，只需键入：`, `0`
```
## 数据来源
<details>
<summary>参考的数据来源,展开查看</summary>
<pre><code>
https://github.com/liucongg/NLPDataSet
https://huggingface.co/datasets/tiansz/ChineseSTS
https://zhuanlan.zhihu.com/p/454173790
https://huggingface.co/datasets/shibing624/nli_zh
</code></pre>
</details>
|
tyzhu/squad_v2_1000_0.50_id | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 97308726.73032843
num_examples: 55568
- name: validation
num_bytes: 1917601
num_examples: 1000
download_size: 4274826
dataset_size: 99226327.73032843
---
# Dataset Card for "squad_v2_1000_0.50_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
theblackcat102/IMO-geometry | ---
dataset_info:
features:
- name: source
dtype: string
- name: question
dtype: string
- name: category
dtype: string
splits:
- name: test
num_bytes: 33953
num_examples: 87
download_size: 18740
dataset_size: 33953
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: mit
language:
- en
tags:
- IMO
- geometry
- math
---
# IMO geometry questions
32 IMO geometry questions from 2000 to 2021 (filter by category "IMO")
Data source : [https://artofproblemsolving.com/wiki/index.php/Category:Olympiad_Geometry_Problems](https://artofproblemsolving.com/wiki/index.php/Category:Olympiad_Geometry_Problems)
55 more questions from others (other regional olympiad competition) as well as 13 GPT-4 generate ones.
Only the raw questions are available, if you want to use them for alpha geometry there's still a missing translation step.
This is the example shown in Alpha Geometry
Question:
```
Let ABC be an acute-angled triangle with AB ≠ AC.
The circle with diameter BC intersects the sides AB and AC at M and N respectively.
Denote by O the midpoint of the side BC. The bisectors of the angles ∠BAC and ∠MON intersect at R.
Prove that the circumcircles of the triangles BMR and CNR have a common point lying on the side BC.
```
Translated:
```
Premise
A B C O M N R P : Points
mid_point(O,B,C) [--]
same_line(B,M,A) [00] OM=OB [01]
same_line(N,C,A) [02] ON=OB [03]
∠BAR=∠RAC [04] ∠MOR=∠RON [05]
circle(B,M,R,P) [06] circle(C,N,R,P) [07]
Goal
same_line(P, B, C)
``` |
arianhosseini/quail_with_tree_depth | ---
dataset_info:
features:
- name: id
dtype: string
- name: context_id
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- name: question_id
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- name: title
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- name: url
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- name: answers
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- name: correct_answer_id
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- name: constituency_depth
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num_examples: 556
download_size: 2299730
dataset_size: 29725652
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: challenge
path: data/challenge-*
---
|
vigneshgs7/Boundary_detection_Doc_9 | ---
dataset_info:
features:
- name: name
dtype: string
- name: uuid
dtype: string
- name: status
dtype: string
- name: image
dtype: image
- name: label.annotations
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- name: id
dtype: int32
- name: category_id
dtype: int32
- name: label.segmentation_bitmap
dtype: image
splits:
- name: train
num_bytes: 19702064281.0
num_examples: 396
download_size: 1300564586
dataset_size: 19702064281.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/cardcaptorsakura1998 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Card Captor Sakura (1998)
This is the image base of bangumi Card Captor Sakura (1998), we detected 59 characters, 8455 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2737 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 116 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 111 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 75 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 94 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 261 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 37 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 943 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 77 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 297 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 195 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 316 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 86 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 62 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 111 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 40 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 132 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 186 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 79 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 296 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 373 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 452 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 37 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 32 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 37 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 32 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 21 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 8 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 66 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 11 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 96 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 112 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 28 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 30 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 13 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 21 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 8 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 67 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 9 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 18 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 8 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 5 | [Download](57/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 345 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
liuyanchen1015/MULTI_VALUE_qqp_chaining_main_verbs | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 328469
num_examples: 1595
- name: test
num_bytes: 3188970
num_examples: 15981
- name: train
num_bytes: 2943698
num_examples: 14178
download_size: 4078015
dataset_size: 6461137
---
# Dataset Card for "MULTI_VALUE_qqp_chaining_main_verbs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/sst2_non_affix_neg | ---
dataset_info:
features:
- name: idx
dtype: int32
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: validation
num_bytes: 98088.14220183487
num_examples: 805
download_size: 66484
dataset_size: 98088.14220183487
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "sst2_non_affix_neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
llamafactory/xsum_tiny | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
license: apache-2.0
task_categories:
- summarization
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
This dataset is a subset of https://huggingface.co/datasets/EdinburghNLP/xsum.
The training set is composed of 2,000 examples of the original training set and the test set is composed of 1,000 examples of the original validation set.
|
yzhuang/autotree_automl_covertype_sgosdt_l256_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 205680000
num_examples: 10000
- name: validation
num_bytes: 205680000
num_examples: 10000
download_size: 149993354
dataset_size: 411360000
---
# Dataset Card for "autotree_automl_covertype_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openaccess-ai-collective/7b5e4ae0b864df6b32a7bffc40735059 | Invalid username or password. |
open-llm-leaderboard/details_alnrg2arg__blockchainlabs_7B_merged_test2_4_prune | ---
pretty_name: Evaluation run of alnrg2arg/blockchainlabs_7B_merged_test2_4_prune
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [alnrg2arg/blockchainlabs_7B_merged_test2_4_prune](https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune)\
\ 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_alnrg2arg__blockchainlabs_7B_merged_test2_4_prune\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-20T12:08:51.547790](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__blockchainlabs_7B_merged_test2_4_prune/blob/main/results_2024-01-20T12-08-51.547790.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\": 0.5235864683456122,\n\
\ \"acc_stderr\": 0.0342174975692429,\n \"acc_norm\": 0.5284479425508523,\n\
\ \"acc_norm_stderr\": 0.03496859005639417,\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.0173292345804091,\n \"mc2\": 0.5902640868436692,\n\
\ \"mc2_stderr\": 0.015985277759229078\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5887372013651877,\n \"acc_stderr\": 0.014379441068522084,\n\
\ \"acc_norm\": 0.60580204778157,\n \"acc_norm_stderr\": 0.014280522667467325\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5762796255725952,\n\
\ \"acc_stderr\": 0.004931372657129799,\n \"acc_norm\": 0.7774347739494125,\n\
\ \"acc_norm_stderr\": 0.004151185615952065\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5111111111111111,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5592105263157895,\n \"acc_stderr\": 0.04040311062490436,\n\
\ \"acc_norm\": 0.5592105263157895,\n \"acc_norm_stderr\": 0.04040311062490436\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.5320754716981132,\n \"acc_stderr\": 0.03070948699255655,\n\
\ \"acc_norm\": 0.5320754716981132,\n \"acc_norm_stderr\": 0.03070948699255655\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n\
\ \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.5694444444444444,\n\
\ \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n\
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4508670520231214,\n\
\ \"acc_stderr\": 0.037940126746970296,\n \"acc_norm\": 0.4508670520231214,\n\
\ \"acc_norm_stderr\": 0.037940126746970296\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.41702127659574467,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.41702127659574467,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3492063492063492,\n \"acc_stderr\": 0.02455229220934266,\n \"\
acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.02455229220934266\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6225806451612903,\n\
\ \"acc_stderr\": 0.02757596072327823,\n \"acc_norm\": 0.6225806451612903,\n\
\ \"acc_norm_stderr\": 0.02757596072327823\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.034819048444388045,\n\
\ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.034819048444388045\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.03756335775187896,\n\
\ \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03756335775187896\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6363636363636364,\n \"acc_stderr\": 0.03427308652999934,\n \"\
acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03427308652999934\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.03003114797764154,\n\
\ \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.03003114797764154\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.49743589743589745,\n \"acc_stderr\": 0.025350672979412195,\n\
\ \"acc_norm\": 0.49743589743589745,\n \"acc_norm_stderr\": 0.025350672979412195\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.46638655462184875,\n \"acc_stderr\": 0.03240501447690071,\n\
\ \"acc_norm\": 0.46638655462184875,\n \"acc_norm_stderr\": 0.03240501447690071\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7119266055045872,\n \"acc_stderr\": 0.019416445892636032,\n \"\
acc_norm\": 0.7119266055045872,\n \"acc_norm_stderr\": 0.019416445892636032\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.41203703703703703,\n \"acc_stderr\": 0.03356787758160835,\n \"\
acc_norm\": 0.41203703703703703,\n \"acc_norm_stderr\": 0.03356787758160835\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6421568627450981,\n \"acc_stderr\": 0.033644872860882996,\n \"\
acc_norm\": 0.6421568627450981,\n \"acc_norm_stderr\": 0.033644872860882996\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6962025316455697,\n \"acc_stderr\": 0.029936696387138608,\n \
\ \"acc_norm\": 0.6962025316455697,\n \"acc_norm_stderr\": 0.029936696387138608\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\": 0.5419847328244275,\n \"acc_stderr\": 0.04369802690578756,\n\
\ \"acc_norm\": 0.5419847328244275,\n \"acc_norm_stderr\": 0.04369802690578756\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6942148760330579,\n \"acc_stderr\": 0.04205953933884123,\n \"\
acc_norm\": 0.6942148760330579,\n \"acc_norm_stderr\": 0.04205953933884123\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.0471282125742677,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.0471282125742677\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6073619631901841,\n \"acc_stderr\": 0.03836740907831029,\n\
\ \"acc_norm\": 0.6073619631901841,\n \"acc_norm_stderr\": 0.03836740907831029\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6504854368932039,\n \"acc_stderr\": 0.047211885060971716,\n\
\ \"acc_norm\": 0.6504854368932039,\n \"acc_norm_stderr\": 0.047211885060971716\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\
\ \"acc_stderr\": 0.024662496845209807,\n \"acc_norm\": 0.8290598290598291,\n\
\ \"acc_norm_stderr\": 0.024662496845209807\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.719029374201788,\n\
\ \"acc_stderr\": 0.016073127851221232,\n \"acc_norm\": 0.719029374201788,\n\
\ \"acc_norm_stderr\": 0.016073127851221232\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.02651126136940925,\n\
\ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.02651126136940925\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3474860335195531,\n\
\ \"acc_stderr\": 0.01592556406020815,\n \"acc_norm\": 0.3474860335195531,\n\
\ \"acc_norm_stderr\": 0.01592556406020815\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5686274509803921,\n \"acc_stderr\": 0.02835895631342355,\n\
\ \"acc_norm\": 0.5686274509803921,\n \"acc_norm_stderr\": 0.02835895631342355\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5530546623794212,\n\
\ \"acc_stderr\": 0.028237769422085335,\n \"acc_norm\": 0.5530546623794212,\n\
\ \"acc_norm_stderr\": 0.028237769422085335\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5308641975308642,\n \"acc_stderr\": 0.027767689606833932,\n\
\ \"acc_norm\": 0.5308641975308642,\n \"acc_norm_stderr\": 0.027767689606833932\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4148936170212766,\n \"acc_stderr\": 0.0293922365846125,\n \
\ \"acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.0293922365846125\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3709256844850065,\n\
\ \"acc_stderr\": 0.012337391684530312,\n \"acc_norm\": 0.3709256844850065,\n\
\ \"acc_norm_stderr\": 0.012337391684530312\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4889705882352941,\n \"acc_stderr\": 0.030365446477275675,\n\
\ \"acc_norm\": 0.4889705882352941,\n \"acc_norm_stderr\": 0.030365446477275675\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5294117647058824,\n \"acc_stderr\": 0.020192808271433795,\n \
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.020192808271433795\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\
\ \"acc_stderr\": 0.04724577405731571,\n \"acc_norm\": 0.5818181818181818,\n\
\ \"acc_norm_stderr\": 0.04724577405731571\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.031680911612338825,\n\
\ \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.031680911612338825\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6965174129353234,\n\
\ \"acc_stderr\": 0.03251006816458618,\n \"acc_norm\": 0.6965174129353234,\n\
\ \"acc_norm_stderr\": 0.03251006816458618\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7076023391812866,\n \"acc_stderr\": 0.03488647713457922,\n\
\ \"acc_norm\": 0.7076023391812866,\n \"acc_norm_stderr\": 0.03488647713457922\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.0173292345804091,\n \"mc2\": 0.5902640868436692,\n\
\ \"mc2_stderr\": 0.015985277759229078\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275626\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.21455648218347234,\n \
\ \"acc_stderr\": 0.011307604104052885\n }\n}\n```"
repo_url: https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|arc:challenge|25_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|gsm8k|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hellaswag|10_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T12-08-51.547790.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-20T12-08-51.547790.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- '**/details_harness|winogrande|5_2024-01-20T12-08-51.547790.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-20T12-08-51.547790.parquet'
- config_name: results
data_files:
- split: 2024_01_20T12_08_51.547790
path:
- results_2024-01-20T12-08-51.547790.parquet
- split: latest
path:
- results_2024-01-20T12-08-51.547790.parquet
---
# Dataset Card for Evaluation run of alnrg2arg/blockchainlabs_7B_merged_test2_4_prune
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [alnrg2arg/blockchainlabs_7B_merged_test2_4_prune](https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4_prune) 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_alnrg2arg__blockchainlabs_7B_merged_test2_4_prune",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-20T12:08:51.547790](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__blockchainlabs_7B_merged_test2_4_prune/blob/main/results_2024-01-20T12-08-51.547790.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": 0.5235864683456122,
"acc_stderr": 0.0342174975692429,
"acc_norm": 0.5284479425508523,
"acc_norm_stderr": 0.03496859005639417,
"mc1": 0.42962056303549573,
"mc1_stderr": 0.0173292345804091,
"mc2": 0.5902640868436692,
"mc2_stderr": 0.015985277759229078
},
"harness|arc:challenge|25": {
"acc": 0.5887372013651877,
"acc_stderr": 0.014379441068522084,
"acc_norm": 0.60580204778157,
"acc_norm_stderr": 0.014280522667467325
},
"harness|hellaswag|10": {
"acc": 0.5762796255725952,
"acc_stderr": 0.004931372657129799,
"acc_norm": 0.7774347739494125,
"acc_norm_stderr": 0.004151185615952065
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5111111111111111,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.5111111111111111,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5592105263157895,
"acc_stderr": 0.04040311062490436,
"acc_norm": 0.5592105263157895,
"acc_norm_stderr": 0.04040311062490436
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5320754716981132,
"acc_stderr": 0.03070948699255655,
"acc_norm": 0.5320754716981132,
"acc_norm_stderr": 0.03070948699255655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111503,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111503
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4508670520231214,
"acc_stderr": 0.037940126746970296,
"acc_norm": 0.4508670520231214,
"acc_norm_stderr": 0.037940126746970296
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.04389869956808778,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.04389869956808778
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.41702127659574467,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.41702127659574467,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.044346007015849245,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.044346007015849245
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.4413793103448276,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3492063492063492,
"acc_stderr": 0.02455229220934266,
"acc_norm": 0.3492063492063492,
"acc_norm_stderr": 0.02455229220934266
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.043758884927270605,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.043758884927270605
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6225806451612903,
"acc_stderr": 0.02757596072327823,
"acc_norm": 0.6225806451612903,
"acc_norm_stderr": 0.02757596072327823
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.034819048444388045,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.034819048444388045
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.03756335775187896,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.03756335775187896
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.03427308652999934,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.03427308652999934
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7772020725388601,
"acc_stderr": 0.03003114797764154,
"acc_norm": 0.7772020725388601,
"acc_norm_stderr": 0.03003114797764154
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.49743589743589745,
"acc_stderr": 0.025350672979412195,
"acc_norm": 0.49743589743589745,
"acc_norm_stderr": 0.025350672979412195
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3037037037037037,
"acc_stderr": 0.028037929969114986,
"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.028037929969114986
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.46638655462184875,
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|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": {
"acc": 0.7640094711917916,
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},
"harness|gsm8k|5": {
"acc": 0.21455648218347234,
"acc_stderr": 0.011307604104052885
}
}
```
## 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] |
Existance/coding_train_data-0-of-5000 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2247021
num_examples: 4700
- name: validation
num_bytes: 141313
num_examples: 300
download_size: 912529
dataset_size: 2388334
---
# Dataset Card for "coding_train_data-0-of-5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/medium_articles_posts | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: authors
dtype: string
- name: timestamp
dtype: string
- name: tags
dtype: string
splits:
- name: train
num_bytes: 1044746687
num_examples: 192368
download_size: 601519297
dataset_size: 1044746687
---
# Medium Articles Posts Dataset
## Description
The Medium Articles Posts dataset contains a collection of articles published on the Medium platform. Each article entry includes information such as the article's title, main content or text, associated URL or link, authors' names, timestamps, and tags or categories.
## Dataset Info
The dataset consists of the following features:
- **title**: *(string)* The title of the Medium article.
- **text**: *(string)* The main content or text of the Medium article.
- **url**: *(string)* The URL or link to the Medium article.
- **authors**: *(string)* The authors or contributors of the Medium article.
- **timestamp**: *(string)* The timestamp or date when the Medium article was published.
- **tags**: *(string)* Tags or categories associated with the Medium article.
## Dataset Size
- **Total Dataset Size**: 1,044,746,687 bytes (approximately 1000 MB)
## Splits
The dataset is split into the following part:
- **Train**:
- Number of examples: 192,368
- Size: 1,044,746,687 bytes (approximately 1000 MB)
## Download Size
- **Compressed Download Size**: 601,519,297 bytes (approximately 600 MB)
### Usage example
```python
from datasets import load_dataset
#Load the dataset
dataset = load_dataset("Falah/medium_articles_posts")
``` |
domenicrosati/TruthfulQA | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: TruthfulQA
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
- closed-domain-qa
---
# Dataset Card for TruthfulQA
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/sylinrl/TruthfulQA](https://github.com/sylinrl/TruthfulQA)
- **Repository:** [https://github.com/sylinrl/TruthfulQA](https://github.com/sylinrl/TruthfulQA)
- **Paper:** [https://arxiv.org/abs/2109.07958](https://arxiv.org/abs/2109.07958)
### Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.
### Supported Tasks and Leaderboards
See: [Tasks](https://github.com/sylinrl/TruthfulQA#tasks)
### Languages
English
## Dataset Structure
### Data Instances
The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics.
### Data Fields
1. **Type**: Adversarial v Non-Adversarial Questions
2. **Category**: Category of misleading question
3. **Question**: The question
4. **Best Answer**: The best correct answer
5. **Correct Answers**: A set of correct answers. Delimited by `;`.
6. **Incorrect Answers**: A set of incorrect answers. Delimited by `;`.
7. **Source**: A source that supports the correct answers.
### Data Splits
Due to constraints of huggingface the dataset is loaded into a "train" split.
### Contributions
Thanks to [@sylinrl](https://github.com/sylinrl) for adding this dataset. |
burcusayin/pubmed_qa_labeled_fold0_source_binary_gp4_wrong_long_answers | ---
dataset_info:
features:
- name: QUESTION
dtype: string
- name: CONTEXTS
dtype: string
- name: final_decision
dtype: string
- name: LONG_ANSWER
dtype: string
- name: gpt4_completion
dtype: string
- name: gpt4_short_completion
dtype: string
- name: gpt4_long_completion
dtype: string
splits:
- name: gpt_test
num_bytes: 1489006
num_examples: 445
download_size: 743807
dataset_size: 1489006
configs:
- config_name: default
data_files:
- split: gpt_test
path: data/gpt_test-*
---
|
ovior/twitter_dataset_1713033254 | ---
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: 2391918
num_examples: 7292
download_size: 1353711
dataset_size: 2391918
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
another-symato/vietstock-raw | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 1613188331
num_examples: 711362
download_size: 763751078
dataset_size: 1613188331
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
odunola/food_intent | ---
license: apache-2.0
---
|
DeepFoldProtein/CATH_v4.3_S35_processed_512 | ---
dataset_info:
features:
- name: index
dtype: string
- name: ndom
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: domain_labels
sequence:
sequence: int64
- name: label
sequence:
sequence: float64
splits:
- name: train
num_bytes: 49247734191
num_examples: 23431
download_size: 9563677
dataset_size: 49247734191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HiTZ/xnli-eu | ---
license: cc-by-nc-4.0
language:
- eu
pretty_name: XNLI EU
size_categories:
- 1K<n<10K
dataset_info:
- config_name: eu
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- config_name: eu_mt
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- config_name: eu_native
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
configs:
- config_name: eu
data_files:
- split: train
path: xnli.train.eu.mt.tsv
- split: validation
path: xnli.dev.eu.tsv
- split: test
path: xnli.test.eu.tsv
- config_name: eu_mt
data_files:
- split: train
path: xnli.train.eu.mt.tsv
- split: validation
path: xnli.dev.eu.mt.tsv
- split: test
path: xnli.test.eu.mt.tsv
- config_name: eu_native
data_files:
- split: test
path: xnli.test.eu.native.tsv
task_categories:
- text-classification
---
# Dataset Card for XNLIeu
<!-- Provide a quick summary of the dataset. -->
XNLIeu is an extension of [XNLI](https://huggingface.co/datasets/xnli) translated from English to **Basque**. It has been designed as a cross-lingual dataset for the Natural Language Inference task, a text-classification task that consists on classifying pairs of sentences, a premise and a hypothesis, according to their semantic relation out of three possible labels: entailment, contradiction and neutral.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages.
We expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches.
The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step.
- **Language(s) (NLP):** Basque (eu)
- **License:** XNLIeu is derived from XNLI and distributed under its same license.
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** [Link to the GitHub Repository](https://github.com/hitz-zentroa/xnli-eu/)
- **Paper:** [Link to the Paper](https://arxiv.org/abs/2404.06996)
## Uses
XNLieu is meant as an cross-lingual evaluation dataset. It can be used in combination with the train sets of [XNLI](https://huggingface.co/datasets/xnli) for a cross-lingual zero-shot setting, and we provide a machine-translated train set in both "eu" and "eu_mt" splits to implement a translate-train setting.
## Dataset Structure
The dataset has three subsets:
- **eu**: XNLIeu, machine-translated and post-edited from English to Basque.
- **eu_MT**: XNLIeu<sub>MT</sub>, a machine-translated version prior post-edition.
- **eu_native**: An original, non-translated test set.
### Splits
| name |train |validation|test|
|-------------|-----:|---------:|---:|
|eu |392702| 2490|5010|
|eu_mt |392702| 2490|5010|
|eu_native |- | - |621 |
### Dataset Fields
All splits have the same fields: *premise*, *hypothesis* and *label*.
- **premise**: a string variable.
- **hypothesis**: a string variable.
- **label**: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
### Dataset Instances
An example from the "eu" split:
```
{
"premise": "Dena idazten saiatu nintzen"
"hypothesis": "Nire helburua gauzak idaztea zen.",
"label": 0,
}
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The biases of this dataset have been studied and reported in the paper.
<!--## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section.
RELLENAR-->
**BibTeX:**
```
@article{heredia2024xnlieu,
title={XNLIeu: a dataset for cross-lingual NLI in Basque},
author={Maite Heredia and Julen Etxaniz and Muitze Zulaika and Xabier Saralegi and Jeremy Barnes and Aitor Soroa},
year={2024},
eprint={2404.06996},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
Maite Heredia, Julen Etxaniz, Muitze Zulaika, Xabier Saralegi, Jeremy Barnes, & Aitor Soroa (2024). [XNLIeu: a dataset for cross-lingual NLI in Basque.](https://arxiv.org/abs/2404.06996)
<!--
## Dataset Card Contact
[More Information Needed]--> |
rajendrabaskota/hc3-wiki-intro-tokenized-max-len-512 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: text
dtype: string
- name: source
dtype: string
- name: label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 775237004
num_examples: 330347
- name: test
num_bytes: 40840334
num_examples: 17387
download_size: 429915523
dataset_size: 816077338
---
# Dataset Card for "hc3-wiki-intro-tokenized-max-len-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DBQ/Mr.Porter.Product.prices.Hong.Kong | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Hong Kong - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8934085
num_examples: 27206
download_size: 2064760
dataset_size: 8934085
---
# Mr Porter web scraped data
## About the website
Mr Porter operates within the **Ecommerce Fashion Retail** industry, one thats seeing a dynamic surge in the **Asia Pacific** region, particularly in **Hong Kong**. This metropolis, known for its fashion-forward populace and high internet penetration, boasts a thriving Ecommerce scene. Young, tech-savvy consumers are driving growth in online shopping, appreciating the ease and variety it provides. As per our dataset, we have specifically observed **Ecommerce product-list page (PLP) data** for **Mr Porter in Hong Kong**. This data provides valuable insights into market trends, consumer preferences, and competitive dynamics, all crucial for strategizing in this vibrant digital retail landscape.
## Link to **dataset**
[Hong Kong - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Hong%20Kong/r/reccGQkaol1aca5fH)
|
CyberHarem/f1_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of f1/F1/F1 (Girls' Frontline)
This is the dataset of f1/F1/F1 (Girls' Frontline), containing 10 images and their tags.
The core tags of this character are `hat, blue_eyes, brown_hair, long_hair, twintails`, 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 | 10 | 10.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/f1_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 6.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/f1_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 22 | 13.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/f1_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 10.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/f1_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 22 | 18.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/f1_girlsfrontline/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/f1_girlsfrontline',
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 | 10 |  |  |  |  |  | 1girl, solo, open_mouth, holding, looking_at_viewer, boots, fingerless_gloves, scarf, :d, rifle, shirt, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | open_mouth | holding | looking_at_viewer | boots | fingerless_gloves | scarf | :d | rifle | shirt | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:----------|:--------------------|:--------|:--------------------|:--------|:-----|:--------|:--------|:--------------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
|
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