datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Jouryjc/vm-training-data | ---
license: apache-2.0
task_categories:
- text-classification
language:
- zh
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
jamestalentium/xsum_100_test | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: output_text
dtype: string
- name: id
dtype: string
splits:
- name: test
num_bytes: 15613650.659431798
num_examples: 6614
download_size: 5619267
dataset_size: 15613650.659431798
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "xsum_100_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sahityas/goodreads-llama-7b-negated | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 29534
num_examples: 254
download_size: 16020
dataset_size: 29534
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TICK666/Basic-Math-Chinese-1M | ---
license: llama2
language:
- zh
pretty_name: Basic-Math-Chinese-1M
size_categories:
- 1M<n<10M
---
这是我做数学题的python脚本，做的可能不好，见谅
数学题包含了：
1.基础四则运算
2.一元一次方程
3.实际问题
联系方式：qq：2981447942
bilibili：一髅子Tick |
pnadel/latin_sentences | ---
dataset_info:
features:
- name: f_name
dtype: string
- name: title
dtype: string
- name: author
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 39199112.23995617
num_examples: 170421
- name: test
num_bytes: 13066600.760043832
num_examples: 56808
download_size: 25166966
dataset_size: 52265713.0
---
# Dataset Card for "latin_sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sajid73/SUBESCO-audio-dataset | ---
license: cc-by-4.0
task_categories:
- audio-classification
language:
- bn
pretty_name: SUST BANGLA EMOTIONAL SPEECH CORPUS
size_categories:
- 1K<n<10K
---
# SUST BANGLA EMOTIONAL SPEECH CORPUS
## Dataset Description
- **Homepage:** [bn_emotion_speech_corpus](https://huggingface.co/datasets/sustcsenlp/bn_emotion_speech_corpus)
- **Repository:**
- **Paper:** [SUBESCO PAPER](https://doi.org/10.1371/journal.pone.0250173)
- **Leaderboard:**
- **Point of Contact:** [Sadia Sultana](sadia-cse@sust.edu)
### Dataset Summary
SUBESCO is an audio-only emotional speech corpus of 7000 sentence-level utterances of the Bangla language. 20 professional actors (10 males and 10 females) participated in the recordings of 10 sentences for 7 target emotions. The emotions are Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. Total duration of the corpus is 7 hours 40 min 40 sec. Total size of the dataset is 2.03 GB. The dataset was evaluated by 50 raters (25 males, 25 females). Human perception test achieved a raw accuracy of 71%. All the details relating to creation, evaluation and analysis of SUBESCO have been described in the corresponding journal paper which has been published in Plos One.
https://doi.org/10.1371/journal.pone.0250173
### Downloading the data
```
from datasets import load_dataset
train = load_dataset("sajid73/SUBESCO-audio-dataset", split="train")
```
### Languages
This dataset contains `Bangla` Audio Data.
## Dataset Creation
This database was created as a part of PhD thesis project of the author Sadia Sultana. It was designed and developed by the author in the Department of Computer Science and Engineering of Shahjalal University of Science and Technology. Financial grant was supported by the university. If you use the dataset please cite SUBESCO and the corresponding academic journal publication in Plos One.
### Citation Information
```
@dataset{sadia_sultana_2021_4526477,
author = {Sadia Sultana},
title = {SUST Bangla Emotional Speech Corpus (SUBESCO)},
month = feb,
year = 2021,
note = {{This database was created as a part of PhD thesis
project of the author Sadia Sultana. It was
designed and developed by the author in the
Department of Computer Science and Engineering of
Shahjalal University of Science and Technology.
Financial grant was supported by the university.
If you use the dataset please cite SUBESCO and the
corresponding academic journal publication in Plos
One.}},
publisher = {Zenodo},
version = {version - 1.1},
doi = {10.5281/zenodo.4526477},
url = {https://doi.org/10.5281/zenodo.4526477}
}
```
### Contributors
| Name | University |
| ----------- | ----------- |
| Sadia Sultana | Shahjalal University of Science and Technology |
| Dr. M. Zafar Iqbal | Shahjalal University of Science and Technology |
| Dr. M. Shahidur Rahman | Shahjalal University of Science and Technology | |
tollefj/nor-instruct-cleaned | ---
dataset_info:
features:
- name: response
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 17694132
num_examples: 46283
download_size: 11503545
dataset_size: 17694132
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- nb
--- |
fkdosilovic/docee-event-classification | ---
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: DocEE
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- wiki
- news
- event-detection
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for DocEE Dataset
## Dataset Description
- **Homepage:**
- **Repository:** [DocEE Dataset repository](https://github.com/tongmeihan1995/docee)
- **Paper:** [DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction](https://aclanthology.org/2022.naacl-main.291/)
### Dataset Summary
DocEE dataset is an English-language dataset containing more than 27k news and Wikipedia articles. Dataset is primarily annotated and collected for large-scale document-level event extraction.
### Data Fields
- `title`: TODO
- `text`: TODO
- `event_type`: TODO
- `date`: TODO
- `metadata`: TODO
**Note: this repo contains only event detection portion of the dataset.**
### Data Splits
The dataset has 2 splits: _train_ and _test_. Train split contains 21949 documents while test split contains 5536 documents. In total, dataset contains 27485 documents classified into 59 event types.
#### Differences from the original split(s)
Originally, the dataset is split into three splits: train, validation and test. For the purposes of this repository, original splits were joined back together and divided into train and test splits while making sure that splits were stratified across document sources (news and wiki) and event types.
Originally, the `title` column additionally contained information from `date` and `metadata` columns. They are now separated into three columns: `date`, `metadata` and `title`. |
samp3209/528by528logos | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1204173958.858
num_examples: 8817
download_size: 1262219319
dataset_size: 1204173958.858
---
# Dataset Card for "528by528logos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aehus/bumblebee_3 | ---
dataset_info:
features:
- name: new_output
dtype: string
- name: new_input
dtype: string
- name: new_instruction
dtype: string
splits:
- name: train
num_bytes: 5258229
num_examples: 5457
download_size: 2695863
dataset_size: 5258229
---
# Dataset Card for "bumblebee_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_jondurbin__bagel-7b-v0.5 | ---
pretty_name: Evaluation run of jondurbin/bagel-7b-v0.5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/bagel-7b-v0.5](https://huggingface.co/jondurbin/bagel-7b-v0.5) 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_jondurbin__bagel-7b-v0.5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T22:49:18.958321](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-7b-v0.5/blob/main/results_2024-04-15T22-49-18.958321.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\": 0.6486454059115256,\n\
\ \"acc_stderr\": 0.03206535183196842,\n \"acc_norm\": 0.6522385693643847,\n\
\ \"acc_norm_stderr\": 0.032709754040873375,\n \"mc1\": 0.39167686658506734,\n\
\ \"mc1_stderr\": 0.01708779588176962,\n \"mc2\": 0.5576291439647237,\n\
\ \"mc2_stderr\": 0.015413348246562653\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6100682593856656,\n \"acc_stderr\": 0.01425295984889289,\n\
\ \"acc_norm\": 0.636518771331058,\n \"acc_norm_stderr\": 0.014056207319068283\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6347341167098187,\n\
\ \"acc_stderr\": 0.004805205798724572,\n \"acc_norm\": 0.8361880103565027,\n\
\ \"acc_norm_stderr\": 0.0036934848941794166\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\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\": 0.44,\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\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\
\ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\
\ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.0255064816981382,\n \"acc_norm\"\
: 0.4312169312169312,\n \"acc_norm_stderr\": 0.0255064816981382\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\
\ \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n\
\ \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n\
\ \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.02247325333276878,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.02247325333276878\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246571,\n \
\ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246571\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634335,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634335\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\
acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455333,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455333\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8396624472573839,\n \"acc_stderr\": 0.023884380925965665,\n \
\ \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.023884380925965665\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229136,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229136\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.013853724170922526,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.013853724170922526\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39217877094972065,\n\
\ \"acc_stderr\": 0.016329061073207442,\n \"acc_norm\": 0.39217877094972065,\n\
\ \"acc_norm_stderr\": 0.016329061073207442\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.023929155517351305,\n\
\ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.023929155517351305\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7459807073954984,\n\
\ \"acc_stderr\": 0.024723861504771696,\n \"acc_norm\": 0.7459807073954984,\n\
\ \"acc_norm_stderr\": 0.024723861504771696\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495026,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495026\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4804432855280313,\n\
\ \"acc_stderr\": 0.012760464028289299,\n \"acc_norm\": 0.4804432855280313,\n\
\ \"acc_norm_stderr\": 0.012760464028289299\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031215,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031215\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6650326797385621,\n \"acc_stderr\": 0.01909422816700032,\n \
\ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.01909422816700032\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.027979823538744546,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.027979823538744546\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482705,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482705\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39167686658506734,\n\
\ \"mc1_stderr\": 0.01708779588176962,\n \"mc2\": 0.5576291439647237,\n\
\ \"mc2_stderr\": 0.015413348246562653\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8097868981846882,\n \"acc_stderr\": 0.01103033579861744\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5034116755117514,\n \
\ \"acc_stderr\": 0.013772164105556732\n }\n}\n```"
repo_url: https://huggingface.co/jondurbin/bagel-7b-v0.5
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_15T22_49_18.958321
path:
- '**/details_harness|arc:challenge|25_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|gsm8k|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hellaswag|10_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T22-49-18.958321.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T22-49-18.958321.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- '**/details_harness|winogrande|5_2024-04-15T22-49-18.958321.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T22-49-18.958321.parquet'
- config_name: results
data_files:
- split: 2024_04_15T22_49_18.958321
path:
- results_2024-04-15T22-49-18.958321.parquet
- split: latest
path:
- results_2024-04-15T22-49-18.958321.parquet
---
# Dataset Card for Evaluation run of jondurbin/bagel-7b-v0.5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jondurbin/bagel-7b-v0.5](https://huggingface.co/jondurbin/bagel-7b-v0.5) 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_jondurbin__bagel-7b-v0.5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T22:49:18.958321](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__bagel-7b-v0.5/blob/main/results_2024-04-15T22-49-18.958321.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": 0.6486454059115256,
"acc_stderr": 0.03206535183196842,
"acc_norm": 0.6522385693643847,
"acc_norm_stderr": 0.032709754040873375,
"mc1": 0.39167686658506734,
"mc1_stderr": 0.01708779588176962,
"mc2": 0.5576291439647237,
"mc2_stderr": 0.015413348246562653
},
"harness|arc:challenge|25": {
"acc": 0.6100682593856656,
"acc_stderr": 0.01425295984889289,
"acc_norm": 0.636518771331058,
"acc_norm_stderr": 0.014056207319068283
},
"harness|hellaswag|10": {
"acc": 0.6347341167098187,
"acc_stderr": 0.004805205798724572,
"acc_norm": 0.8361880103565027,
"acc_norm_stderr": 0.0036934848941794166
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847415,
"acc_norm": 0.27,
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
daimyoturu/1 | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_qqp_plural_postposed | ---
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: 3950975
num_examples: 22759
- name: test
num_bytes: 38722182
num_examples: 223736
- name: train
num_bytes: 35333454
num_examples: 203295
download_size: 47016225
dataset_size: 78006611
---
# Dataset Card for "MULTI_VALUE_qqp_plural_postposed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Helsinki-NLP/opus_elhuyar | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
- eu
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: OpusElhuyar
dataset_info:
config_name: es-eu
features:
- name: translation
dtype:
translation:
languages:
- es
- eu
splits:
- name: train
num_bytes: 127833419
num_examples: 642348
download_size: 74270872
dataset_size: 127833419
configs:
- config_name: es-eu
data_files:
- split: train
path: es-eu/train-*
default: true
---
# Dataset Card for [opus_elhuyar]
## 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:**[Opus Elhuyar](http://opus.nlpl.eu/Elhuyar.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset provided by the foundation Elhuyar (http://webcorpusak.elhuyar.eus/sarrera_paraleloa.html) and submitted to OPUS by Joseba Garcia Beaumont
### Supported Tasks and Leaderboards
The underlying task is machine translation from Spanish to Basque
### Languages
Spanish to Basque
## 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
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
tobiolatunji/afrispeech-200 | ---
pretty_name: AfriSpeech-200
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:
features:
- name: user_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 1722002133
num_examples: 58000
- name: dev
num_bytes: 86120227
num_examples: 3231
download_size: 1475540500
dataset_size: 1808122360
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the
identity of speakers in the Common Voice dataset.
---
# Dataset Card for AfriSpeech-200
## Table of Contents
- [Dataset Card for AfriSpeech-200](#dataset-card-for-afrispeech-200)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [How to use](#how-to-use)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- **Repository:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- **Paper:** [AfriSpeech-200: Pan-African accented speech dataset for clinical and general domain ASR](https://github.com/intron-innovation/AfriSpeech-Dataset-Paper)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Intron Innovation](mailto:intron@intron.io)
### Dataset Summary
AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; a dataset with 120 African accents from 13 countries and 2,463 unique African speakers.
Our goal is to raise awareness for and advance Pan-African English ASR research, especially for the clinical domain.
## 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.
```python
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "all")
```
The entire dataset is ~120GB and may take about 2hrs to download depending on internet speed/bandwidth. If you have disk space or bandwidth limitations, you can use `streaming` mode described below to work with smaller subsets of the data.
Alterntively you are able to pass a config to the `load_dataset` function and download only a subset of the data corresponding to a specific accent of interest. The example provided below is `isizulu`.
For example, to download the isizulu config, simply specify the corresponding accent config name. The list of supported accents is provided in the `accent list` section below:
```python
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", 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.
```python
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
print(next(iter(afrispeech)))
print(list(afrispeech.take(5)))
```
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
batch_sampler = BatchSampler(RandomSampler(afrispeech), batch_size=32, drop_last=False)
dataloader = DataLoader(afrispeech, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
dataloader = DataLoader(afrispeech, batch_size=32)
```
### Caveats
Note that till the end of the ongoing [AfriSpeech ASR Challenge event](https://zindi.africa/competitions/intron-afrispeech-200-automatic-speech-recognition-challenge) (Feb - May 2023), the transcripts in the validation set are hidden and the test set will be unreleased till May 19, 2023.
### Fine-tuning Colab tutorial
To walk through a complete colab tutorial that finetunes a wav2vec2 model on the afrispeech-200 dataset with `transformers`, take a look at this colab notebook [afrispeech/wav2vec2-colab-tutorial](https://colab.research.google.com/drive/1uZYew6pcgN6UE6sFDLohxD_HKivvDXzD?usp=sharing).
### Supported Tasks and Leaderboards
- Automatic Speech Recognition
- Speech Synthesis (Text-to-Speech)
### Languages
English (Accented)
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called `path` and its transcription, called `transcript`. Some additional information about the speaker is provided.
```
{
'speaker_id': 'b545a4ca235a7b72688a1c0b3eb6bde6',
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'audio_id': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397',
'audio': {
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'array': array([0.00018311, 0.00061035, 0.00012207, ..., 0.00192261, 0.00195312, 0.00216675]),
'sampling_rate': 44100},
'transcript': 'His mother is in her 50 s and has hypertension .',
'age_group': '26-40',
'gender': 'Male',
'accent': 'yoruba',
'domain': 'clinical',
'country': 'US',
'duration': 3.241995464852608
}
```
### Data Fields
- speaker_id: An id for which speaker (voice) made the recording
- path: The path to the audio file
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- transcript: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train, dev, and test.
Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
- Total Number of Unique Speakers: 2,463
- Female/Male/Other Ratio: 57.11/42.41/0.48
- Data was first split on speakers. Speakers in Train/Dev/Test do not cross partitions
| | Train | Dev | Test |
| ----------- | ----------- | ----------- | ----------- |
| # Speakers | 1466 | 247 | 750 |
| # Seconds | 624228.83 | 31447.09 | 67559.10 |
| # Hours | 173.4 | 8.74 | 18.77 |
| # Accents | 71 | 45 | 108 |
| Avg secs/speaker | 425.81 | 127.32 | 90.08 |
| Avg num clips/speaker | 39.56 | 13.08 | 8.46 |
| Avg num speakers/accent | 20.65 | 5.49 | 6.94 |
| Avg secs/accent | 8791.96 | 698.82 | 625.55 |
| # clips general domain | 21682 | 1407 | 2723 |
| # clips clinical domain | 36318 | 1824 | 3623 |
## Dataset Creation
### Curation Rationale
Africa has a very low doctor-to-patient ratio.
At very busy clinics, doctors could see 30+ patients per day-- a heavy patient burden compared with
developed countries-- but productivity tools such as clinical automatic speech recognition
(ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous,
in developed nations, and clinician-reported performance of commercial clinical ASR systems
is generally satisfactory. Furthermore, the recent performance of general domain ASR is
approaching human accuracy. However, several gaps exist. Several publications have
highlighted racial bias with speech-to-text algorithms and performance on minority
accents lags significantly. To our knowledge, there is no publicly available research or
benchmark on accented African clinical ASR, and speech data is non-existent for the
majority of African accents. We release AfriSpeech, 200hrs of Pan-African speech,
67,577 clips from 2,463 unique speakers, across 120 indigenous accents from 13 countries for
clinical and general domain ASR, a benchmark test set, with publicly available pre-trained
models with SOTA performance on the AfriSpeech benchmark.
### Source Data
#### Country Stats
| Country | Clips | Speakers | Duration (seconds) | Duration (hrs) |
| ----------- | ----------- | ----------- | ----------- | ----------- |
| NG | 45875 | 1979 | 512646.88 | 142.40 |
| KE | 8304 | 137 | 75195.43 | 20.89 |
| ZA | 7870 | 223 | 81688.11 | 22.69 |
| GH | 2018 | 37 | 18581.13 | 5.16 |
| BW | 1391 | 38 | 14249.01 | 3.96 |
| UG | 1092 | 26 | 10420.42 | 2.89 |
| RW | 469 | 9 | 5300.99 | 1.47 |
| US | 219 | 5 | 1900.98 | 0.53 |
| TR | 66 | 1 | 664.01 | 0.18 |
| ZW | 63 | 3 | 635.11 | 0.18 |
| MW | 60 | 1 | 554.61 | 0.15 |
| TZ | 51 | 2 | 645.51 | 0.18 |
| LS | 7 | 1 | 78.40 | 0.02 |
#### Accent Stats
| Accent | Clips | Speakers | Duration (s) | Country | Splits |
| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| yoruba | 15407 | 683 | 161587.55 | US,NG | train,test,dev |
| igbo | 8677 | 374 | 93035.79 | US,NG,ZA | train,test,dev |
| swahili | 6320 | 119 | 55932.82 | KE,TZ,ZA,UG | train,test,dev |
| hausa | 5765 | 248 | 70878.67 | NG | train,test,dev |
| ijaw | 2499 | 105 | 33178.9 | NG | train,test,dev |
| afrikaans | 2048 | 33 | 20586.49 | ZA | train,test,dev |
| idoma | 1877 | 72 | 20463.6 | NG | train,test,dev |
| zulu | 1794 | 52 | 18216.97 | ZA,TR,LS | dev,train,test |
| setswana | 1588 | 39 | 16553.22 | BW,ZA | dev,test,train |
| twi | 1566 | 22 | 14340.12 | GH | test,train,dev |
| isizulu | 1048 | 48 | 10376.09 | ZA | test,train,dev |
| igala | 919 | 31 | 9854.72 | NG | train,test |
| izon | 838 | 47 | 9602.53 | NG | train,dev,test |
| kiswahili | 827 | 6 | 8988.26 | KE | train,test |
| ebira | 757 | 42 | 7752.94 | NG | train,test,dev |
| luganda | 722 | 22 | 6768.19 | UG,BW,KE | test,dev,train |
| urhobo | 646 | 32 | 6685.12 | NG | train,dev,test |
| nembe | 578 | 16 | 6644.72 | NG | train,test,dev |
| ibibio | 570 | 39 | 6489.29 | NG | train,test,dev |
| pidgin | 514 | 20 | 5871.57 | NG | test,train,dev |
| luhya | 508 | 4 | 4497.02 | KE | train,test |
| kinyarwanda | 469 | 9 | 5300.99 | RW | train,test,dev |
| xhosa | 392 | 12 | 4604.84 | ZA | train,dev,test |
| tswana | 387 | 18 | 4148.58 | ZA,BW | train,test,dev |
| esan | 380 | 13 | 4162.63 | NG | train,test,dev |
| alago | 363 | 8 | 3902.09 | NG | train,test |
| tshivenda | 353 | 5 | 3264.77 | ZA | test,train |
| fulani | 312 | 18 | 5084.32 | NG | test,train |
| isoko | 298 | 16 | 4236.88 | NG | train,test,dev |
| akan (fante) | 295 | 9 | 2848.54 | GH | train,dev,test |
| ikwere | 293 | 14 | 3480.43 | NG | test,train,dev |
| sepedi | 275 | 10 | 2751.68 | ZA | dev,test,train |
| efik | 269 | 11 | 2559.32 | NG | test,train,dev |
| edo | 237 | 12 | 1842.32 | NG | train,test,dev |
| luo | 234 | 4 | 2052.25 | UG,KE | test,train,dev |
| kikuyu | 229 | 4 | 1949.62 | KE | train,test,dev |
| bekwarra | 218 | 3 | 2000.46 | NG | train,test |
| isixhosa | 210 | 9 | 2100.28 | ZA | train,dev,test |
| hausa/fulani | 202 | 3 | 2213.53 | NG | test,train |
| epie | 202 | 6 | 2320.21 | NG | train,test |
| isindebele | 198 | 2 | 1759.49 | ZA | train,test |
| venda and xitsonga | 188 | 2 | 2603.75 | ZA | train,test |
| sotho | 182 | 4 | 2082.21 | ZA | dev,test,train |
| akan | 157 | 6 | 1392.47 | GH | test,train |
| nupe | 156 | 9 | 1608.24 | NG | dev,train,test |
| anaang | 153 | 8 | 1532.56 | NG | test,dev |
| english | 151 | 11 | 2445.98 | NG | dev,test |
| afemai | 142 | 2 | 1877.04 | NG | train,test |
| shona | 138 | 8 | 1419.98 | ZA,ZW | test,train,dev |
| eggon | 137 | 5 | 1833.77 | NG | test |
| luganda and kiswahili | 134 | 1 | 1356.93 | UG | train |
| ukwuani | 133 | 7 | 1269.02 | NG | test |
| sesotho | 132 | 10 | 1397.16 | ZA | train,dev,test |
| benin | 124 | 4 | 1457.48 | NG | train,test |
| kagoma | 123 | 1 | 1781.04 | NG | train |
| nasarawa eggon | 120 | 1 | 1039.99 | NG | train |
| tiv | 120 | 14 | 1084.52 | NG | train,test,dev |
| south african english | 119 | 2 | 1643.82 | ZA | train,test |
| borana | 112 | 1 | 1090.71 | KE | train |
| swahili ,luganda ,arabic | 109 | 1 | 929.46 | UG | train |
| ogoni | 109 | 4 | 1629.7 | NG | train,test |
| mada | 109 | 2 | 1786.26 | NG | test |
| bette | 106 | 4 | 930.16 | NG | train,test |
| berom | 105 | 4 | 1272.99 | NG | dev,test |
| bini | 104 | 4 | 1499.75 | NG | test |
| ngas | 102 | 3 | 1234.16 | NG | train,test |
| etsako | 101 | 4 | 1074.53 | NG | train,test |
| okrika | 100 | 3 | 1887.47 | NG | train,test |
| venda | 99 | 2 | 938.14 | ZA | train,test |
| siswati | 96 | 5 | 1367.45 | ZA | dev,train,test |
| damara | 92 | 1 | 674.43 | NG | train |
| yoruba, hausa | 89 | 5 | 928.98 | NG | test |
| southern sotho | 89 | 1 | 889.73 | ZA | train |
| kanuri | 86 | 7 | 1936.78 | NG | test,dev |
| itsekiri | 82 | 3 | 778.47 | NG | test,dev |
| ekpeye | 80 | 2 | 922.88 | NG | test |
| mwaghavul | 78 | 2 | 738.02 | NG | test |
| bajju | 72 | 2 | 758.16 | NG | test |
| luo, swahili | 71 | 1 | 616.57 | KE | train |
| dholuo | 70 | 1 | 669.07 | KE | train |
| ekene | 68 | 1 | 839.31 | NG | test |
| jaba | 65 | 2 | 540.66 | NG | test |
| ika | 65 | 4 | 576.56 | NG | test,dev |
| angas | 65 | 1 | 589.99 | NG | test |
| ateso | 63 | 1 | 624.28 | UG | train |
| brass | 62 | 2 | 900.04 | NG | test |
| ikulu | 61 | 1 | 313.2 | NG | test |
| eleme | 60 | 2 | 1207.92 | NG | test |
| chichewa | 60 | 1 | 554.61 | MW | train |
| oklo | 58 | 1 | 871.37 | NG | test |
| meru | 58 | 2 | 865.07 | KE | train,test |
| agatu | 55 | 1 | 369.11 | NG | test |
| okirika | 54 | 1 | 792.65 | NG | test |
| igarra | 54 | 1 | 562.12 | NG | test |
| ijaw(nembe) | 54 | 2 | 537.56 | NG | test |
| khana | 51 | 2 | 497.42 | NG | test |
| ogbia | 51 | 4 | 461.15 | NG | test,dev |
| gbagyi | 51 | 4 | 693.43 | NG | test |
| portuguese | 50 | 1 | 525.02 | ZA | train |
| delta | 49 | 2 | 425.76 | NG | test |
| bassa | 49 | 1 | 646.13 | NG | test |
| etche | 49 | 1 | 637.48 | NG | test |
| kubi | 46 | 1 | 495.21 | NG | test |
| jukun | 44 | 2 | 362.12 | NG | test |
| igbo and yoruba | 43 | 2 | 466.98 | NG | test |
| urobo | 43 | 3 | 573.14 | NG | test |
| kalabari | 42 | 5 | 305.49 | NG | test |
| ibani | 42 | 1 | 322.34 | NG | test |
| obolo | 37 | 1 | 204.79 | NG | test |
| idah | 34 | 1 | 533.5 | NG | test |
| bassa-nge/nupe | 31 | 3 | 267.42 | NG | test,dev |
| yala mbembe | 29 | 1 | 237.27 | NG | test |
| eket | 28 | 1 | 238.85 | NG | test |
| afo | 26 | 1 | 171.15 | NG | test |
| ebiobo | 25 | 1 | 226.27 | NG | test |
| nyandang | 25 | 1 | 230.41 | NG | test |
| ishan | 23 | 1 | 194.12 | NG | test |
| bagi | 20 | 1 | 284.54 | NG | test |
| estako | 20 | 1 | 480.78 | NG | test |
| gerawa | 13 | 1 | 342.15 | NG | test |
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
The dataset was initially prepared by Intron and refined for public release by CLAIR Lab.
### Licensing Information
Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))
### Citation Information
@article{olatunji2023afrispeech,
title={AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR},
author={Olatunji, Tobi and Afonja, Tejumade and Yadavalli, Aditya and Emezue, Chris Chinenye and Singh, Sahib and Dossou, Bonaventure FP and Osuchukwu, Joanne and Osei, Salomey and Tonja, Atnafu Lambebo and Etori, Naome and others},
journal={arXiv preprint arXiv:2310.00274},
year={2023}
}
### Contributions
Thanks to [@tobiolatunji](https://github.com/tobiolatunji) for adding this dataset. |
Jason773/w1024_part1 | ---
dataset_info:
features:
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dtype: string
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splits:
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download_size: 3717646259
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configs:
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path: data/train-*
---
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880 | ---
dataset_info:
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download_size: 1174480
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---
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bh8648/esg2 | ---
dataset_info:
features:
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "esg2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-94000 | ---
dataset_info:
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---
|
open-llm-leaderboard/details_BarraHome__Mistroll-7B-v0.2-16bit | ---
pretty_name: Evaluation run of BarraHome/Mistroll-7B-v0.2-16bit
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BarraHome/Mistroll-7B-v0.2-16bit](https://huggingface.co/BarraHome/Mistroll-7B-v0.2-16bit)\
\ 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_BarraHome__Mistroll-7B-v0.2-16bit\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-22T14:27:00.887610](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Mistroll-7B-v0.2-16bit/blob/main/results_2024-02-22T14-27-00.887610.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\": 0.6035900946907772,\n\
\ \"acc_stderr\": 0.03332626012735341,\n \"acc_norm\": 0.608197043808571,\n\
\ \"acc_norm_stderr\": 0.03400206342738601,\n \"mc1\": 0.5226438188494492,\n\
\ \"mc1_stderr\": 0.01748554225848964,\n \"mc2\": 0.6765488433253143,\n\
\ \"mc2_stderr\": 0.015262726337203318\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5742320819112628,\n \"acc_stderr\": 0.01444946427886881,\n\
\ \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.0141696645203031\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6611232822146983,\n\
\ \"acc_stderr\": 0.004723605376936912,\n \"acc_norm\": 0.8485361481776539,\n\
\ \"acc_norm_stderr\": 0.0035776774950640826\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.039531733777491945,\n\
\ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.039531733777491945\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n\
\ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\
\ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\
\ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\
\ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n\
\ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159795,\n \"\
acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159795\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6709677419354839,\n \"acc_stderr\": 0.026729499068349958,\n \"\
acc_norm\": 0.6709677419354839,\n \"acc_norm_stderr\": 0.026729499068349958\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\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153314,\n\
\ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153314\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5564102564102564,\n \"acc_stderr\": 0.0251891498947642,\n \
\ \"acc_norm\": 0.5564102564102564,\n \"acc_norm_stderr\": 0.0251891498947642\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652457,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652457\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\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\"\
: 0.8018348623853211,\n \"acc_stderr\": 0.017090573804217905,\n \"\
acc_norm\": 0.8018348623853211,\n \"acc_norm_stderr\": 0.017090573804217905\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4351851851851852,\n \"acc_stderr\": 0.03381200005643525,\n \"\
acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.03381200005643525\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"\
acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \
\ \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6322869955156951,\n\
\ \"acc_stderr\": 0.03236198350928275,\n \"acc_norm\": 0.6322869955156951,\n\
\ \"acc_norm_stderr\": 0.03236198350928275\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.040393149787245605,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.040393149787245605\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\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n\
\ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597552,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597552\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7752234993614304,\n\
\ \"acc_stderr\": 0.014927447101937148,\n \"acc_norm\": 0.7752234993614304,\n\
\ \"acc_norm_stderr\": 0.014927447101937148\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6676300578034682,\n \"acc_stderr\": 0.025361168749688225,\n\
\ \"acc_norm\": 0.6676300578034682,\n \"acc_norm_stderr\": 0.025361168749688225\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34413407821229053,\n\
\ \"acc_stderr\": 0.015889221313307094,\n \"acc_norm\": 0.34413407821229053,\n\
\ \"acc_norm_stderr\": 0.015889221313307094\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.02671611838015685,\n\
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.02671611838015685\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.02604176620271716,\n\
\ \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.02604176620271716\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \
\ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4282920469361147,\n\
\ \"acc_stderr\": 0.012638223880313161,\n \"acc_norm\": 0.4282920469361147,\n\
\ \"acc_norm_stderr\": 0.012638223880313161\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\
\ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6143790849673203,\n \"acc_stderr\": 0.019691459052354022,\n \
\ \"acc_norm\": 0.6143790849673203,\n \"acc_norm_stderr\": 0.019691459052354022\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7562189054726368,\n\
\ \"acc_stderr\": 0.03036049015401464,\n \"acc_norm\": 0.7562189054726368,\n\
\ \"acc_norm_stderr\": 0.03036049015401464\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366255,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366255\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5226438188494492,\n\
\ \"mc1_stderr\": 0.01748554225848964,\n \"mc2\": 0.6765488433253143,\n\
\ \"mc2_stderr\": 0.015262726337203318\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.011850040124850508\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.40181956027293403,\n \
\ \"acc_stderr\": 0.013504357787494039\n }\n}\n```"
repo_url: https://huggingface.co/BarraHome/Mistroll-7B-v0.2-16bit
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|arc:challenge|25_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|gsm8k|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hellaswag|10_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-22T14-27-00.887610.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-22T14-27-00.887610.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- '**/details_harness|winogrande|5_2024-02-22T14-27-00.887610.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-22T14-27-00.887610.parquet'
- config_name: results
data_files:
- split: 2024_02_22T14_27_00.887610
path:
- results_2024-02-22T14-27-00.887610.parquet
- split: latest
path:
- results_2024-02-22T14-27-00.887610.parquet
---
# Dataset Card for Evaluation run of BarraHome/Mistroll-7B-v0.2-16bit
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BarraHome/Mistroll-7B-v0.2-16bit](https://huggingface.co/BarraHome/Mistroll-7B-v0.2-16bit) 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_BarraHome__Mistroll-7B-v0.2-16bit",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-22T14:27:00.887610](https://huggingface.co/datasets/open-llm-leaderboard/details_BarraHome__Mistroll-7B-v0.2-16bit/blob/main/results_2024-02-22T14-27-00.887610.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": 0.6035900946907772,
"acc_stderr": 0.03332626012735341,
"acc_norm": 0.608197043808571,
"acc_norm_stderr": 0.03400206342738601,
"mc1": 0.5226438188494492,
"mc1_stderr": 0.01748554225848964,
"mc2": 0.6765488433253143,
"mc2_stderr": 0.015262726337203318
},
"harness|arc:challenge|25": {
"acc": 0.5742320819112628,
"acc_stderr": 0.01444946427886881,
"acc_norm": 0.6220136518771331,
"acc_norm_stderr": 0.0141696645203031
},
"harness|hellaswag|10": {
"acc": 0.6611232822146983,
"acc_stderr": 0.004723605376936912,
"acc_norm": 0.8485361481776539,
"acc_norm_stderr": 0.0035776774950640826
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.039531733777491945,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.039531733777491945
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6597222222222222,
"acc_stderr": 0.039621355734862175,
"acc_norm": 0.6597222222222222,
"acc_norm_stderr": 0.039621355734862175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5838150289017341,
"acc_stderr": 0.03758517775404947,
"acc_norm": 0.5838150289017341,
"acc_norm_stderr": 0.03758517775404947
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5148936170212766,
"acc_stderr": 0.03267151848924777,
"acc_norm": 0.5148936170212766,
"acc_norm_stderr": 0.03267151848924777
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.04657047260594963,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.04657047260594963
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.37566137566137564,
"acc_stderr": 0.024942368931159795,
"acc_norm": 0.37566137566137564,
"acc_norm_stderr": 0.024942368931159795
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.04390259265377563,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.04390259265377563
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6709677419354839,
"acc_stderr": 0.026729499068349958,
"acc_norm": 0.6709677419354839,
"acc_norm_stderr": 0.026729499068349958
},
"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": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.03453131801885417,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.03453131801885417
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365897,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.844559585492228,
"acc_stderr": 0.026148483469153314,
"acc_norm": 0.844559585492228,
"acc_norm_stderr": 0.026148483469153314
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5564102564102564,
"acc_stderr": 0.0251891498947642,
"acc_norm": 0.5564102564102564,
"acc_norm_stderr": 0.0251891498947642
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.02866120111652457,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.02866120111652457
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.031282177063684614
},
"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": 0.8018348623853211,
"acc_stderr": 0.017090573804217905,
"acc_norm": 0.8018348623853211,
"acc_norm_stderr": 0.017090573804217905
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4351851851851852,
"acc_stderr": 0.03381200005643525,
"acc_norm": 0.4351851851851852,
"acc_norm_stderr": 0.03381200005643525
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.03019028245350195,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.03019028245350195
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7510548523206751,
"acc_stderr": 0.028146970599422644,
"acc_norm": 0.7510548523206751,
"acc_norm_stderr": 0.028146970599422644
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6322869955156951,
"acc_stderr": 0.03236198350928275,
"acc_norm": 0.6322869955156951,
"acc_norm_stderr": 0.03236198350928275
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6946564885496184,
"acc_stderr": 0.040393149787245605,
"acc_norm": 0.6946564885496184,
"acc_norm_stderr": 0.040393149787245605
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.04414343666854933,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.04414343666854933
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7300613496932515,
"acc_stderr": 0.03487825168497892,
"acc_norm": 0.7300613496932515,
"acc_norm_stderr": 0.03487825168497892
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690878,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690878
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597552,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597552
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7752234993614304,
"acc_stderr": 0.014927447101937148,
"acc_norm": 0.7752234993614304,
"acc_norm_stderr": 0.014927447101937148
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6676300578034682,
"acc_stderr": 0.025361168749688225,
"acc_norm": 0.6676300578034682,
"acc_norm_stderr": 0.025361168749688225
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.34413407821229053,
"acc_stderr": 0.015889221313307094,
"acc_norm": 0.34413407821229053,
"acc_norm_stderr": 0.015889221313307094
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6797385620915033,
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"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.02671611838015685
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6752411575562701,
"acc_stderr": 0.026596782287697043,
"acc_norm": 0.6752411575562701,
"acc_norm_stderr": 0.026596782287697043
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6759259259259259,
"acc_stderr": 0.02604176620271716,
"acc_norm": 0.6759259259259259,
"acc_norm_stderr": 0.02604176620271716
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.450354609929078,
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"acc_norm": 0.450354609929078,
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},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.4282920469361147,
"acc_norm_stderr": 0.012638223880313161
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5992647058823529,
"acc_stderr": 0.029768263528933105,
"acc_norm": 0.5992647058823529,
"acc_norm_stderr": 0.029768263528933105
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6143790849673203,
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"acc_norm": 0.6143790849673203,
"acc_norm_stderr": 0.019691459052354022
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.0282638899437846,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.0282638899437846
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7562189054726368,
"acc_stderr": 0.03036049015401464,
"acc_norm": 0.7562189054726368,
"acc_norm_stderr": 0.03036049015401464
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366255,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366255
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835816,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835816
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
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"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5226438188494492,
"mc1_stderr": 0.01748554225848964,
"mc2": 0.6765488433253143,
"mc2_stderr": 0.015262726337203318
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.011850040124850508
},
"harness|gsm8k|5": {
"acc": 0.40181956027293403,
"acc_stderr": 0.013504357787494039
}
}
```
## 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]
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### Dataset Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
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CVasNLPExperiments/VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0
num_bytes: 2443
num_examples: 2
download_size: 10579
dataset_size: 2443
---
# Dataset Card for "VQAv2_sample_validation_text_davinci_002_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
loicmagne/open-subtitles-bitext-mining | ---
configs:
- config_name: af-ar
data_files: "data/af-ar.jsonl"
- config_name: af-bg
data_files: "data/af-bg.jsonl"
- config_name: af-bn
data_files: "data/af-bn.jsonl"
- config_name: af-bs
data_files: "data/af-bs.jsonl"
- config_name: af-cs
data_files: "data/af-cs.jsonl"
- config_name: af-da
data_files: "data/af-da.jsonl"
- config_name: af-de
data_files: "data/af-de.jsonl"
- config_name: af-el
data_files: "data/af-el.jsonl"
- config_name: af-en
data_files: "data/af-en.jsonl"
- config_name: af-eo
data_files: "data/af-eo.jsonl"
- config_name: af-es
data_files: "data/af-es.jsonl"
- config_name: af-et
data_files: "data/af-et.jsonl"
- config_name: af-fa
data_files: "data/af-fa.jsonl"
- config_name: af-fi
data_files: "data/af-fi.jsonl"
- config_name: af-fr
data_files: "data/af-fr.jsonl"
- config_name: af-he
data_files: "data/af-he.jsonl"
- config_name: af-hi
data_files: "data/af-hi.jsonl"
- config_name: af-hr
data_files: "data/af-hr.jsonl"
- config_name: af-hu
data_files: "data/af-hu.jsonl"
- config_name: af-id
data_files: "data/af-id.jsonl"
- config_name: af-it
data_files: "data/af-it.jsonl"
- config_name: af-ja
data_files: "data/af-ja.jsonl"
- config_name: af-lt
data_files: "data/af-lt.jsonl"
- config_name: af-lv
data_files: "data/af-lv.jsonl"
- config_name: af-mk
data_files: "data/af-mk.jsonl"
- config_name: af-ml
data_files: "data/af-ml.jsonl"
- config_name: af-ms
data_files: "data/af-ms.jsonl"
- config_name: af-nl
data_files: "data/af-nl.jsonl"
- config_name: af-no
data_files: "data/af-no.jsonl"
- config_name: af-pl
data_files: "data/af-pl.jsonl"
- config_name: af-pt
data_files: "data/af-pt.jsonl"
- config_name: af-ro
data_files: "data/af-ro.jsonl"
- config_name: af-ru
data_files: "data/af-ru.jsonl"
- config_name: af-si
data_files: "data/af-si.jsonl"
- config_name: af-sk
data_files: "data/af-sk.jsonl"
- config_name: af-sl
data_files: "data/af-sl.jsonl"
- config_name: af-sq
data_files: "data/af-sq.jsonl"
- config_name: af-sr
data_files: "data/af-sr.jsonl"
- config_name: af-sv
data_files: "data/af-sv.jsonl"
- config_name: af-ta
data_files: "data/af-ta.jsonl"
- config_name: af-th
data_files: "data/af-th.jsonl"
- config_name: af-tr
data_files: "data/af-tr.jsonl"
- config_name: af-uk
data_files: "data/af-uk.jsonl"
- config_name: af-vi
data_files: "data/af-vi.jsonl"
- config_name: af-pt_br
data_files: "data/af-pt_br.jsonl"
- config_name: af-ze_en
data_files: "data/af-ze_en.jsonl"
- config_name: af-zh_cn
data_files: "data/af-zh_cn.jsonl"
- config_name: af-zh_tw
data_files: "data/af-zh_tw.jsonl"
- config_name: ar-bg
data_files: "data/ar-bg.jsonl"
- config_name: ar-bn
data_files: "data/ar-bn.jsonl"
- config_name: ar-br
data_files: "data/ar-br.jsonl"
- config_name: ar-bs
data_files: "data/ar-bs.jsonl"
- config_name: ar-ca
data_files: "data/ar-ca.jsonl"
- config_name: ar-cs
data_files: "data/ar-cs.jsonl"
- config_name: ar-da
data_files: "data/ar-da.jsonl"
- config_name: ar-de
data_files: "data/ar-de.jsonl"
- config_name: ar-el
data_files: "data/ar-el.jsonl"
- config_name: ar-en
data_files: "data/ar-en.jsonl"
- config_name: ar-eo
data_files: "data/ar-eo.jsonl"
- config_name: ar-es
data_files: "data/ar-es.jsonl"
- config_name: ar-et
data_files: "data/ar-et.jsonl"
- config_name: ar-eu
data_files: "data/ar-eu.jsonl"
- config_name: ar-fa
data_files: "data/ar-fa.jsonl"
- config_name: ar-fi
data_files: "data/ar-fi.jsonl"
- config_name: ar-fr
data_files: "data/ar-fr.jsonl"
- config_name: ar-gl
data_files: "data/ar-gl.jsonl"
- config_name: ar-he
data_files: "data/ar-he.jsonl"
- config_name: ar-hi
data_files: "data/ar-hi.jsonl"
- config_name: ar-hr
data_files: "data/ar-hr.jsonl"
- config_name: ar-hu
data_files: "data/ar-hu.jsonl"
- config_name: ar-hy
data_files: "data/ar-hy.jsonl"
- config_name: ar-id
data_files: "data/ar-id.jsonl"
- config_name: ar-is
data_files: "data/ar-is.jsonl"
- config_name: ar-it
data_files: "data/ar-it.jsonl"
- config_name: ar-ja
data_files: "data/ar-ja.jsonl"
- config_name: ar-ka
data_files: "data/ar-ka.jsonl"
- config_name: ar-kk
data_files: "data/ar-kk.jsonl"
- config_name: ar-ko
data_files: "data/ar-ko.jsonl"
- config_name: ar-lt
data_files: "data/ar-lt.jsonl"
- config_name: ar-lv
data_files: "data/ar-lv.jsonl"
- config_name: ar-mk
data_files: "data/ar-mk.jsonl"
- config_name: ar-ml
data_files: "data/ar-ml.jsonl"
- config_name: ar-ms
data_files: "data/ar-ms.jsonl"
- config_name: ar-nl
data_files: "data/ar-nl.jsonl"
- config_name: ar-no
data_files: "data/ar-no.jsonl"
- config_name: ar-pl
data_files: "data/ar-pl.jsonl"
- config_name: ar-pt
data_files: "data/ar-pt.jsonl"
- config_name: ar-ro
data_files: "data/ar-ro.jsonl"
- config_name: ar-ru
data_files: "data/ar-ru.jsonl"
- config_name: ar-si
data_files: "data/ar-si.jsonl"
- config_name: ar-sk
data_files: "data/ar-sk.jsonl"
- config_name: ar-sl
data_files: "data/ar-sl.jsonl"
- config_name: ar-sq
data_files: "data/ar-sq.jsonl"
- config_name: ar-sr
data_files: "data/ar-sr.jsonl"
- config_name: ar-sv
data_files: "data/ar-sv.jsonl"
- config_name: ar-ta
data_files: "data/ar-ta.jsonl"
- config_name: ar-te
data_files: "data/ar-te.jsonl"
- config_name: ar-th
data_files: "data/ar-th.jsonl"
- config_name: ar-tl
data_files: "data/ar-tl.jsonl"
- config_name: ar-tr
data_files: "data/ar-tr.jsonl"
- config_name: ar-uk
data_files: "data/ar-uk.jsonl"
- config_name: ar-ur
data_files: "data/ar-ur.jsonl"
- config_name: ar-vi
data_files: "data/ar-vi.jsonl"
- config_name: ar-pt_br
data_files: "data/ar-pt_br.jsonl"
- config_name: ar-ze_en
data_files: "data/ar-ze_en.jsonl"
- config_name: ar-ze_zh
data_files: "data/ar-ze_zh.jsonl"
- config_name: ar-zh_cn
data_files: "data/ar-zh_cn.jsonl"
- config_name: ar-zh_tw
data_files: "data/ar-zh_tw.jsonl"
- config_name: bg-bn
data_files: "data/bg-bn.jsonl"
- config_name: bg-br
data_files: "data/bg-br.jsonl"
- config_name: bg-bs
data_files: "data/bg-bs.jsonl"
- config_name: bg-ca
data_files: "data/bg-ca.jsonl"
- config_name: bg-cs
data_files: "data/bg-cs.jsonl"
- config_name: bg-da
data_files: "data/bg-da.jsonl"
- config_name: bg-de
data_files: "data/bg-de.jsonl"
- config_name: bg-el
data_files: "data/bg-el.jsonl"
- config_name: bg-en
data_files: "data/bg-en.jsonl"
- config_name: bg-eo
data_files: "data/bg-eo.jsonl"
- config_name: bg-es
data_files: "data/bg-es.jsonl"
- config_name: bg-et
data_files: "data/bg-et.jsonl"
- config_name: bg-eu
data_files: "data/bg-eu.jsonl"
- config_name: bg-fa
data_files: "data/bg-fa.jsonl"
- config_name: bg-fi
data_files: "data/bg-fi.jsonl"
- config_name: bg-fr
data_files: "data/bg-fr.jsonl"
- config_name: bg-gl
data_files: "data/bg-gl.jsonl"
- config_name: bg-he
data_files: "data/bg-he.jsonl"
- config_name: bg-hi
data_files: "data/bg-hi.jsonl"
- config_name: bg-hr
data_files: "data/bg-hr.jsonl"
- config_name: bg-hu
data_files: "data/bg-hu.jsonl"
- config_name: bg-hy
data_files: "data/bg-hy.jsonl"
- config_name: bg-id
data_files: "data/bg-id.jsonl"
- config_name: bg-is
data_files: "data/bg-is.jsonl"
- config_name: bg-it
data_files: "data/bg-it.jsonl"
- config_name: bg-ja
data_files: "data/bg-ja.jsonl"
- config_name: bg-ka
data_files: "data/bg-ka.jsonl"
- config_name: bg-kk
data_files: "data/bg-kk.jsonl"
- config_name: bg-ko
data_files: "data/bg-ko.jsonl"
- config_name: bg-lt
data_files: "data/bg-lt.jsonl"
- config_name: bg-lv
data_files: "data/bg-lv.jsonl"
- config_name: bg-mk
data_files: "data/bg-mk.jsonl"
- config_name: bg-ml
data_files: "data/bg-ml.jsonl"
- config_name: bg-ms
data_files: "data/bg-ms.jsonl"
- config_name: bg-nl
data_files: "data/bg-nl.jsonl"
- config_name: bg-no
data_files: "data/bg-no.jsonl"
- config_name: bg-pl
data_files: "data/bg-pl.jsonl"
- config_name: bg-pt
data_files: "data/bg-pt.jsonl"
- config_name: bg-ro
data_files: "data/bg-ro.jsonl"
- config_name: bg-ru
data_files: "data/bg-ru.jsonl"
- config_name: bg-si
data_files: "data/bg-si.jsonl"
- config_name: bg-sk
data_files: "data/bg-sk.jsonl"
- config_name: bg-sl
data_files: "data/bg-sl.jsonl"
- config_name: bg-sq
data_files: "data/bg-sq.jsonl"
- config_name: bg-sr
data_files: "data/bg-sr.jsonl"
- config_name: bg-sv
data_files: "data/bg-sv.jsonl"
- config_name: bg-ta
data_files: "data/bg-ta.jsonl"
- config_name: bg-te
data_files: "data/bg-te.jsonl"
- config_name: bg-th
data_files: "data/bg-th.jsonl"
- config_name: bg-tl
data_files: "data/bg-tl.jsonl"
- config_name: bg-tr
data_files: "data/bg-tr.jsonl"
- config_name: bg-uk
data_files: "data/bg-uk.jsonl"
- config_name: bg-ur
data_files: "data/bg-ur.jsonl"
- config_name: bg-vi
data_files: "data/bg-vi.jsonl"
- config_name: bg-pt_br
data_files: "data/bg-pt_br.jsonl"
- config_name: bg-ze_en
data_files: "data/bg-ze_en.jsonl"
- config_name: bg-ze_zh
data_files: "data/bg-ze_zh.jsonl"
- config_name: bg-zh_cn
data_files: "data/bg-zh_cn.jsonl"
- config_name: bg-zh_tw
data_files: "data/bg-zh_tw.jsonl"
- config_name: bn-bs
data_files: "data/bn-bs.jsonl"
- config_name: bn-ca
data_files: "data/bn-ca.jsonl"
- config_name: bn-cs
data_files: "data/bn-cs.jsonl"
- config_name: bn-da
data_files: "data/bn-da.jsonl"
- config_name: bn-de
data_files: "data/bn-de.jsonl"
- config_name: bn-el
data_files: "data/bn-el.jsonl"
- config_name: bn-en
data_files: "data/bn-en.jsonl"
- config_name: bn-es
data_files: "data/bn-es.jsonl"
- config_name: bn-et
data_files: "data/bn-et.jsonl"
- config_name: bn-eu
data_files: "data/bn-eu.jsonl"
- config_name: bn-fa
data_files: "data/bn-fa.jsonl"
- config_name: bn-fi
data_files: "data/bn-fi.jsonl"
- config_name: bn-fr
data_files: "data/bn-fr.jsonl"
- config_name: bn-gl
data_files: "data/bn-gl.jsonl"
- config_name: bn-he
data_files: "data/bn-he.jsonl"
- config_name: bn-hi
data_files: "data/bn-hi.jsonl"
- config_name: bn-hr
data_files: "data/bn-hr.jsonl"
- config_name: bn-hu
data_files: "data/bn-hu.jsonl"
- config_name: bn-id
data_files: "data/bn-id.jsonl"
- config_name: bn-is
data_files: "data/bn-is.jsonl"
- config_name: bn-it
data_files: "data/bn-it.jsonl"
- config_name: bn-ja
data_files: "data/bn-ja.jsonl"
- config_name: bn-ka
data_files: "data/bn-ka.jsonl"
- config_name: bn-ko
data_files: "data/bn-ko.jsonl"
- config_name: bn-lt
data_files: "data/bn-lt.jsonl"
- config_name: bn-lv
data_files: "data/bn-lv.jsonl"
- config_name: bn-mk
data_files: "data/bn-mk.jsonl"
- config_name: bn-ml
data_files: "data/bn-ml.jsonl"
- config_name: bn-ms
data_files: "data/bn-ms.jsonl"
- config_name: bn-nl
data_files: "data/bn-nl.jsonl"
- config_name: bn-no
data_files: "data/bn-no.jsonl"
- config_name: bn-pl
data_files: "data/bn-pl.jsonl"
- config_name: bn-pt
data_files: "data/bn-pt.jsonl"
- config_name: bn-ro
data_files: "data/bn-ro.jsonl"
- config_name: bn-ru
data_files: "data/bn-ru.jsonl"
- config_name: bn-si
data_files: "data/bn-si.jsonl"
- config_name: bn-sk
data_files: "data/bn-sk.jsonl"
- config_name: bn-sl
data_files: "data/bn-sl.jsonl"
- config_name: bn-sq
data_files: "data/bn-sq.jsonl"
- config_name: bn-sr
data_files: "data/bn-sr.jsonl"
- config_name: bn-sv
data_files: "data/bn-sv.jsonl"
- config_name: bn-ta
data_files: "data/bn-ta.jsonl"
- config_name: bn-th
data_files: "data/bn-th.jsonl"
- config_name: bn-tl
data_files: "data/bn-tl.jsonl"
- config_name: bn-tr
data_files: "data/bn-tr.jsonl"
- config_name: bn-uk
data_files: "data/bn-uk.jsonl"
- config_name: bn-ur
data_files: "data/bn-ur.jsonl"
- config_name: bn-vi
data_files: "data/bn-vi.jsonl"
- config_name: bn-pt_br
data_files: "data/bn-pt_br.jsonl"
- config_name: bn-ze_en
data_files: "data/bn-ze_en.jsonl"
- config_name: bn-ze_zh
data_files: "data/bn-ze_zh.jsonl"
- config_name: bn-zh_cn
data_files: "data/bn-zh_cn.jsonl"
- config_name: bn-zh_tw
data_files: "data/bn-zh_tw.jsonl"
- config_name: br-bs
data_files: "data/br-bs.jsonl"
- config_name: br-ca
data_files: "data/br-ca.jsonl"
- config_name: br-cs
data_files: "data/br-cs.jsonl"
- config_name: br-da
data_files: "data/br-da.jsonl"
- config_name: br-de
data_files: "data/br-de.jsonl"
- config_name: br-el
data_files: "data/br-el.jsonl"
- config_name: br-en
data_files: "data/br-en.jsonl"
- config_name: br-eo
data_files: "data/br-eo.jsonl"
- config_name: br-es
data_files: "data/br-es.jsonl"
- config_name: br-et
data_files: "data/br-et.jsonl"
- config_name: br-eu
data_files: "data/br-eu.jsonl"
- config_name: br-fa
data_files: "data/br-fa.jsonl"
- config_name: br-fi
data_files: "data/br-fi.jsonl"
- config_name: br-fr
data_files: "data/br-fr.jsonl"
- config_name: br-gl
data_files: "data/br-gl.jsonl"
- config_name: br-he
data_files: "data/br-he.jsonl"
- config_name: br-hr
data_files: "data/br-hr.jsonl"
- config_name: br-hu
data_files: "data/br-hu.jsonl"
- config_name: br-id
data_files: "data/br-id.jsonl"
- config_name: br-is
data_files: "data/br-is.jsonl"
- config_name: br-it
data_files: "data/br-it.jsonl"
- config_name: br-mk
data_files: "data/br-mk.jsonl"
- config_name: br-ml
data_files: "data/br-ml.jsonl"
- config_name: br-nl
data_files: "data/br-nl.jsonl"
- config_name: br-no
data_files: "data/br-no.jsonl"
- config_name: br-pl
data_files: "data/br-pl.jsonl"
- config_name: br-pt
data_files: "data/br-pt.jsonl"
- config_name: br-ro
data_files: "data/br-ro.jsonl"
- config_name: br-ru
data_files: "data/br-ru.jsonl"
- config_name: br-sk
data_files: "data/br-sk.jsonl"
- config_name: br-sl
data_files: "data/br-sl.jsonl"
- config_name: br-sq
data_files: "data/br-sq.jsonl"
- config_name: br-sr
data_files: "data/br-sr.jsonl"
- config_name: br-sv
data_files: "data/br-sv.jsonl"
- config_name: br-tr
data_files: "data/br-tr.jsonl"
- config_name: br-uk
data_files: "data/br-uk.jsonl"
- config_name: br-pt_br
data_files: "data/br-pt_br.jsonl"
- config_name: br-zh_cn
data_files: "data/br-zh_cn.jsonl"
- config_name: bs-ca
data_files: "data/bs-ca.jsonl"
- config_name: bs-cs
data_files: "data/bs-cs.jsonl"
- config_name: bs-da
data_files: "data/bs-da.jsonl"
- config_name: bs-de
data_files: "data/bs-de.jsonl"
- config_name: bs-el
data_files: "data/bs-el.jsonl"
- config_name: bs-en
data_files: "data/bs-en.jsonl"
- config_name: bs-eo
data_files: "data/bs-eo.jsonl"
- config_name: bs-es
data_files: "data/bs-es.jsonl"
- config_name: bs-et
data_files: "data/bs-et.jsonl"
- config_name: bs-eu
data_files: "data/bs-eu.jsonl"
- config_name: bs-fa
data_files: "data/bs-fa.jsonl"
- config_name: bs-fi
data_files: "data/bs-fi.jsonl"
- config_name: bs-fr
data_files: "data/bs-fr.jsonl"
- config_name: bs-gl
data_files: "data/bs-gl.jsonl"
- config_name: bs-he
data_files: "data/bs-he.jsonl"
- config_name: bs-hi
data_files: "data/bs-hi.jsonl"
- config_name: bs-hr
data_files: "data/bs-hr.jsonl"
- config_name: bs-hu
data_files: "data/bs-hu.jsonl"
- config_name: bs-hy
data_files: "data/bs-hy.jsonl"
- config_name: bs-id
data_files: "data/bs-id.jsonl"
- config_name: bs-is
data_files: "data/bs-is.jsonl"
- config_name: bs-it
data_files: "data/bs-it.jsonl"
- config_name: bs-ja
data_files: "data/bs-ja.jsonl"
- config_name: bs-ka
data_files: "data/bs-ka.jsonl"
- config_name: bs-kk
data_files: "data/bs-kk.jsonl"
- config_name: bs-ko
data_files: "data/bs-ko.jsonl"
- config_name: bs-lt
data_files: "data/bs-lt.jsonl"
- config_name: bs-lv
data_files: "data/bs-lv.jsonl"
- config_name: bs-mk
data_files: "data/bs-mk.jsonl"
- config_name: bs-ml
data_files: "data/bs-ml.jsonl"
- config_name: bs-ms
data_files: "data/bs-ms.jsonl"
- config_name: bs-nl
data_files: "data/bs-nl.jsonl"
- config_name: bs-no
data_files: "data/bs-no.jsonl"
- config_name: bs-pl
data_files: "data/bs-pl.jsonl"
- config_name: bs-pt
data_files: "data/bs-pt.jsonl"
- config_name: bs-ro
data_files: "data/bs-ro.jsonl"
- config_name: bs-ru
data_files: "data/bs-ru.jsonl"
- config_name: bs-si
data_files: "data/bs-si.jsonl"
- config_name: bs-sk
data_files: "data/bs-sk.jsonl"
- config_name: bs-sl
data_files: "data/bs-sl.jsonl"
- config_name: bs-sq
data_files: "data/bs-sq.jsonl"
- config_name: bs-sr
data_files: "data/bs-sr.jsonl"
- config_name: bs-sv
data_files: "data/bs-sv.jsonl"
- config_name: bs-ta
data_files: "data/bs-ta.jsonl"
- config_name: bs-te
data_files: "data/bs-te.jsonl"
- config_name: bs-th
data_files: "data/bs-th.jsonl"
- config_name: bs-tl
data_files: "data/bs-tl.jsonl"
- config_name: bs-tr
data_files: "data/bs-tr.jsonl"
- config_name: bs-uk
data_files: "data/bs-uk.jsonl"
- config_name: bs-ur
data_files: "data/bs-ur.jsonl"
- config_name: bs-vi
data_files: "data/bs-vi.jsonl"
- config_name: bs-pt_br
data_files: "data/bs-pt_br.jsonl"
- config_name: bs-ze_en
data_files: "data/bs-ze_en.jsonl"
- config_name: bs-ze_zh
data_files: "data/bs-ze_zh.jsonl"
- config_name: bs-zh_cn
data_files: "data/bs-zh_cn.jsonl"
- config_name: bs-zh_tw
data_files: "data/bs-zh_tw.jsonl"
- config_name: ca-cs
data_files: "data/ca-cs.jsonl"
- config_name: ca-da
data_files: "data/ca-da.jsonl"
- config_name: ca-de
data_files: "data/ca-de.jsonl"
- config_name: ca-el
data_files: "data/ca-el.jsonl"
- config_name: ca-en
data_files: "data/ca-en.jsonl"
- config_name: ca-es
data_files: "data/ca-es.jsonl"
- config_name: ca-et
data_files: "data/ca-et.jsonl"
- config_name: ca-eu
data_files: "data/ca-eu.jsonl"
- config_name: ca-fa
data_files: "data/ca-fa.jsonl"
- config_name: ca-fi
data_files: "data/ca-fi.jsonl"
- config_name: ca-fr
data_files: "data/ca-fr.jsonl"
- config_name: ca-gl
data_files: "data/ca-gl.jsonl"
- config_name: ca-he
data_files: "data/ca-he.jsonl"
- config_name: ca-hi
data_files: "data/ca-hi.jsonl"
- config_name: ca-hr
data_files: "data/ca-hr.jsonl"
- config_name: ca-hu
data_files: "data/ca-hu.jsonl"
- config_name: ca-id
data_files: "data/ca-id.jsonl"
- config_name: ca-is
data_files: "data/ca-is.jsonl"
- config_name: ca-it
data_files: "data/ca-it.jsonl"
- config_name: ca-ja
data_files: "data/ca-ja.jsonl"
- config_name: ca-ka
data_files: "data/ca-ka.jsonl"
- config_name: ca-ko
data_files: "data/ca-ko.jsonl"
- config_name: ca-lt
data_files: "data/ca-lt.jsonl"
- config_name: ca-lv
data_files: "data/ca-lv.jsonl"
- config_name: ca-mk
data_files: "data/ca-mk.jsonl"
- config_name: ca-ml
data_files: "data/ca-ml.jsonl"
- config_name: ca-ms
data_files: "data/ca-ms.jsonl"
- config_name: ca-nl
data_files: "data/ca-nl.jsonl"
- config_name: ca-no
data_files: "data/ca-no.jsonl"
- config_name: ca-pl
data_files: "data/ca-pl.jsonl"
- config_name: ca-pt
data_files: "data/ca-pt.jsonl"
- config_name: ca-ro
data_files: "data/ca-ro.jsonl"
- config_name: ca-ru
data_files: "data/ca-ru.jsonl"
- config_name: ca-si
data_files: "data/ca-si.jsonl"
- config_name: ca-sk
data_files: "data/ca-sk.jsonl"
- config_name: ca-sl
data_files: "data/ca-sl.jsonl"
- config_name: ca-sq
data_files: "data/ca-sq.jsonl"
- config_name: ca-sr
data_files: "data/ca-sr.jsonl"
- config_name: ca-sv
data_files: "data/ca-sv.jsonl"
- config_name: ca-th
data_files: "data/ca-th.jsonl"
- config_name: ca-tr
data_files: "data/ca-tr.jsonl"
- config_name: ca-uk
data_files: "data/ca-uk.jsonl"
- config_name: ca-vi
data_files: "data/ca-vi.jsonl"
- config_name: ca-pt_br
data_files: "data/ca-pt_br.jsonl"
- config_name: ca-ze_en
data_files: "data/ca-ze_en.jsonl"
- config_name: ca-ze_zh
data_files: "data/ca-ze_zh.jsonl"
- config_name: ca-zh_cn
data_files: "data/ca-zh_cn.jsonl"
- config_name: ca-zh_tw
data_files: "data/ca-zh_tw.jsonl"
- config_name: cs-da
data_files: "data/cs-da.jsonl"
- config_name: cs-de
data_files: "data/cs-de.jsonl"
- config_name: cs-el
data_files: "data/cs-el.jsonl"
- config_name: cs-en
data_files: "data/cs-en.jsonl"
- config_name: cs-eo
data_files: "data/cs-eo.jsonl"
- config_name: cs-es
data_files: "data/cs-es.jsonl"
- config_name: cs-et
data_files: "data/cs-et.jsonl"
- config_name: cs-eu
data_files: "data/cs-eu.jsonl"
- config_name: cs-fa
data_files: "data/cs-fa.jsonl"
- config_name: cs-fi
data_files: "data/cs-fi.jsonl"
- config_name: cs-fr
data_files: "data/cs-fr.jsonl"
- config_name: cs-gl
data_files: "data/cs-gl.jsonl"
- config_name: cs-he
data_files: "data/cs-he.jsonl"
- config_name: cs-hi
data_files: "data/cs-hi.jsonl"
- config_name: cs-hr
data_files: "data/cs-hr.jsonl"
- config_name: cs-hu
data_files: "data/cs-hu.jsonl"
- config_name: cs-hy
data_files: "data/cs-hy.jsonl"
- config_name: cs-id
data_files: "data/cs-id.jsonl"
- config_name: cs-is
data_files: "data/cs-is.jsonl"
- config_name: cs-it
data_files: "data/cs-it.jsonl"
- config_name: cs-ja
data_files: "data/cs-ja.jsonl"
- config_name: cs-ka
data_files: "data/cs-ka.jsonl"
- config_name: cs-kk
data_files: "data/cs-kk.jsonl"
- config_name: cs-ko
data_files: "data/cs-ko.jsonl"
- config_name: cs-lt
data_files: "data/cs-lt.jsonl"
- config_name: cs-lv
data_files: "data/cs-lv.jsonl"
- config_name: cs-mk
data_files: "data/cs-mk.jsonl"
- config_name: cs-ml
data_files: "data/cs-ml.jsonl"
- config_name: cs-ms
data_files: "data/cs-ms.jsonl"
- config_name: cs-nl
data_files: "data/cs-nl.jsonl"
- config_name: cs-no
data_files: "data/cs-no.jsonl"
- config_name: cs-pl
data_files: "data/cs-pl.jsonl"
- config_name: cs-pt
data_files: "data/cs-pt.jsonl"
- config_name: cs-ro
data_files: "data/cs-ro.jsonl"
- config_name: cs-ru
data_files: "data/cs-ru.jsonl"
- config_name: cs-si
data_files: "data/cs-si.jsonl"
- config_name: cs-sk
data_files: "data/cs-sk.jsonl"
- config_name: cs-sl
data_files: "data/cs-sl.jsonl"
- config_name: cs-sq
data_files: "data/cs-sq.jsonl"
- config_name: cs-sr
data_files: "data/cs-sr.jsonl"
- config_name: cs-sv
data_files: "data/cs-sv.jsonl"
- config_name: cs-ta
data_files: "data/cs-ta.jsonl"
- config_name: cs-te
data_files: "data/cs-te.jsonl"
- config_name: cs-th
data_files: "data/cs-th.jsonl"
- config_name: cs-tl
data_files: "data/cs-tl.jsonl"
- config_name: cs-tr
data_files: "data/cs-tr.jsonl"
- config_name: cs-uk
data_files: "data/cs-uk.jsonl"
- config_name: cs-ur
data_files: "data/cs-ur.jsonl"
- config_name: cs-vi
data_files: "data/cs-vi.jsonl"
- config_name: cs-pt_br
data_files: "data/cs-pt_br.jsonl"
- config_name: cs-ze_en
data_files: "data/cs-ze_en.jsonl"
- config_name: cs-ze_zh
data_files: "data/cs-ze_zh.jsonl"
- config_name: cs-zh_cn
data_files: "data/cs-zh_cn.jsonl"
- config_name: cs-zh_tw
data_files: "data/cs-zh_tw.jsonl"
- config_name: da-de
data_files: "data/da-de.jsonl"
- config_name: da-el
data_files: "data/da-el.jsonl"
- config_name: da-en
data_files: "data/da-en.jsonl"
- config_name: da-eo
data_files: "data/da-eo.jsonl"
- config_name: da-es
data_files: "data/da-es.jsonl"
- config_name: da-et
data_files: "data/da-et.jsonl"
- config_name: da-eu
data_files: "data/da-eu.jsonl"
- config_name: da-fa
data_files: "data/da-fa.jsonl"
- config_name: da-fi
data_files: "data/da-fi.jsonl"
- config_name: da-fr
data_files: "data/da-fr.jsonl"
- config_name: da-gl
data_files: "data/da-gl.jsonl"
- config_name: da-he
data_files: "data/da-he.jsonl"
- config_name: da-hi
data_files: "data/da-hi.jsonl"
- config_name: da-hr
data_files: "data/da-hr.jsonl"
- config_name: da-hu
data_files: "data/da-hu.jsonl"
- config_name: da-id
data_files: "data/da-id.jsonl"
- config_name: da-is
data_files: "data/da-is.jsonl"
- config_name: da-it
data_files: "data/da-it.jsonl"
- config_name: da-ja
data_files: "data/da-ja.jsonl"
- config_name: da-ka
data_files: "data/da-ka.jsonl"
- config_name: da-kk
data_files: "data/da-kk.jsonl"
- config_name: da-ko
data_files: "data/da-ko.jsonl"
- config_name: da-lt
data_files: "data/da-lt.jsonl"
- config_name: da-lv
data_files: "data/da-lv.jsonl"
- config_name: da-mk
data_files: "data/da-mk.jsonl"
- config_name: da-ml
data_files: "data/da-ml.jsonl"
- config_name: da-ms
data_files: "data/da-ms.jsonl"
- config_name: da-nl
data_files: "data/da-nl.jsonl"
- config_name: da-no
data_files: "data/da-no.jsonl"
- config_name: da-pl
data_files: "data/da-pl.jsonl"
- config_name: da-pt
data_files: "data/da-pt.jsonl"
- config_name: da-ro
data_files: "data/da-ro.jsonl"
- config_name: da-ru
data_files: "data/da-ru.jsonl"
- config_name: da-si
data_files: "data/da-si.jsonl"
- config_name: da-sk
data_files: "data/da-sk.jsonl"
- config_name: da-sl
data_files: "data/da-sl.jsonl"
- config_name: da-sq
data_files: "data/da-sq.jsonl"
- config_name: da-sr
data_files: "data/da-sr.jsonl"
- config_name: da-sv
data_files: "data/da-sv.jsonl"
- config_name: da-ta
data_files: "data/da-ta.jsonl"
- config_name: da-te
data_files: "data/da-te.jsonl"
- config_name: da-th
data_files: "data/da-th.jsonl"
- config_name: da-tl
data_files: "data/da-tl.jsonl"
- config_name: da-tr
data_files: "data/da-tr.jsonl"
- config_name: da-uk
data_files: "data/da-uk.jsonl"
- config_name: da-ur
data_files: "data/da-ur.jsonl"
- config_name: da-vi
data_files: "data/da-vi.jsonl"
- config_name: da-pt_br
data_files: "data/da-pt_br.jsonl"
- config_name: da-ze_en
data_files: "data/da-ze_en.jsonl"
- config_name: da-ze_zh
data_files: "data/da-ze_zh.jsonl"
- config_name: da-zh_cn
data_files: "data/da-zh_cn.jsonl"
- config_name: da-zh_tw
data_files: "data/da-zh_tw.jsonl"
- config_name: de-el
data_files: "data/de-el.jsonl"
- config_name: de-en
data_files: "data/de-en.jsonl"
- config_name: de-eo
data_files: "data/de-eo.jsonl"
- config_name: de-es
data_files: "data/de-es.jsonl"
- config_name: de-et
data_files: "data/de-et.jsonl"
- config_name: de-eu
data_files: "data/de-eu.jsonl"
- config_name: de-fa
data_files: "data/de-fa.jsonl"
- config_name: de-fi
data_files: "data/de-fi.jsonl"
- config_name: de-fr
data_files: "data/de-fr.jsonl"
- config_name: de-gl
data_files: "data/de-gl.jsonl"
- config_name: de-he
data_files: "data/de-he.jsonl"
- config_name: de-hi
data_files: "data/de-hi.jsonl"
- config_name: de-hr
data_files: "data/de-hr.jsonl"
- config_name: de-hu
data_files: "data/de-hu.jsonl"
- config_name: de-hy
data_files: "data/de-hy.jsonl"
- config_name: de-id
data_files: "data/de-id.jsonl"
- config_name: de-is
data_files: "data/de-is.jsonl"
- config_name: de-it
data_files: "data/de-it.jsonl"
- config_name: de-ja
data_files: "data/de-ja.jsonl"
- config_name: de-ka
data_files: "data/de-ka.jsonl"
- config_name: de-kk
data_files: "data/de-kk.jsonl"
- config_name: de-ko
data_files: "data/de-ko.jsonl"
- config_name: de-lt
data_files: "data/de-lt.jsonl"
- config_name: de-lv
data_files: "data/de-lv.jsonl"
- config_name: de-mk
data_files: "data/de-mk.jsonl"
- config_name: de-ml
data_files: "data/de-ml.jsonl"
- config_name: de-ms
data_files: "data/de-ms.jsonl"
- config_name: de-nl
data_files: "data/de-nl.jsonl"
- config_name: de-no
data_files: "data/de-no.jsonl"
- config_name: de-pl
data_files: "data/de-pl.jsonl"
- config_name: de-pt
data_files: "data/de-pt.jsonl"
- config_name: de-ro
data_files: "data/de-ro.jsonl"
- config_name: de-ru
data_files: "data/de-ru.jsonl"
- config_name: de-si
data_files: "data/de-si.jsonl"
- config_name: de-sk
data_files: "data/de-sk.jsonl"
- config_name: de-sl
data_files: "data/de-sl.jsonl"
- config_name: de-sq
data_files: "data/de-sq.jsonl"
- config_name: de-sr
data_files: "data/de-sr.jsonl"
- config_name: de-sv
data_files: "data/de-sv.jsonl"
- config_name: de-ta
data_files: "data/de-ta.jsonl"
- config_name: de-te
data_files: "data/de-te.jsonl"
- config_name: de-th
data_files: "data/de-th.jsonl"
- config_name: de-tl
data_files: "data/de-tl.jsonl"
- config_name: de-tr
data_files: "data/de-tr.jsonl"
- config_name: de-uk
data_files: "data/de-uk.jsonl"
- config_name: de-ur
data_files: "data/de-ur.jsonl"
- config_name: de-vi
data_files: "data/de-vi.jsonl"
- config_name: de-pt_br
data_files: "data/de-pt_br.jsonl"
- config_name: de-ze_en
data_files: "data/de-ze_en.jsonl"
- config_name: de-ze_zh
data_files: "data/de-ze_zh.jsonl"
- config_name: de-zh_cn
data_files: "data/de-zh_cn.jsonl"
- config_name: de-zh_tw
data_files: "data/de-zh_tw.jsonl"
- config_name: el-en
data_files: "data/el-en.jsonl"
- config_name: el-eo
data_files: "data/el-eo.jsonl"
- config_name: el-es
data_files: "data/el-es.jsonl"
- config_name: el-et
data_files: "data/el-et.jsonl"
- config_name: el-eu
data_files: "data/el-eu.jsonl"
- config_name: el-fa
data_files: "data/el-fa.jsonl"
- config_name: el-fi
data_files: "data/el-fi.jsonl"
- config_name: el-fr
data_files: "data/el-fr.jsonl"
- config_name: el-gl
data_files: "data/el-gl.jsonl"
- config_name: el-he
data_files: "data/el-he.jsonl"
- config_name: el-hi
data_files: "data/el-hi.jsonl"
- config_name: el-hr
data_files: "data/el-hr.jsonl"
- config_name: el-hu
data_files: "data/el-hu.jsonl"
- config_name: el-hy
data_files: "data/el-hy.jsonl"
- config_name: el-id
data_files: "data/el-id.jsonl"
- config_name: el-is
data_files: "data/el-is.jsonl"
- config_name: el-it
data_files: "data/el-it.jsonl"
- config_name: el-ja
data_files: "data/el-ja.jsonl"
- config_name: el-ka
data_files: "data/el-ka.jsonl"
- config_name: el-kk
data_files: "data/el-kk.jsonl"
- config_name: el-ko
data_files: "data/el-ko.jsonl"
- config_name: el-lt
data_files: "data/el-lt.jsonl"
- config_name: el-lv
data_files: "data/el-lv.jsonl"
- config_name: el-mk
data_files: "data/el-mk.jsonl"
- config_name: el-ml
data_files: "data/el-ml.jsonl"
- config_name: el-ms
data_files: "data/el-ms.jsonl"
- config_name: el-nl
data_files: "data/el-nl.jsonl"
- config_name: el-no
data_files: "data/el-no.jsonl"
- config_name: el-pl
data_files: "data/el-pl.jsonl"
- config_name: el-pt
data_files: "data/el-pt.jsonl"
- config_name: el-ro
data_files: "data/el-ro.jsonl"
- config_name: el-ru
data_files: "data/el-ru.jsonl"
- config_name: el-si
data_files: "data/el-si.jsonl"
- config_name: el-sk
data_files: "data/el-sk.jsonl"
- config_name: el-sl
data_files: "data/el-sl.jsonl"
- config_name: el-sq
data_files: "data/el-sq.jsonl"
- config_name: el-sr
data_files: "data/el-sr.jsonl"
- config_name: el-sv
data_files: "data/el-sv.jsonl"
- config_name: el-ta
data_files: "data/el-ta.jsonl"
- config_name: el-te
data_files: "data/el-te.jsonl"
- config_name: el-th
data_files: "data/el-th.jsonl"
- config_name: el-tl
data_files: "data/el-tl.jsonl"
- config_name: el-tr
data_files: "data/el-tr.jsonl"
- config_name: el-uk
data_files: "data/el-uk.jsonl"
- config_name: el-ur
data_files: "data/el-ur.jsonl"
- config_name: el-vi
data_files: "data/el-vi.jsonl"
- config_name: el-pt_br
data_files: "data/el-pt_br.jsonl"
- config_name: el-ze_en
data_files: "data/el-ze_en.jsonl"
- config_name: el-ze_zh
data_files: "data/el-ze_zh.jsonl"
- config_name: el-zh_cn
data_files: "data/el-zh_cn.jsonl"
- config_name: el-zh_tw
data_files: "data/el-zh_tw.jsonl"
- config_name: en-eo
data_files: "data/en-eo.jsonl"
- config_name: en-es
data_files: "data/en-es.jsonl"
- config_name: en-et
data_files: "data/en-et.jsonl"
- config_name: en-eu
data_files: "data/en-eu.jsonl"
- config_name: en-fa
data_files: "data/en-fa.jsonl"
- config_name: en-fi
data_files: "data/en-fi.jsonl"
- config_name: en-fr
data_files: "data/en-fr.jsonl"
- config_name: en-gl
data_files: "data/en-gl.jsonl"
- config_name: en-he
data_files: "data/en-he.jsonl"
- config_name: en-hi
data_files: "data/en-hi.jsonl"
- config_name: en-hr
data_files: "data/en-hr.jsonl"
- config_name: en-hu
data_files: "data/en-hu.jsonl"
- config_name: en-hy
data_files: "data/en-hy.jsonl"
- config_name: en-id
data_files: "data/en-id.jsonl"
- config_name: en-is
data_files: "data/en-is.jsonl"
- config_name: en-it
data_files: "data/en-it.jsonl"
- config_name: en-ja
data_files: "data/en-ja.jsonl"
- config_name: en-ka
data_files: "data/en-ka.jsonl"
- config_name: en-kk
data_files: "data/en-kk.jsonl"
- config_name: en-ko
data_files: "data/en-ko.jsonl"
- config_name: en-lt
data_files: "data/en-lt.jsonl"
- config_name: en-lv
data_files: "data/en-lv.jsonl"
- config_name: en-mk
data_files: "data/en-mk.jsonl"
- config_name: en-ml
data_files: "data/en-ml.jsonl"
- config_name: en-ms
data_files: "data/en-ms.jsonl"
- config_name: en-nl
data_files: "data/en-nl.jsonl"
- config_name: en-no
data_files: "data/en-no.jsonl"
- config_name: eo-es
data_files: "data/eo-es.jsonl"
- config_name: eo-et
data_files: "data/eo-et.jsonl"
- config_name: eo-eu
data_files: "data/eo-eu.jsonl"
- config_name: eo-fa
data_files: "data/eo-fa.jsonl"
- config_name: eo-fi
data_files: "data/eo-fi.jsonl"
- config_name: eo-fr
data_files: "data/eo-fr.jsonl"
- config_name: eo-gl
data_files: "data/eo-gl.jsonl"
- config_name: eo-he
data_files: "data/eo-he.jsonl"
- config_name: eo-hi
data_files: "data/eo-hi.jsonl"
- config_name: eo-hr
data_files: "data/eo-hr.jsonl"
- config_name: eo-hu
data_files: "data/eo-hu.jsonl"
- config_name: eo-hy
data_files: "data/eo-hy.jsonl"
- config_name: eo-id
data_files: "data/eo-id.jsonl"
- config_name: eo-is
data_files: "data/eo-is.jsonl"
- config_name: eo-it
data_files: "data/eo-it.jsonl"
- config_name: eo-ja
data_files: "data/eo-ja.jsonl"
- config_name: eo-kk
data_files: "data/eo-kk.jsonl"
- config_name: eo-ko
data_files: "data/eo-ko.jsonl"
- config_name: eo-lt
data_files: "data/eo-lt.jsonl"
- config_name: eo-lv
data_files: "data/eo-lv.jsonl"
- config_name: eo-mk
data_files: "data/eo-mk.jsonl"
- config_name: eo-ml
data_files: "data/eo-ml.jsonl"
- config_name: eo-ms
data_files: "data/eo-ms.jsonl"
- config_name: eo-nl
data_files: "data/eo-nl.jsonl"
- config_name: eo-no
data_files: "data/eo-no.jsonl"
- config_name: eo-pl
data_files: "data/eo-pl.jsonl"
- config_name: eo-pt
data_files: "data/eo-pt.jsonl"
- config_name: eo-ro
data_files: "data/eo-ro.jsonl"
- config_name: eo-ru
data_files: "data/eo-ru.jsonl"
- config_name: eo-si
data_files: "data/eo-si.jsonl"
- config_name: eo-sk
data_files: "data/eo-sk.jsonl"
- config_name: eo-sl
data_files: "data/eo-sl.jsonl"
- config_name: eo-sq
data_files: "data/eo-sq.jsonl"
- config_name: eo-sr
data_files: "data/eo-sr.jsonl"
- config_name: eo-sv
data_files: "data/eo-sv.jsonl"
- config_name: eo-th
data_files: "data/eo-th.jsonl"
- config_name: eo-tl
data_files: "data/eo-tl.jsonl"
- config_name: eo-tr
data_files: "data/eo-tr.jsonl"
- config_name: eo-uk
data_files: "data/eo-uk.jsonl"
- config_name: eo-vi
data_files: "data/eo-vi.jsonl"
- config_name: eo-pt_br
data_files: "data/eo-pt_br.jsonl"
- config_name: eo-ze_en
data_files: "data/eo-ze_en.jsonl"
- config_name: eo-ze_zh
data_files: "data/eo-ze_zh.jsonl"
- config_name: eo-zh_cn
data_files: "data/eo-zh_cn.jsonl"
- config_name: eo-zh_tw
data_files: "data/eo-zh_tw.jsonl"
- config_name: es-et
data_files: "data/es-et.jsonl"
- config_name: es-eu
data_files: "data/es-eu.jsonl"
- config_name: es-fa
data_files: "data/es-fa.jsonl"
- config_name: es-fi
data_files: "data/es-fi.jsonl"
- config_name: es-fr
data_files: "data/es-fr.jsonl"
- config_name: es-gl
data_files: "data/es-gl.jsonl"
- config_name: es-he
data_files: "data/es-he.jsonl"
- config_name: es-hi
data_files: "data/es-hi.jsonl"
- config_name: es-hr
data_files: "data/es-hr.jsonl"
- config_name: es-hu
data_files: "data/es-hu.jsonl"
- config_name: es-hy
data_files: "data/es-hy.jsonl"
- config_name: es-id
data_files: "data/es-id.jsonl"
- config_name: es-is
data_files: "data/es-is.jsonl"
- config_name: es-it
data_files: "data/es-it.jsonl"
- config_name: es-ja
data_files: "data/es-ja.jsonl"
- config_name: es-ka
data_files: "data/es-ka.jsonl"
- config_name: es-kk
data_files: "data/es-kk.jsonl"
- config_name: es-ko
data_files: "data/es-ko.jsonl"
- config_name: es-lt
data_files: "data/es-lt.jsonl"
- config_name: es-lv
data_files: "data/es-lv.jsonl"
- config_name: es-mk
data_files: "data/es-mk.jsonl"
- config_name: es-ml
data_files: "data/es-ml.jsonl"
- config_name: es-ms
data_files: "data/es-ms.jsonl"
- config_name: es-nl
data_files: "data/es-nl.jsonl"
- config_name: es-no
data_files: "data/es-no.jsonl"
- config_name: es-pl
data_files: "data/es-pl.jsonl"
- config_name: es-pt
data_files: "data/es-pt.jsonl"
- config_name: es-ro
data_files: "data/es-ro.jsonl"
- config_name: es-ru
data_files: "data/es-ru.jsonl"
- config_name: es-si
data_files: "data/es-si.jsonl"
- config_name: es-sk
data_files: "data/es-sk.jsonl"
- config_name: es-sl
data_files: "data/es-sl.jsonl"
- config_name: es-sq
data_files: "data/es-sq.jsonl"
- config_name: es-sr
data_files: "data/es-sr.jsonl"
- config_name: es-sv
data_files: "data/es-sv.jsonl"
- config_name: es-ta
data_files: "data/es-ta.jsonl"
- config_name: es-te
data_files: "data/es-te.jsonl"
- config_name: es-th
data_files: "data/es-th.jsonl"
- config_name: es-tl
data_files: "data/es-tl.jsonl"
- config_name: es-tr
data_files: "data/es-tr.jsonl"
- config_name: es-uk
data_files: "data/es-uk.jsonl"
- config_name: es-ur
data_files: "data/es-ur.jsonl"
- config_name: es-vi
data_files: "data/es-vi.jsonl"
- config_name: es-pt_br
data_files: "data/es-pt_br.jsonl"
- config_name: es-ze_en
data_files: "data/es-ze_en.jsonl"
- config_name: es-ze_zh
data_files: "data/es-ze_zh.jsonl"
- config_name: es-zh_cn
data_files: "data/es-zh_cn.jsonl"
- config_name: es-zh_tw
data_files: "data/es-zh_tw.jsonl"
- config_name: et-eu
data_files: "data/et-eu.jsonl"
- config_name: et-fa
data_files: "data/et-fa.jsonl"
- config_name: et-fi
data_files: "data/et-fi.jsonl"
- config_name: et-fr
data_files: "data/et-fr.jsonl"
- config_name: et-gl
data_files: "data/et-gl.jsonl"
- config_name: et-he
data_files: "data/et-he.jsonl"
- config_name: et-hi
data_files: "data/et-hi.jsonl"
- config_name: et-hr
data_files: "data/et-hr.jsonl"
- config_name: et-hu
data_files: "data/et-hu.jsonl"
- config_name: et-hy
data_files: "data/et-hy.jsonl"
- config_name: et-id
data_files: "data/et-id.jsonl"
- config_name: et-is
data_files: "data/et-is.jsonl"
- config_name: et-it
data_files: "data/et-it.jsonl"
- config_name: et-ja
data_files: "data/et-ja.jsonl"
- config_name: et-ka
data_files: "data/et-ka.jsonl"
- config_name: et-kk
data_files: "data/et-kk.jsonl"
- config_name: et-ko
data_files: "data/et-ko.jsonl"
- config_name: et-lt
data_files: "data/et-lt.jsonl"
- config_name: et-lv
data_files: "data/et-lv.jsonl"
- config_name: et-mk
data_files: "data/et-mk.jsonl"
- config_name: et-ml
data_files: "data/et-ml.jsonl"
- config_name: et-ms
data_files: "data/et-ms.jsonl"
- config_name: et-nl
data_files: "data/et-nl.jsonl"
- config_name: et-no
data_files: "data/et-no.jsonl"
- config_name: et-pl
data_files: "data/et-pl.jsonl"
- config_name: et-pt
data_files: "data/et-pt.jsonl"
- config_name: et-ro
data_files: "data/et-ro.jsonl"
- config_name: et-ru
data_files: "data/et-ru.jsonl"
- config_name: et-si
data_files: "data/et-si.jsonl"
- config_name: et-sk
data_files: "data/et-sk.jsonl"
- config_name: et-sl
data_files: "data/et-sl.jsonl"
- config_name: et-sq
data_files: "data/et-sq.jsonl"
- config_name: et-sr
data_files: "data/et-sr.jsonl"
- config_name: et-sv
data_files: "data/et-sv.jsonl"
- config_name: et-ta
data_files: "data/et-ta.jsonl"
- config_name: et-te
data_files: "data/et-te.jsonl"
- config_name: et-th
data_files: "data/et-th.jsonl"
- config_name: et-tl
data_files: "data/et-tl.jsonl"
- config_name: et-tr
data_files: "data/et-tr.jsonl"
- config_name: et-uk
data_files: "data/et-uk.jsonl"
- config_name: et-ur
data_files: "data/et-ur.jsonl"
- config_name: et-vi
data_files: "data/et-vi.jsonl"
- config_name: et-pt_br
data_files: "data/et-pt_br.jsonl"
- config_name: et-ze_en
data_files: "data/et-ze_en.jsonl"
- config_name: et-ze_zh
data_files: "data/et-ze_zh.jsonl"
- config_name: et-zh_cn
data_files: "data/et-zh_cn.jsonl"
- config_name: et-zh_tw
data_files: "data/et-zh_tw.jsonl"
- config_name: eu-fa
data_files: "data/eu-fa.jsonl"
- config_name: eu-fi
data_files: "data/eu-fi.jsonl"
- config_name: eu-fr
data_files: "data/eu-fr.jsonl"
- config_name: eu-gl
data_files: "data/eu-gl.jsonl"
- config_name: eu-he
data_files: "data/eu-he.jsonl"
- config_name: eu-hi
data_files: "data/eu-hi.jsonl"
- config_name: eu-hr
data_files: "data/eu-hr.jsonl"
- config_name: eu-hu
data_files: "data/eu-hu.jsonl"
- config_name: eu-id
data_files: "data/eu-id.jsonl"
- config_name: eu-is
data_files: "data/eu-is.jsonl"
- config_name: eu-it
data_files: "data/eu-it.jsonl"
- config_name: eu-ja
data_files: "data/eu-ja.jsonl"
- config_name: eu-ka
data_files: "data/eu-ka.jsonl"
- config_name: eu-ko
data_files: "data/eu-ko.jsonl"
- config_name: eu-lt
data_files: "data/eu-lt.jsonl"
- config_name: eu-lv
data_files: "data/eu-lv.jsonl"
- config_name: eu-mk
data_files: "data/eu-mk.jsonl"
- config_name: eu-ml
data_files: "data/eu-ml.jsonl"
- config_name: eu-ms
data_files: "data/eu-ms.jsonl"
- config_name: eu-nl
data_files: "data/eu-nl.jsonl"
- config_name: eu-no
data_files: "data/eu-no.jsonl"
- config_name: eu-pl
data_files: "data/eu-pl.jsonl"
- config_name: eu-pt
data_files: "data/eu-pt.jsonl"
- config_name: eu-ro
data_files: "data/eu-ro.jsonl"
- config_name: eu-ru
data_files: "data/eu-ru.jsonl"
- config_name: eu-si
data_files: "data/eu-si.jsonl"
- config_name: eu-sk
data_files: "data/eu-sk.jsonl"
- config_name: eu-sl
data_files: "data/eu-sl.jsonl"
- config_name: eu-sq
data_files: "data/eu-sq.jsonl"
- config_name: eu-sr
data_files: "data/eu-sr.jsonl"
- config_name: eu-sv
data_files: "data/eu-sv.jsonl"
- config_name: eu-ta
data_files: "data/eu-ta.jsonl"
- config_name: eu-te
data_files: "data/eu-te.jsonl"
- config_name: eu-th
data_files: "data/eu-th.jsonl"
- config_name: eu-tl
data_files: "data/eu-tl.jsonl"
- config_name: eu-tr
data_files: "data/eu-tr.jsonl"
- config_name: eu-uk
data_files: "data/eu-uk.jsonl"
- config_name: eu-ur
data_files: "data/eu-ur.jsonl"
- config_name: eu-vi
data_files: "data/eu-vi.jsonl"
- config_name: eu-pt_br
data_files: "data/eu-pt_br.jsonl"
- config_name: eu-ze_en
data_files: "data/eu-ze_en.jsonl"
- config_name: eu-ze_zh
data_files: "data/eu-ze_zh.jsonl"
- config_name: eu-zh_cn
data_files: "data/eu-zh_cn.jsonl"
- config_name: eu-zh_tw
data_files: "data/eu-zh_tw.jsonl"
- config_name: fa-fi
data_files: "data/fa-fi.jsonl"
- config_name: fa-fr
data_files: "data/fa-fr.jsonl"
- config_name: fa-gl
data_files: "data/fa-gl.jsonl"
- config_name: fa-he
data_files: "data/fa-he.jsonl"
- config_name: fa-hi
data_files: "data/fa-hi.jsonl"
- config_name: fa-hr
data_files: "data/fa-hr.jsonl"
- config_name: fa-hu
data_files: "data/fa-hu.jsonl"
- config_name: fa-id
data_files: "data/fa-id.jsonl"
- config_name: fa-is
data_files: "data/fa-is.jsonl"
- config_name: fa-it
data_files: "data/fa-it.jsonl"
- config_name: fa-ja
data_files: "data/fa-ja.jsonl"
- config_name: fa-ka
data_files: "data/fa-ka.jsonl"
- config_name: fa-kk
data_files: "data/fa-kk.jsonl"
- config_name: fa-ko
data_files: "data/fa-ko.jsonl"
- config_name: fa-lt
data_files: "data/fa-lt.jsonl"
- config_name: fa-lv
data_files: "data/fa-lv.jsonl"
- config_name: fa-mk
data_files: "data/fa-mk.jsonl"
- config_name: fa-ml
data_files: "data/fa-ml.jsonl"
- config_name: fa-ms
data_files: "data/fa-ms.jsonl"
- config_name: fa-nl
data_files: "data/fa-nl.jsonl"
- config_name: fa-no
data_files: "data/fa-no.jsonl"
- config_name: fa-pl
data_files: "data/fa-pl.jsonl"
- config_name: fa-pt
data_files: "data/fa-pt.jsonl"
- config_name: fa-ro
data_files: "data/fa-ro.jsonl"
- config_name: fa-ru
data_files: "data/fa-ru.jsonl"
- config_name: fa-si
data_files: "data/fa-si.jsonl"
- config_name: fa-sk
data_files: "data/fa-sk.jsonl"
- config_name: fa-sl
data_files: "data/fa-sl.jsonl"
- config_name: fa-sq
data_files: "data/fa-sq.jsonl"
- config_name: fa-sr
data_files: "data/fa-sr.jsonl"
- config_name: fa-sv
data_files: "data/fa-sv.jsonl"
- config_name: fa-ta
data_files: "data/fa-ta.jsonl"
- config_name: fa-te
data_files: "data/fa-te.jsonl"
- config_name: fa-th
data_files: "data/fa-th.jsonl"
- config_name: fa-tl
data_files: "data/fa-tl.jsonl"
- config_name: fa-tr
data_files: "data/fa-tr.jsonl"
- config_name: fa-uk
data_files: "data/fa-uk.jsonl"
- config_name: fa-ur
data_files: "data/fa-ur.jsonl"
- config_name: fa-vi
data_files: "data/fa-vi.jsonl"
- config_name: fa-pt_br
data_files: "data/fa-pt_br.jsonl"
- config_name: fa-ze_en
data_files: "data/fa-ze_en.jsonl"
- config_name: fa-ze_zh
data_files: "data/fa-ze_zh.jsonl"
- config_name: fa-zh_cn
data_files: "data/fa-zh_cn.jsonl"
- config_name: fa-zh_tw
data_files: "data/fa-zh_tw.jsonl"
- config_name: fi-fr
data_files: "data/fi-fr.jsonl"
- config_name: fi-gl
data_files: "data/fi-gl.jsonl"
- config_name: fi-he
data_files: "data/fi-he.jsonl"
- config_name: fi-hi
data_files: "data/fi-hi.jsonl"
- config_name: fi-hr
data_files: "data/fi-hr.jsonl"
- config_name: fi-hu
data_files: "data/fi-hu.jsonl"
- config_name: fi-hy
data_files: "data/fi-hy.jsonl"
- config_name: fi-id
data_files: "data/fi-id.jsonl"
- config_name: fi-is
data_files: "data/fi-is.jsonl"
- config_name: fi-it
data_files: "data/fi-it.jsonl"
- config_name: fi-ja
data_files: "data/fi-ja.jsonl"
- config_name: fi-ka
data_files: "data/fi-ka.jsonl"
- config_name: fi-kk
data_files: "data/fi-kk.jsonl"
- config_name: fi-ko
data_files: "data/fi-ko.jsonl"
- config_name: fi-lt
data_files: "data/fi-lt.jsonl"
- config_name: fi-lv
data_files: "data/fi-lv.jsonl"
- config_name: fi-mk
data_files: "data/fi-mk.jsonl"
- config_name: fi-ml
data_files: "data/fi-ml.jsonl"
- config_name: fi-ms
data_files: "data/fi-ms.jsonl"
- config_name: fi-nl
data_files: "data/fi-nl.jsonl"
- config_name: fi-no
data_files: "data/fi-no.jsonl"
- config_name: fi-pl
data_files: "data/fi-pl.jsonl"
- config_name: fi-pt
data_files: "data/fi-pt.jsonl"
- config_name: fi-ro
data_files: "data/fi-ro.jsonl"
- config_name: fi-ru
data_files: "data/fi-ru.jsonl"
- config_name: fi-si
data_files: "data/fi-si.jsonl"
- config_name: fi-sk
data_files: "data/fi-sk.jsonl"
- config_name: fi-sl
data_files: "data/fi-sl.jsonl"
- config_name: fi-sq
data_files: "data/fi-sq.jsonl"
- config_name: fi-sr
data_files: "data/fi-sr.jsonl"
- config_name: fi-sv
data_files: "data/fi-sv.jsonl"
- config_name: fi-ta
data_files: "data/fi-ta.jsonl"
- config_name: fi-te
data_files: "data/fi-te.jsonl"
- config_name: fi-th
data_files: "data/fi-th.jsonl"
- config_name: fi-tl
data_files: "data/fi-tl.jsonl"
- config_name: fi-tr
data_files: "data/fi-tr.jsonl"
- config_name: fi-uk
data_files: "data/fi-uk.jsonl"
- config_name: fi-ur
data_files: "data/fi-ur.jsonl"
- config_name: fi-vi
data_files: "data/fi-vi.jsonl"
- config_name: fi-pt_br
data_files: "data/fi-pt_br.jsonl"
- config_name: fi-ze_en
data_files: "data/fi-ze_en.jsonl"
- config_name: fi-ze_zh
data_files: "data/fi-ze_zh.jsonl"
- config_name: fi-zh_cn
data_files: "data/fi-zh_cn.jsonl"
- config_name: fi-zh_tw
data_files: "data/fi-zh_tw.jsonl"
- config_name: fr-gl
data_files: "data/fr-gl.jsonl"
- config_name: fr-he
data_files: "data/fr-he.jsonl"
- config_name: fr-hi
data_files: "data/fr-hi.jsonl"
- config_name: fr-hr
data_files: "data/fr-hr.jsonl"
- config_name: fr-hu
data_files: "data/fr-hu.jsonl"
- config_name: fr-hy
data_files: "data/fr-hy.jsonl"
- config_name: fr-id
data_files: "data/fr-id.jsonl"
- config_name: fr-is
data_files: "data/fr-is.jsonl"
- config_name: fr-it
data_files: "data/fr-it.jsonl"
- config_name: fr-ja
data_files: "data/fr-ja.jsonl"
- config_name: fr-ka
data_files: "data/fr-ka.jsonl"
- config_name: fr-kk
data_files: "data/fr-kk.jsonl"
- config_name: fr-ko
data_files: "data/fr-ko.jsonl"
- config_name: fr-lt
data_files: "data/fr-lt.jsonl"
- config_name: fr-lv
data_files: "data/fr-lv.jsonl"
- config_name: fr-mk
data_files: "data/fr-mk.jsonl"
- config_name: fr-ml
data_files: "data/fr-ml.jsonl"
- config_name: fr-ms
data_files: "data/fr-ms.jsonl"
- config_name: fr-nl
data_files: "data/fr-nl.jsonl"
- config_name: fr-no
data_files: "data/fr-no.jsonl"
- config_name: fr-pl
data_files: "data/fr-pl.jsonl"
- config_name: fr-pt
data_files: "data/fr-pt.jsonl"
- config_name: fr-ro
data_files: "data/fr-ro.jsonl"
- config_name: fr-ru
data_files: "data/fr-ru.jsonl"
- config_name: fr-si
data_files: "data/fr-si.jsonl"
- config_name: fr-sk
data_files: "data/fr-sk.jsonl"
- config_name: fr-sl
data_files: "data/fr-sl.jsonl"
- config_name: fr-sq
data_files: "data/fr-sq.jsonl"
- config_name: fr-sr
data_files: "data/fr-sr.jsonl"
- config_name: fr-sv
data_files: "data/fr-sv.jsonl"
- config_name: fr-ta
data_files: "data/fr-ta.jsonl"
- config_name: fr-te
data_files: "data/fr-te.jsonl"
- config_name: fr-th
data_files: "data/fr-th.jsonl"
- config_name: fr-tl
data_files: "data/fr-tl.jsonl"
- config_name: fr-tr
data_files: "data/fr-tr.jsonl"
- config_name: fr-uk
data_files: "data/fr-uk.jsonl"
- config_name: fr-ur
data_files: "data/fr-ur.jsonl"
- config_name: fr-vi
data_files: "data/fr-vi.jsonl"
- config_name: fr-pt_br
data_files: "data/fr-pt_br.jsonl"
- config_name: fr-ze_en
data_files: "data/fr-ze_en.jsonl"
- config_name: fr-ze_zh
data_files: "data/fr-ze_zh.jsonl"
- config_name: fr-zh_cn
data_files: "data/fr-zh_cn.jsonl"
- config_name: fr-zh_tw
data_files: "data/fr-zh_tw.jsonl"
- config_name: gl-he
data_files: "data/gl-he.jsonl"
- config_name: gl-hi
data_files: "data/gl-hi.jsonl"
- config_name: gl-hr
data_files: "data/gl-hr.jsonl"
- config_name: gl-hu
data_files: "data/gl-hu.jsonl"
- config_name: gl-id
data_files: "data/gl-id.jsonl"
- config_name: gl-is
data_files: "data/gl-is.jsonl"
- config_name: gl-it
data_files: "data/gl-it.jsonl"
- config_name: gl-ja
data_files: "data/gl-ja.jsonl"
- config_name: gl-ka
data_files: "data/gl-ka.jsonl"
- config_name: gl-ko
data_files: "data/gl-ko.jsonl"
- config_name: gl-lt
data_files: "data/gl-lt.jsonl"
- config_name: gl-lv
data_files: "data/gl-lv.jsonl"
- config_name: gl-mk
data_files: "data/gl-mk.jsonl"
- config_name: gl-ml
data_files: "data/gl-ml.jsonl"
- config_name: gl-ms
data_files: "data/gl-ms.jsonl"
- config_name: gl-nl
data_files: "data/gl-nl.jsonl"
- config_name: gl-no
data_files: "data/gl-no.jsonl"
- config_name: gl-pl
data_files: "data/gl-pl.jsonl"
- config_name: gl-pt
data_files: "data/gl-pt.jsonl"
- config_name: gl-ro
data_files: "data/gl-ro.jsonl"
- config_name: gl-ru
data_files: "data/gl-ru.jsonl"
- config_name: gl-si
data_files: "data/gl-si.jsonl"
- config_name: gl-sk
data_files: "data/gl-sk.jsonl"
- config_name: gl-sl
data_files: "data/gl-sl.jsonl"
- config_name: gl-sq
data_files: "data/gl-sq.jsonl"
- config_name: gl-sr
data_files: "data/gl-sr.jsonl"
- config_name: gl-sv
data_files: "data/gl-sv.jsonl"
- config_name: gl-th
data_files: "data/gl-th.jsonl"
- config_name: gl-tr
data_files: "data/gl-tr.jsonl"
- config_name: gl-uk
data_files: "data/gl-uk.jsonl"
- config_name: gl-ur
data_files: "data/gl-ur.jsonl"
- config_name: gl-vi
data_files: "data/gl-vi.jsonl"
- config_name: gl-pt_br
data_files: "data/gl-pt_br.jsonl"
- config_name: gl-ze_en
data_files: "data/gl-ze_en.jsonl"
- config_name: gl-ze_zh
data_files: "data/gl-ze_zh.jsonl"
- config_name: gl-zh_cn
data_files: "data/gl-zh_cn.jsonl"
- config_name: gl-zh_tw
data_files: "data/gl-zh_tw.jsonl"
- config_name: he-hi
data_files: "data/he-hi.jsonl"
- config_name: he-hr
data_files: "data/he-hr.jsonl"
- config_name: he-hu
data_files: "data/he-hu.jsonl"
- config_name: he-hy
data_files: "data/he-hy.jsonl"
- config_name: he-id
data_files: "data/he-id.jsonl"
- config_name: he-is
data_files: "data/he-is.jsonl"
- config_name: he-it
data_files: "data/he-it.jsonl"
- config_name: he-ja
data_files: "data/he-ja.jsonl"
- config_name: he-ka
data_files: "data/he-ka.jsonl"
- config_name: he-kk
data_files: "data/he-kk.jsonl"
- config_name: he-ko
data_files: "data/he-ko.jsonl"
- config_name: he-lt
data_files: "data/he-lt.jsonl"
- config_name: he-lv
data_files: "data/he-lv.jsonl"
- config_name: he-mk
data_files: "data/he-mk.jsonl"
- config_name: he-ml
data_files: "data/he-ml.jsonl"
- config_name: he-ms
data_files: "data/he-ms.jsonl"
- config_name: he-nl
data_files: "data/he-nl.jsonl"
- config_name: he-no
data_files: "data/he-no.jsonl"
- config_name: he-pl
data_files: "data/he-pl.jsonl"
- config_name: he-pt
data_files: "data/he-pt.jsonl"
- config_name: he-ro
data_files: "data/he-ro.jsonl"
- config_name: he-ru
data_files: "data/he-ru.jsonl"
- config_name: he-si
data_files: "data/he-si.jsonl"
- config_name: he-sk
data_files: "data/he-sk.jsonl"
- config_name: he-sl
data_files: "data/he-sl.jsonl"
- config_name: he-sq
data_files: "data/he-sq.jsonl"
- config_name: he-sr
data_files: "data/he-sr.jsonl"
- config_name: he-sv
data_files: "data/he-sv.jsonl"
- config_name: he-ta
data_files: "data/he-ta.jsonl"
- config_name: he-te
data_files: "data/he-te.jsonl"
- config_name: he-th
data_files: "data/he-th.jsonl"
- config_name: he-tl
data_files: "data/he-tl.jsonl"
- config_name: he-tr
data_files: "data/he-tr.jsonl"
- config_name: he-uk
data_files: "data/he-uk.jsonl"
- config_name: he-ur
data_files: "data/he-ur.jsonl"
- config_name: he-vi
data_files: "data/he-vi.jsonl"
- config_name: he-pt_br
data_files: "data/he-pt_br.jsonl"
- config_name: he-ze_en
data_files: "data/he-ze_en.jsonl"
- config_name: he-ze_zh
data_files: "data/he-ze_zh.jsonl"
- config_name: he-zh_cn
data_files: "data/he-zh_cn.jsonl"
- config_name: he-zh_tw
data_files: "data/he-zh_tw.jsonl"
- config_name: hi-hr
data_files: "data/hi-hr.jsonl"
- config_name: hi-hu
data_files: "data/hi-hu.jsonl"
- config_name: hi-id
data_files: "data/hi-id.jsonl"
- config_name: hi-is
data_files: "data/hi-is.jsonl"
- config_name: hi-it
data_files: "data/hi-it.jsonl"
- config_name: hi-ja
data_files: "data/hi-ja.jsonl"
- config_name: hi-ka
data_files: "data/hi-ka.jsonl"
- config_name: hi-ko
data_files: "data/hi-ko.jsonl"
- config_name: hi-lt
data_files: "data/hi-lt.jsonl"
- config_name: hi-lv
data_files: "data/hi-lv.jsonl"
- config_name: hi-mk
data_files: "data/hi-mk.jsonl"
- config_name: hi-ml
data_files: "data/hi-ml.jsonl"
- config_name: hi-ms
data_files: "data/hi-ms.jsonl"
- config_name: hi-nl
data_files: "data/hi-nl.jsonl"
- config_name: hi-no
data_files: "data/hi-no.jsonl"
- config_name: hi-pl
data_files: "data/hi-pl.jsonl"
- config_name: hi-pt
data_files: "data/hi-pt.jsonl"
- config_name: hi-ro
data_files: "data/hi-ro.jsonl"
- config_name: hi-ru
data_files: "data/hi-ru.jsonl"
- config_name: hi-si
data_files: "data/hi-si.jsonl"
- config_name: hi-sk
data_files: "data/hi-sk.jsonl"
- config_name: hi-sl
data_files: "data/hi-sl.jsonl"
- config_name: hi-sq
data_files: "data/hi-sq.jsonl"
- config_name: hi-sr
data_files: "data/hi-sr.jsonl"
- config_name: hi-sv
data_files: "data/hi-sv.jsonl"
- config_name: hi-ta
data_files: "data/hi-ta.jsonl"
- config_name: hi-te
data_files: "data/hi-te.jsonl"
- config_name: hi-th
data_files: "data/hi-th.jsonl"
- config_name: hi-tl
data_files: "data/hi-tl.jsonl"
- config_name: hi-tr
data_files: "data/hi-tr.jsonl"
- config_name: hi-uk
data_files: "data/hi-uk.jsonl"
- config_name: hi-ur
data_files: "data/hi-ur.jsonl"
- config_name: hi-vi
data_files: "data/hi-vi.jsonl"
- config_name: hi-pt_br
data_files: "data/hi-pt_br.jsonl"
- config_name: hi-ze_en
data_files: "data/hi-ze_en.jsonl"
- config_name: hi-ze_zh
data_files: "data/hi-ze_zh.jsonl"
- config_name: hi-zh_cn
data_files: "data/hi-zh_cn.jsonl"
- config_name: hi-zh_tw
data_files: "data/hi-zh_tw.jsonl"
- config_name: hr-hu
data_files: "data/hr-hu.jsonl"
- config_name: hr-hy
data_files: "data/hr-hy.jsonl"
- config_name: hr-id
data_files: "data/hr-id.jsonl"
- config_name: hr-is
data_files: "data/hr-is.jsonl"
- config_name: hr-it
data_files: "data/hr-it.jsonl"
- config_name: hr-ja
data_files: "data/hr-ja.jsonl"
- config_name: hr-ka
data_files: "data/hr-ka.jsonl"
- config_name: hr-kk
data_files: "data/hr-kk.jsonl"
- config_name: hr-ko
data_files: "data/hr-ko.jsonl"
- config_name: hr-lt
data_files: "data/hr-lt.jsonl"
- config_name: hr-lv
data_files: "data/hr-lv.jsonl"
- config_name: hr-mk
data_files: "data/hr-mk.jsonl"
- config_name: hr-ml
data_files: "data/hr-ml.jsonl"
- config_name: hr-ms
data_files: "data/hr-ms.jsonl"
- config_name: hr-nl
data_files: "data/hr-nl.jsonl"
- config_name: hr-no
data_files: "data/hr-no.jsonl"
- config_name: hr-pl
data_files: "data/hr-pl.jsonl"
- config_name: hr-pt
data_files: "data/hr-pt.jsonl"
- config_name: hr-ro
data_files: "data/hr-ro.jsonl"
- config_name: hr-ru
data_files: "data/hr-ru.jsonl"
- config_name: hr-si
data_files: "data/hr-si.jsonl"
- config_name: hr-sk
data_files: "data/hr-sk.jsonl"
- config_name: hr-sl
data_files: "data/hr-sl.jsonl"
- config_name: hr-sq
data_files: "data/hr-sq.jsonl"
- config_name: hr-sr
data_files: "data/hr-sr.jsonl"
- config_name: hr-sv
data_files: "data/hr-sv.jsonl"
- config_name: hr-ta
data_files: "data/hr-ta.jsonl"
- config_name: hr-te
data_files: "data/hr-te.jsonl"
- config_name: hr-th
data_files: "data/hr-th.jsonl"
- config_name: hr-tl
data_files: "data/hr-tl.jsonl"
- config_name: hr-tr
data_files: "data/hr-tr.jsonl"
- config_name: hr-uk
data_files: "data/hr-uk.jsonl"
- config_name: hr-ur
data_files: "data/hr-ur.jsonl"
- config_name: hr-vi
data_files: "data/hr-vi.jsonl"
- config_name: hr-pt_br
data_files: "data/hr-pt_br.jsonl"
- config_name: hr-ze_en
data_files: "data/hr-ze_en.jsonl"
- config_name: hr-ze_zh
data_files: "data/hr-ze_zh.jsonl"
- config_name: hr-zh_cn
data_files: "data/hr-zh_cn.jsonl"
- config_name: hr-zh_tw
data_files: "data/hr-zh_tw.jsonl"
- config_name: hu-hy
data_files: "data/hu-hy.jsonl"
- config_name: hu-id
data_files: "data/hu-id.jsonl"
- config_name: hu-is
data_files: "data/hu-is.jsonl"
- config_name: hu-it
data_files: "data/hu-it.jsonl"
- config_name: hu-ja
data_files: "data/hu-ja.jsonl"
- config_name: hu-ka
data_files: "data/hu-ka.jsonl"
- config_name: hu-kk
data_files: "data/hu-kk.jsonl"
- config_name: hu-ko
data_files: "data/hu-ko.jsonl"
- config_name: hu-lt
data_files: "data/hu-lt.jsonl"
- config_name: hu-lv
data_files: "data/hu-lv.jsonl"
- config_name: hu-mk
data_files: "data/hu-mk.jsonl"
- config_name: hu-ml
data_files: "data/hu-ml.jsonl"
- config_name: hu-ms
data_files: "data/hu-ms.jsonl"
- config_name: hu-nl
data_files: "data/hu-nl.jsonl"
- config_name: hu-no
data_files: "data/hu-no.jsonl"
- config_name: hu-pl
data_files: "data/hu-pl.jsonl"
- config_name: hu-pt
data_files: "data/hu-pt.jsonl"
- config_name: hu-ro
data_files: "data/hu-ro.jsonl"
- config_name: hu-ru
data_files: "data/hu-ru.jsonl"
- config_name: hu-si
data_files: "data/hu-si.jsonl"
- config_name: hu-sk
data_files: "data/hu-sk.jsonl"
- config_name: hu-sl
data_files: "data/hu-sl.jsonl"
- config_name: hu-sq
data_files: "data/hu-sq.jsonl"
- config_name: hu-sr
data_files: "data/hu-sr.jsonl"
- config_name: hu-sv
data_files: "data/hu-sv.jsonl"
- config_name: hu-ta
data_files: "data/hu-ta.jsonl"
- config_name: hu-te
data_files: "data/hu-te.jsonl"
- config_name: hu-th
data_files: "data/hu-th.jsonl"
- config_name: hu-tl
data_files: "data/hu-tl.jsonl"
- config_name: hu-tr
data_files: "data/hu-tr.jsonl"
- config_name: hu-uk
data_files: "data/hu-uk.jsonl"
- config_name: hu-ur
data_files: "data/hu-ur.jsonl"
- config_name: hu-vi
data_files: "data/hu-vi.jsonl"
- config_name: hu-pt_br
data_files: "data/hu-pt_br.jsonl"
- config_name: hu-ze_en
data_files: "data/hu-ze_en.jsonl"
- config_name: hu-ze_zh
data_files: "data/hu-ze_zh.jsonl"
- config_name: hu-zh_cn
data_files: "data/hu-zh_cn.jsonl"
- config_name: hu-zh_tw
data_files: "data/hu-zh_tw.jsonl"
- config_name: hy-id
data_files: "data/hy-id.jsonl"
- config_name: hy-it
data_files: "data/hy-it.jsonl"
- config_name: hy-mk
data_files: "data/hy-mk.jsonl"
- config_name: hy-ml
data_files: "data/hy-ml.jsonl"
- config_name: hy-nl
data_files: "data/hy-nl.jsonl"
- config_name: hy-pl
data_files: "data/hy-pl.jsonl"
- config_name: hy-pt
data_files: "data/hy-pt.jsonl"
- config_name: hy-ro
data_files: "data/hy-ro.jsonl"
- config_name: hy-ru
data_files: "data/hy-ru.jsonl"
- config_name: hy-sk
data_files: "data/hy-sk.jsonl"
- config_name: hy-sl
data_files: "data/hy-sl.jsonl"
- config_name: hy-sq
data_files: "data/hy-sq.jsonl"
- config_name: hy-sr
data_files: "data/hy-sr.jsonl"
- config_name: hy-sv
data_files: "data/hy-sv.jsonl"
- config_name: hy-tr
data_files: "data/hy-tr.jsonl"
- config_name: hy-pt_br
data_files: "data/hy-pt_br.jsonl"
- config_name: hy-zh_cn
data_files: "data/hy-zh_cn.jsonl"
- config_name: hy-zh_tw
data_files: "data/hy-zh_tw.jsonl"
- config_name: id-is
data_files: "data/id-is.jsonl"
- config_name: id-it
data_files: "data/id-it.jsonl"
- config_name: id-ja
data_files: "data/id-ja.jsonl"
- config_name: id-ka
data_files: "data/id-ka.jsonl"
- config_name: id-kk
data_files: "data/id-kk.jsonl"
- config_name: id-ko
data_files: "data/id-ko.jsonl"
- config_name: id-lt
data_files: "data/id-lt.jsonl"
- config_name: id-lv
data_files: "data/id-lv.jsonl"
- config_name: id-mk
data_files: "data/id-mk.jsonl"
- config_name: id-ml
data_files: "data/id-ml.jsonl"
- config_name: id-ms
data_files: "data/id-ms.jsonl"
- config_name: id-nl
data_files: "data/id-nl.jsonl"
- config_name: id-pl
data_files: "data/id-pl.jsonl"
- config_name: id-pt
data_files: "data/id-pt.jsonl"
- config_name: id-ro
data_files: "data/id-ro.jsonl"
- config_name: id-ru
data_files: "data/id-ru.jsonl"
- config_name: id-si
data_files: "data/id-si.jsonl"
- config_name: id-sk
data_files: "data/id-sk.jsonl"
- config_name: id-sl
data_files: "data/id-sl.jsonl"
- config_name: id-sq
data_files: "data/id-sq.jsonl"
- config_name: id-sr
data_files: "data/id-sr.jsonl"
- config_name: id-sv
data_files: "data/id-sv.jsonl"
- config_name: id-ta
data_files: "data/id-ta.jsonl"
- config_name: id-te
data_files: "data/id-te.jsonl"
- config_name: id-th
data_files: "data/id-th.jsonl"
- config_name: id-tl
data_files: "data/id-tl.jsonl"
- config_name: id-tr
data_files: "data/id-tr.jsonl"
- config_name: id-uk
data_files: "data/id-uk.jsonl"
- config_name: id-ur
data_files: "data/id-ur.jsonl"
- config_name: id-vi
data_files: "data/id-vi.jsonl"
- config_name: id-pt_br
data_files: "data/id-pt_br.jsonl"
- config_name: id-ze_en
data_files: "data/id-ze_en.jsonl"
- config_name: id-ze_zh
data_files: "data/id-ze_zh.jsonl"
- config_name: id-zh_cn
data_files: "data/id-zh_cn.jsonl"
- config_name: id-zh_tw
data_files: "data/id-zh_tw.jsonl"
- config_name: is-it
data_files: "data/is-it.jsonl"
- config_name: is-ja
data_files: "data/is-ja.jsonl"
- config_name: is-ka
data_files: "data/is-ka.jsonl"
- config_name: is-kk
data_files: "data/is-kk.jsonl"
- config_name: is-ko
data_files: "data/is-ko.jsonl"
- config_name: is-lt
data_files: "data/is-lt.jsonl"
- config_name: is-lv
data_files: "data/is-lv.jsonl"
- config_name: is-mk
data_files: "data/is-mk.jsonl"
- config_name: is-ml
data_files: "data/is-ml.jsonl"
- config_name: is-ms
data_files: "data/is-ms.jsonl"
- config_name: is-nl
data_files: "data/is-nl.jsonl"
- config_name: is-no
data_files: "data/is-no.jsonl"
- config_name: is-pl
data_files: "data/is-pl.jsonl"
- config_name: is-pt
data_files: "data/is-pt.jsonl"
- config_name: is-ro
data_files: "data/is-ro.jsonl"
- config_name: is-ru
data_files: "data/is-ru.jsonl"
- config_name: is-si
data_files: "data/is-si.jsonl"
- config_name: is-sk
data_files: "data/is-sk.jsonl"
- config_name: is-sl
data_files: "data/is-sl.jsonl"
- config_name: is-sq
data_files: "data/is-sq.jsonl"
- config_name: is-sr
data_files: "data/is-sr.jsonl"
- config_name: is-sv
data_files: "data/is-sv.jsonl"
- config_name: is-ta
data_files: "data/is-ta.jsonl"
- config_name: is-th
data_files: "data/is-th.jsonl"
- config_name: is-tl
data_files: "data/is-tl.jsonl"
- config_name: is-tr
data_files: "data/is-tr.jsonl"
- config_name: is-uk
data_files: "data/is-uk.jsonl"
- config_name: is-ur
data_files: "data/is-ur.jsonl"
- config_name: is-vi
data_files: "data/is-vi.jsonl"
- config_name: is-pt_br
data_files: "data/is-pt_br.jsonl"
- config_name: is-ze_en
data_files: "data/is-ze_en.jsonl"
- config_name: is-ze_zh
data_files: "data/is-ze_zh.jsonl"
- config_name: is-zh_cn
data_files: "data/is-zh_cn.jsonl"
- config_name: is-zh_tw
data_files: "data/is-zh_tw.jsonl"
- config_name: it-ja
data_files: "data/it-ja.jsonl"
- config_name: it-ka
data_files: "data/it-ka.jsonl"
- config_name: it-kk
data_files: "data/it-kk.jsonl"
- config_name: it-ko
data_files: "data/it-ko.jsonl"
- config_name: it-lt
data_files: "data/it-lt.jsonl"
- config_name: it-lv
data_files: "data/it-lv.jsonl"
- config_name: it-mk
data_files: "data/it-mk.jsonl"
- config_name: it-ml
data_files: "data/it-ml.jsonl"
- config_name: it-ms
data_files: "data/it-ms.jsonl"
- config_name: it-nl
data_files: "data/it-nl.jsonl"
- config_name: it-no
data_files: "data/it-no.jsonl"
- config_name: it-pl
data_files: "data/it-pl.jsonl"
- config_name: it-pt
data_files: "data/it-pt.jsonl"
- config_name: it-ro
data_files: "data/it-ro.jsonl"
- config_name: it-ru
data_files: "data/it-ru.jsonl"
- config_name: it-si
data_files: "data/it-si.jsonl"
- config_name: it-sk
data_files: "data/it-sk.jsonl"
- config_name: it-sl
data_files: "data/it-sl.jsonl"
- config_name: it-sq
data_files: "data/it-sq.jsonl"
- config_name: it-sr
data_files: "data/it-sr.jsonl"
- config_name: it-sv
data_files: "data/it-sv.jsonl"
- config_name: it-ta
data_files: "data/it-ta.jsonl"
- config_name: it-te
data_files: "data/it-te.jsonl"
- config_name: it-th
data_files: "data/it-th.jsonl"
- config_name: it-tl
data_files: "data/it-tl.jsonl"
- config_name: it-tr
data_files: "data/it-tr.jsonl"
- config_name: it-uk
data_files: "data/it-uk.jsonl"
- config_name: it-ur
data_files: "data/it-ur.jsonl"
- config_name: it-vi
data_files: "data/it-vi.jsonl"
- config_name: it-pt_br
data_files: "data/it-pt_br.jsonl"
- config_name: it-ze_en
data_files: "data/it-ze_en.jsonl"
- config_name: it-ze_zh
data_files: "data/it-ze_zh.jsonl"
- config_name: it-zh_cn
data_files: "data/it-zh_cn.jsonl"
- config_name: it-zh_tw
data_files: "data/it-zh_tw.jsonl"
- config_name: ja-ka
data_files: "data/ja-ka.jsonl"
- config_name: ja-kk
data_files: "data/ja-kk.jsonl"
- config_name: ja-ko
data_files: "data/ja-ko.jsonl"
- config_name: ja-lt
data_files: "data/ja-lt.jsonl"
- config_name: ja-lv
data_files: "data/ja-lv.jsonl"
- config_name: ja-mk
data_files: "data/ja-mk.jsonl"
- config_name: ja-ml
data_files: "data/ja-ml.jsonl"
- config_name: ja-ms
data_files: "data/ja-ms.jsonl"
- config_name: ja-nl
data_files: "data/ja-nl.jsonl"
- config_name: ja-no
data_files: "data/ja-no.jsonl"
- config_name: ja-pl
data_files: "data/ja-pl.jsonl"
- config_name: ja-pt
data_files: "data/ja-pt.jsonl"
- config_name: ja-ro
data_files: "data/ja-ro.jsonl"
- config_name: ja-ru
data_files: "data/ja-ru.jsonl"
- config_name: ja-si
data_files: "data/ja-si.jsonl"
- config_name: ja-sk
data_files: "data/ja-sk.jsonl"
- config_name: ja-sl
data_files: "data/ja-sl.jsonl"
- config_name: ja-sq
data_files: "data/ja-sq.jsonl"
- config_name: ja-sr
data_files: "data/ja-sr.jsonl"
- config_name: ja-sv
data_files: "data/ja-sv.jsonl"
- config_name: ja-ta
data_files: "data/ja-ta.jsonl"
- config_name: ja-te
data_files: "data/ja-te.jsonl"
- config_name: ja-th
data_files: "data/ja-th.jsonl"
- config_name: ja-tl
data_files: "data/ja-tl.jsonl"
- config_name: ja-tr
data_files: "data/ja-tr.jsonl"
- config_name: ja-uk
data_files: "data/ja-uk.jsonl"
- config_name: ja-ur
data_files: "data/ja-ur.jsonl"
- config_name: ja-vi
data_files: "data/ja-vi.jsonl"
- config_name: ja-pt_br
data_files: "data/ja-pt_br.jsonl"
- config_name: ja-ze_en
data_files: "data/ja-ze_en.jsonl"
- config_name: ja-ze_zh
data_files: "data/ja-ze_zh.jsonl"
- config_name: ja-zh_cn
data_files: "data/ja-zh_cn.jsonl"
- config_name: ja-zh_tw
data_files: "data/ja-zh_tw.jsonl"
- config_name: ka-ko
data_files: "data/ka-ko.jsonl"
- config_name: ka-lt
data_files: "data/ka-lt.jsonl"
- config_name: ka-lv
data_files: "data/ka-lv.jsonl"
- config_name: ka-mk
data_files: "data/ka-mk.jsonl"
- config_name: ka-ml
data_files: "data/ka-ml.jsonl"
- config_name: ka-ms
data_files: "data/ka-ms.jsonl"
- config_name: ka-nl
data_files: "data/ka-nl.jsonl"
- config_name: ka-no
data_files: "data/ka-no.jsonl"
- config_name: ka-pl
data_files: "data/ka-pl.jsonl"
- config_name: ka-pt
data_files: "data/ka-pt.jsonl"
- config_name: ka-ro
data_files: "data/ka-ro.jsonl"
- config_name: ka-ru
data_files: "data/ka-ru.jsonl"
- config_name: ka-si
data_files: "data/ka-si.jsonl"
- config_name: ka-sk
data_files: "data/ka-sk.jsonl"
- config_name: ka-sl
data_files: "data/ka-sl.jsonl"
- config_name: ka-sq
data_files: "data/ka-sq.jsonl"
- config_name: ka-sr
data_files: "data/ka-sr.jsonl"
- config_name: ka-sv
data_files: "data/ka-sv.jsonl"
- config_name: ka-th
data_files: "data/ka-th.jsonl"
- config_name: ka-tl
data_files: "data/ka-tl.jsonl"
- config_name: ka-tr
data_files: "data/ka-tr.jsonl"
- config_name: ka-uk
data_files: "data/ka-uk.jsonl"
- config_name: ka-ur
data_files: "data/ka-ur.jsonl"
- config_name: ka-vi
data_files: "data/ka-vi.jsonl"
- config_name: ka-pt_br
data_files: "data/ka-pt_br.jsonl"
- config_name: ka-ze_en
data_files: "data/ka-ze_en.jsonl"
- config_name: ka-ze_zh
data_files: "data/ka-ze_zh.jsonl"
- config_name: ka-zh_cn
data_files: "data/ka-zh_cn.jsonl"
- config_name: ka-zh_tw
data_files: "data/ka-zh_tw.jsonl"
- config_name: kk-lt
data_files: "data/kk-lt.jsonl"
- config_name: kk-lv
data_files: "data/kk-lv.jsonl"
- config_name: kk-ms
data_files: "data/kk-ms.jsonl"
- config_name: kk-nl
data_files: "data/kk-nl.jsonl"
- config_name: kk-no
data_files: "data/kk-no.jsonl"
- config_name: kk-pl
data_files: "data/kk-pl.jsonl"
- config_name: kk-pt
data_files: "data/kk-pt.jsonl"
- config_name: kk-ro
data_files: "data/kk-ro.jsonl"
- config_name: kk-ru
data_files: "data/kk-ru.jsonl"
- config_name: kk-sk
data_files: "data/kk-sk.jsonl"
- config_name: kk-sl
data_files: "data/kk-sl.jsonl"
- config_name: kk-sr
data_files: "data/kk-sr.jsonl"
- config_name: kk-sv
data_files: "data/kk-sv.jsonl"
- config_name: kk-th
data_files: "data/kk-th.jsonl"
- config_name: kk-tr
data_files: "data/kk-tr.jsonl"
- config_name: kk-uk
data_files: "data/kk-uk.jsonl"
- config_name: kk-vi
data_files: "data/kk-vi.jsonl"
- config_name: kk-pt_br
data_files: "data/kk-pt_br.jsonl"
- config_name: kk-zh_cn
data_files: "data/kk-zh_cn.jsonl"
- config_name: ko-lt
data_files: "data/ko-lt.jsonl"
- config_name: ko-lv
data_files: "data/ko-lv.jsonl"
- config_name: ko-mk
data_files: "data/ko-mk.jsonl"
- config_name: ko-ml
data_files: "data/ko-ml.jsonl"
- config_name: ko-ms
data_files: "data/ko-ms.jsonl"
- config_name: ko-nl
data_files: "data/ko-nl.jsonl"
- config_name: ko-no
data_files: "data/ko-no.jsonl"
- config_name: ko-pl
data_files: "data/ko-pl.jsonl"
- config_name: ko-pt
data_files: "data/ko-pt.jsonl"
- config_name: ko-ro
data_files: "data/ko-ro.jsonl"
- config_name: ko-ru
data_files: "data/ko-ru.jsonl"
- config_name: ko-si
data_files: "data/ko-si.jsonl"
- config_name: ko-sk
data_files: "data/ko-sk.jsonl"
- config_name: ko-sl
data_files: "data/ko-sl.jsonl"
- config_name: ko-sq
data_files: "data/ko-sq.jsonl"
- config_name: ko-sr
data_files: "data/ko-sr.jsonl"
- config_name: ko-sv
data_files: "data/ko-sv.jsonl"
- config_name: ko-ta
data_files: "data/ko-ta.jsonl"
- config_name: ko-te
data_files: "data/ko-te.jsonl"
- config_name: ko-th
data_files: "data/ko-th.jsonl"
- config_name: ko-tl
data_files: "data/ko-tl.jsonl"
- config_name: ko-tr
data_files: "data/ko-tr.jsonl"
- config_name: ko-uk
data_files: "data/ko-uk.jsonl"
- config_name: ko-ur
data_files: "data/ko-ur.jsonl"
- config_name: ko-vi
data_files: "data/ko-vi.jsonl"
- config_name: ko-pt_br
data_files: "data/ko-pt_br.jsonl"
- config_name: ko-ze_en
data_files: "data/ko-ze_en.jsonl"
- config_name: ko-ze_zh
data_files: "data/ko-ze_zh.jsonl"
- config_name: ko-zh_cn
data_files: "data/ko-zh_cn.jsonl"
- config_name: ko-zh_tw
data_files: "data/ko-zh_tw.jsonl"
- config_name: lt-lv
data_files: "data/lt-lv.jsonl"
- config_name: lt-mk
data_files: "data/lt-mk.jsonl"
- config_name: lt-ml
data_files: "data/lt-ml.jsonl"
- config_name: lt-ms
data_files: "data/lt-ms.jsonl"
- config_name: lt-nl
data_files: "data/lt-nl.jsonl"
- config_name: lt-no
data_files: "data/lt-no.jsonl"
- config_name: lt-pl
data_files: "data/lt-pl.jsonl"
- config_name: lt-pt
data_files: "data/lt-pt.jsonl"
- config_name: lt-ro
data_files: "data/lt-ro.jsonl"
- config_name: lt-ru
data_files: "data/lt-ru.jsonl"
- config_name: lt-si
data_files: "data/lt-si.jsonl"
- config_name: lt-sk
data_files: "data/lt-sk.jsonl"
- config_name: lt-sl
data_files: "data/lt-sl.jsonl"
- config_name: lt-sq
data_files: "data/lt-sq.jsonl"
- config_name: lt-sr
data_files: "data/lt-sr.jsonl"
- config_name: lt-sv
data_files: "data/lt-sv.jsonl"
- config_name: lt-ta
data_files: "data/lt-ta.jsonl"
- config_name: lt-te
data_files: "data/lt-te.jsonl"
- config_name: lt-th
data_files: "data/lt-th.jsonl"
- config_name: lt-tl
data_files: "data/lt-tl.jsonl"
- config_name: lt-tr
data_files: "data/lt-tr.jsonl"
- config_name: lt-uk
data_files: "data/lt-uk.jsonl"
- config_name: lt-ur
data_files: "data/lt-ur.jsonl"
- config_name: lt-vi
data_files: "data/lt-vi.jsonl"
- config_name: lt-pt_br
data_files: "data/lt-pt_br.jsonl"
- config_name: lt-ze_en
data_files: "data/lt-ze_en.jsonl"
- config_name: lt-ze_zh
data_files: "data/lt-ze_zh.jsonl"
- config_name: lt-zh_cn
data_files: "data/lt-zh_cn.jsonl"
- config_name: lt-zh_tw
data_files: "data/lt-zh_tw.jsonl"
- config_name: lv-mk
data_files: "data/lv-mk.jsonl"
- config_name: lv-ml
data_files: "data/lv-ml.jsonl"
- config_name: lv-ms
data_files: "data/lv-ms.jsonl"
- config_name: lv-nl
data_files: "data/lv-nl.jsonl"
- config_name: lv-no
data_files: "data/lv-no.jsonl"
- config_name: lv-pl
data_files: "data/lv-pl.jsonl"
- config_name: lv-pt
data_files: "data/lv-pt.jsonl"
- config_name: lv-ro
data_files: "data/lv-ro.jsonl"
- config_name: lv-ru
data_files: "data/lv-ru.jsonl"
- config_name: lv-si
data_files: "data/lv-si.jsonl"
- config_name: lv-sk
data_files: "data/lv-sk.jsonl"
- config_name: lv-sl
data_files: "data/lv-sl.jsonl"
- config_name: lv-sq
data_files: "data/lv-sq.jsonl"
- config_name: lv-sr
data_files: "data/lv-sr.jsonl"
- config_name: lv-sv
data_files: "data/lv-sv.jsonl"
- config_name: lv-ta
data_files: "data/lv-ta.jsonl"
- config_name: lv-te
data_files: "data/lv-te.jsonl"
- config_name: lv-th
data_files: "data/lv-th.jsonl"
- config_name: lv-tr
data_files: "data/lv-tr.jsonl"
- config_name: lv-uk
data_files: "data/lv-uk.jsonl"
- config_name: lv-ur
data_files: "data/lv-ur.jsonl"
- config_name: lv-vi
data_files: "data/lv-vi.jsonl"
- config_name: lv-pt_br
data_files: "data/lv-pt_br.jsonl"
- config_name: lv-ze_en
data_files: "data/lv-ze_en.jsonl"
- config_name: lv-ze_zh
data_files: "data/lv-ze_zh.jsonl"
- config_name: lv-zh_cn
data_files: "data/lv-zh_cn.jsonl"
- config_name: lv-zh_tw
data_files: "data/lv-zh_tw.jsonl"
- config_name: mk-ml
data_files: "data/mk-ml.jsonl"
- config_name: mk-ms
data_files: "data/mk-ms.jsonl"
- config_name: mk-nl
data_files: "data/mk-nl.jsonl"
- config_name: mk-no
data_files: "data/mk-no.jsonl"
- config_name: mk-pl
data_files: "data/mk-pl.jsonl"
- config_name: mk-pt
data_files: "data/mk-pt.jsonl"
- config_name: mk-ro
data_files: "data/mk-ro.jsonl"
- config_name: mk-ru
data_files: "data/mk-ru.jsonl"
- config_name: mk-si
data_files: "data/mk-si.jsonl"
- config_name: mk-sk
data_files: "data/mk-sk.jsonl"
- config_name: mk-sl
data_files: "data/mk-sl.jsonl"
- config_name: mk-sq
data_files: "data/mk-sq.jsonl"
- config_name: mk-sr
data_files: "data/mk-sr.jsonl"
- config_name: mk-sv
data_files: "data/mk-sv.jsonl"
- config_name: mk-ta
data_files: "data/mk-ta.jsonl"
- config_name: mk-te
data_files: "data/mk-te.jsonl"
- config_name: mk-th
data_files: "data/mk-th.jsonl"
- config_name: mk-tl
data_files: "data/mk-tl.jsonl"
- config_name: mk-tr
data_files: "data/mk-tr.jsonl"
- config_name: mk-uk
data_files: "data/mk-uk.jsonl"
- config_name: mk-ur
data_files: "data/mk-ur.jsonl"
- config_name: mk-vi
data_files: "data/mk-vi.jsonl"
- config_name: mk-pt_br
data_files: "data/mk-pt_br.jsonl"
- config_name: mk-ze_en
data_files: "data/mk-ze_en.jsonl"
- config_name: mk-ze_zh
data_files: "data/mk-ze_zh.jsonl"
- config_name: mk-zh_cn
data_files: "data/mk-zh_cn.jsonl"
- config_name: mk-zh_tw
data_files: "data/mk-zh_tw.jsonl"
- config_name: ml-ms
data_files: "data/ml-ms.jsonl"
- config_name: ml-nl
data_files: "data/ml-nl.jsonl"
- config_name: ml-no
data_files: "data/ml-no.jsonl"
- config_name: ml-pl
data_files: "data/ml-pl.jsonl"
- config_name: ml-pt
data_files: "data/ml-pt.jsonl"
- config_name: ml-ro
data_files: "data/ml-ro.jsonl"
- config_name: ml-ru
data_files: "data/ml-ru.jsonl"
- config_name: ml-si
data_files: "data/ml-si.jsonl"
- config_name: ml-sk
data_files: "data/ml-sk.jsonl"
- config_name: ml-sl
data_files: "data/ml-sl.jsonl"
- config_name: ml-sq
data_files: "data/ml-sq.jsonl"
- config_name: ml-sr
data_files: "data/ml-sr.jsonl"
- config_name: ml-sv
data_files: "data/ml-sv.jsonl"
- config_name: ml-ta
data_files: "data/ml-ta.jsonl"
- config_name: ml-th
data_files: "data/ml-th.jsonl"
- config_name: ml-tl
data_files: "data/ml-tl.jsonl"
- config_name: ml-tr
data_files: "data/ml-tr.jsonl"
- config_name: ml-uk
data_files: "data/ml-uk.jsonl"
- config_name: ml-ur
data_files: "data/ml-ur.jsonl"
- config_name: ml-vi
data_files: "data/ml-vi.jsonl"
- config_name: ml-pt_br
data_files: "data/ml-pt_br.jsonl"
- config_name: ml-ze_en
data_files: "data/ml-ze_en.jsonl"
- config_name: ml-ze_zh
data_files: "data/ml-ze_zh.jsonl"
- config_name: ml-zh_cn
data_files: "data/ml-zh_cn.jsonl"
- config_name: ml-zh_tw
data_files: "data/ml-zh_tw.jsonl"
- config_name: ms-nl
data_files: "data/ms-nl.jsonl"
- config_name: ms-no
data_files: "data/ms-no.jsonl"
- config_name: ms-pl
data_files: "data/ms-pl.jsonl"
- config_name: ms-pt
data_files: "data/ms-pt.jsonl"
- config_name: ms-ro
data_files: "data/ms-ro.jsonl"
- config_name: ms-ru
data_files: "data/ms-ru.jsonl"
- config_name: ms-si
data_files: "data/ms-si.jsonl"
- config_name: ms-sk
data_files: "data/ms-sk.jsonl"
- config_name: ms-sl
data_files: "data/ms-sl.jsonl"
- config_name: ms-sq
data_files: "data/ms-sq.jsonl"
- config_name: ms-sr
data_files: "data/ms-sr.jsonl"
- config_name: ms-sv
data_files: "data/ms-sv.jsonl"
- config_name: ms-ta
data_files: "data/ms-ta.jsonl"
- config_name: ms-te
data_files: "data/ms-te.jsonl"
- config_name: ms-th
data_files: "data/ms-th.jsonl"
- config_name: ms-tl
data_files: "data/ms-tl.jsonl"
- config_name: ms-tr
data_files: "data/ms-tr.jsonl"
- config_name: ms-uk
data_files: "data/ms-uk.jsonl"
- config_name: ms-ur
data_files: "data/ms-ur.jsonl"
- config_name: ms-vi
data_files: "data/ms-vi.jsonl"
- config_name: ms-pt_br
data_files: "data/ms-pt_br.jsonl"
- config_name: ms-ze_en
data_files: "data/ms-ze_en.jsonl"
- config_name: ms-ze_zh
data_files: "data/ms-ze_zh.jsonl"
- config_name: ms-zh_cn
data_files: "data/ms-zh_cn.jsonl"
- config_name: ms-zh_tw
data_files: "data/ms-zh_tw.jsonl"
- config_name: nl-no
data_files: "data/nl-no.jsonl"
- config_name: nl-pl
data_files: "data/nl-pl.jsonl"
- config_name: nl-pt
data_files: "data/nl-pt.jsonl"
- config_name: nl-ro
data_files: "data/nl-ro.jsonl"
- config_name: nl-ru
data_files: "data/nl-ru.jsonl"
- config_name: nl-si
data_files: "data/nl-si.jsonl"
- config_name: nl-sk
data_files: "data/nl-sk.jsonl"
- config_name: nl-sl
data_files: "data/nl-sl.jsonl"
- config_name: nl-sq
data_files: "data/nl-sq.jsonl"
- config_name: nl-sr
data_files: "data/nl-sr.jsonl"
- config_name: nl-sv
data_files: "data/nl-sv.jsonl"
- config_name: nl-ta
data_files: "data/nl-ta.jsonl"
- config_name: nl-te
data_files: "data/nl-te.jsonl"
- config_name: nl-th
data_files: "data/nl-th.jsonl"
- config_name: nl-tl
data_files: "data/nl-tl.jsonl"
- config_name: nl-tr
data_files: "data/nl-tr.jsonl"
- config_name: nl-uk
data_files: "data/nl-uk.jsonl"
- config_name: nl-ur
data_files: "data/nl-ur.jsonl"
- config_name: nl-vi
data_files: "data/nl-vi.jsonl"
- config_name: nl-pt_br
data_files: "data/nl-pt_br.jsonl"
- config_name: nl-ze_en
data_files: "data/nl-ze_en.jsonl"
- config_name: nl-ze_zh
data_files: "data/nl-ze_zh.jsonl"
- config_name: nl-zh_cn
data_files: "data/nl-zh_cn.jsonl"
- config_name: nl-zh_tw
data_files: "data/nl-zh_tw.jsonl"
- config_name: no-pl
data_files: "data/no-pl.jsonl"
- config_name: no-pt
data_files: "data/no-pt.jsonl"
- config_name: no-ro
data_files: "data/no-ro.jsonl"
- config_name: no-ru
data_files: "data/no-ru.jsonl"
- config_name: no-si
data_files: "data/no-si.jsonl"
- config_name: no-sk
data_files: "data/no-sk.jsonl"
- config_name: no-sl
data_files: "data/no-sl.jsonl"
- config_name: no-sq
data_files: "data/no-sq.jsonl"
- config_name: no-sr
data_files: "data/no-sr.jsonl"
- config_name: no-sv
data_files: "data/no-sv.jsonl"
- config_name: no-ta
data_files: "data/no-ta.jsonl"
- config_name: no-te
data_files: "data/no-te.jsonl"
- config_name: no-th
data_files: "data/no-th.jsonl"
- config_name: no-tl
data_files: "data/no-tl.jsonl"
- config_name: no-tr
data_files: "data/no-tr.jsonl"
- config_name: no-uk
data_files: "data/no-uk.jsonl"
- config_name: no-ur
data_files: "data/no-ur.jsonl"
- config_name: no-vi
data_files: "data/no-vi.jsonl"
- config_name: no-pt_br
data_files: "data/no-pt_br.jsonl"
- config_name: no-ze_en
data_files: "data/no-ze_en.jsonl"
- config_name: no-ze_zh
data_files: "data/no-ze_zh.jsonl"
- config_name: no-zh_cn
data_files: "data/no-zh_cn.jsonl"
- config_name: no-zh_tw
data_files: "data/no-zh_tw.jsonl"
- config_name: pl-pt
data_files: "data/pl-pt.jsonl"
- config_name: pl-ro
data_files: "data/pl-ro.jsonl"
- config_name: pl-ru
data_files: "data/pl-ru.jsonl"
- config_name: pl-si
data_files: "data/pl-si.jsonl"
- config_name: pl-sk
data_files: "data/pl-sk.jsonl"
- config_name: pl-sl
data_files: "data/pl-sl.jsonl"
- config_name: pl-sq
data_files: "data/pl-sq.jsonl"
- config_name: pl-sr
data_files: "data/pl-sr.jsonl"
- config_name: pl-sv
data_files: "data/pl-sv.jsonl"
- config_name: pl-ta
data_files: "data/pl-ta.jsonl"
- config_name: pl-te
data_files: "data/pl-te.jsonl"
- config_name: pl-th
data_files: "data/pl-th.jsonl"
- config_name: pl-tl
data_files: "data/pl-tl.jsonl"
- config_name: pl-tr
data_files: "data/pl-tr.jsonl"
- config_name: pl-uk
data_files: "data/pl-uk.jsonl"
- config_name: pl-ur
data_files: "data/pl-ur.jsonl"
- config_name: pl-vi
data_files: "data/pl-vi.jsonl"
- config_name: pl-pt_br
data_files: "data/pl-pt_br.jsonl"
- config_name: pl-ze_en
data_files: "data/pl-ze_en.jsonl"
- config_name: pl-ze_zh
data_files: "data/pl-ze_zh.jsonl"
- config_name: pl-zh_cn
data_files: "data/pl-zh_cn.jsonl"
- config_name: pl-zh_tw
data_files: "data/pl-zh_tw.jsonl"
- config_name: pt-ro
data_files: "data/pt-ro.jsonl"
- config_name: pt-ru
data_files: "data/pt-ru.jsonl"
- config_name: pt-si
data_files: "data/pt-si.jsonl"
- config_name: pt-sk
data_files: "data/pt-sk.jsonl"
- config_name: pt-sl
data_files: "data/pt-sl.jsonl"
- config_name: pt-sq
data_files: "data/pt-sq.jsonl"
- config_name: pt-sr
data_files: "data/pt-sr.jsonl"
- config_name: pt-sv
data_files: "data/pt-sv.jsonl"
- config_name: pt-ta
data_files: "data/pt-ta.jsonl"
- config_name: pt-te
data_files: "data/pt-te.jsonl"
- config_name: pt-th
data_files: "data/pt-th.jsonl"
- config_name: pt-tl
data_files: "data/pt-tl.jsonl"
- config_name: pt-tr
data_files: "data/pt-tr.jsonl"
- config_name: pt-uk
data_files: "data/pt-uk.jsonl"
- config_name: pt-ur
data_files: "data/pt-ur.jsonl"
- config_name: pt-vi
data_files: "data/pt-vi.jsonl"
- config_name: pt-pt_br
data_files: "data/pt-pt_br.jsonl"
- config_name: pt-ze_en
data_files: "data/pt-ze_en.jsonl"
- config_name: pt-ze_zh
data_files: "data/pt-ze_zh.jsonl"
- config_name: pt-zh_cn
data_files: "data/pt-zh_cn.jsonl"
- config_name: pt-zh_tw
data_files: "data/pt-zh_tw.jsonl"
- config_name: ro-ru
data_files: "data/ro-ru.jsonl"
- config_name: ro-si
data_files: "data/ro-si.jsonl"
- config_name: ro-sk
data_files: "data/ro-sk.jsonl"
- config_name: ro-sl
data_files: "data/ro-sl.jsonl"
- config_name: ro-sq
data_files: "data/ro-sq.jsonl"
- config_name: ro-sr
data_files: "data/ro-sr.jsonl"
- config_name: ro-sv
data_files: "data/ro-sv.jsonl"
- config_name: ro-ta
data_files: "data/ro-ta.jsonl"
- config_name: ro-te
data_files: "data/ro-te.jsonl"
- config_name: ro-th
data_files: "data/ro-th.jsonl"
- config_name: ro-tl
data_files: "data/ro-tl.jsonl"
- config_name: ro-tr
data_files: "data/ro-tr.jsonl"
- config_name: ro-uk
data_files: "data/ro-uk.jsonl"
- config_name: ro-ur
data_files: "data/ro-ur.jsonl"
- config_name: ro-vi
data_files: "data/ro-vi.jsonl"
- config_name: ro-ze_en
data_files: "data/ro-ze_en.jsonl"
- config_name: ro-ze_zh
data_files: "data/ro-ze_zh.jsonl"
- config_name: ro-zh_cn
data_files: "data/ro-zh_cn.jsonl"
- config_name: ro-zh_tw
data_files: "data/ro-zh_tw.jsonl"
- config_name: ru-si
data_files: "data/ru-si.jsonl"
- config_name: ru-sk
data_files: "data/ru-sk.jsonl"
- config_name: ru-sl
data_files: "data/ru-sl.jsonl"
- config_name: ru-sq
data_files: "data/ru-sq.jsonl"
- config_name: ru-sr
data_files: "data/ru-sr.jsonl"
- config_name: ru-sv
data_files: "data/ru-sv.jsonl"
- config_name: ru-ta
data_files: "data/ru-ta.jsonl"
- config_name: ru-te
data_files: "data/ru-te.jsonl"
- config_name: ru-th
data_files: "data/ru-th.jsonl"
- config_name: ru-tl
data_files: "data/ru-tl.jsonl"
- config_name: ru-tr
data_files: "data/ru-tr.jsonl"
- config_name: ru-uk
data_files: "data/ru-uk.jsonl"
- config_name: ru-ur
data_files: "data/ru-ur.jsonl"
- config_name: ru-vi
data_files: "data/ru-vi.jsonl"
- config_name: ru-ze_en
data_files: "data/ru-ze_en.jsonl"
- config_name: ru-ze_zh
data_files: "data/ru-ze_zh.jsonl"
- config_name: ru-zh_cn
data_files: "data/ru-zh_cn.jsonl"
- config_name: ru-zh_tw
data_files: "data/ru-zh_tw.jsonl"
- config_name: si-sk
data_files: "data/si-sk.jsonl"
- config_name: si-sl
data_files: "data/si-sl.jsonl"
- config_name: si-sq
data_files: "data/si-sq.jsonl"
- config_name: si-sr
data_files: "data/si-sr.jsonl"
- config_name: si-sv
data_files: "data/si-sv.jsonl"
- config_name: si-ta
data_files: "data/si-ta.jsonl"
- config_name: si-te
data_files: "data/si-te.jsonl"
- config_name: si-th
data_files: "data/si-th.jsonl"
- config_name: si-tl
data_files: "data/si-tl.jsonl"
- config_name: si-tr
data_files: "data/si-tr.jsonl"
- config_name: si-uk
data_files: "data/si-uk.jsonl"
- config_name: si-ur
data_files: "data/si-ur.jsonl"
- config_name: si-vi
data_files: "data/si-vi.jsonl"
- config_name: si-ze_en
data_files: "data/si-ze_en.jsonl"
- config_name: si-ze_zh
data_files: "data/si-ze_zh.jsonl"
- config_name: si-zh_cn
data_files: "data/si-zh_cn.jsonl"
- config_name: si-zh_tw
data_files: "data/si-zh_tw.jsonl"
- config_name: sk-sl
data_files: "data/sk-sl.jsonl"
- config_name: sk-sq
data_files: "data/sk-sq.jsonl"
- config_name: sk-sr
data_files: "data/sk-sr.jsonl"
- config_name: sk-sv
data_files: "data/sk-sv.jsonl"
- config_name: sk-ta
data_files: "data/sk-ta.jsonl"
- config_name: sk-te
data_files: "data/sk-te.jsonl"
- config_name: sk-th
data_files: "data/sk-th.jsonl"
- config_name: sk-tl
data_files: "data/sk-tl.jsonl"
- config_name: sk-tr
data_files: "data/sk-tr.jsonl"
- config_name: sk-uk
data_files: "data/sk-uk.jsonl"
- config_name: sk-ur
data_files: "data/sk-ur.jsonl"
- config_name: sk-vi
data_files: "data/sk-vi.jsonl"
- config_name: sk-ze_en
data_files: "data/sk-ze_en.jsonl"
- config_name: sk-ze_zh
data_files: "data/sk-ze_zh.jsonl"
- config_name: sk-zh_cn
data_files: "data/sk-zh_cn.jsonl"
- config_name: sk-zh_tw
data_files: "data/sk-zh_tw.jsonl"
- config_name: sl-sq
data_files: "data/sl-sq.jsonl"
- config_name: sl-sr
data_files: "data/sl-sr.jsonl"
- config_name: sl-sv
data_files: "data/sl-sv.jsonl"
- config_name: sl-ta
data_files: "data/sl-ta.jsonl"
- config_name: sl-te
data_files: "data/sl-te.jsonl"
- config_name: sl-th
data_files: "data/sl-th.jsonl"
- config_name: sl-tl
data_files: "data/sl-tl.jsonl"
- config_name: sl-tr
data_files: "data/sl-tr.jsonl"
- config_name: sl-uk
data_files: "data/sl-uk.jsonl"
- config_name: sl-ur
data_files: "data/sl-ur.jsonl"
- config_name: sl-vi
data_files: "data/sl-vi.jsonl"
- config_name: sl-ze_en
data_files: "data/sl-ze_en.jsonl"
- config_name: sl-ze_zh
data_files: "data/sl-ze_zh.jsonl"
- config_name: sl-zh_cn
data_files: "data/sl-zh_cn.jsonl"
- config_name: sl-zh_tw
data_files: "data/sl-zh_tw.jsonl"
- config_name: sq-sr
data_files: "data/sq-sr.jsonl"
- config_name: sq-sv
data_files: "data/sq-sv.jsonl"
- config_name: sq-ta
data_files: "data/sq-ta.jsonl"
- config_name: sq-te
data_files: "data/sq-te.jsonl"
- config_name: sq-th
data_files: "data/sq-th.jsonl"
- config_name: sq-tl
data_files: "data/sq-tl.jsonl"
- config_name: sq-tr
data_files: "data/sq-tr.jsonl"
- config_name: sq-uk
data_files: "data/sq-uk.jsonl"
- config_name: sq-ur
data_files: "data/sq-ur.jsonl"
- config_name: sq-vi
data_files: "data/sq-vi.jsonl"
- config_name: sq-ze_en
data_files: "data/sq-ze_en.jsonl"
- config_name: sq-ze_zh
data_files: "data/sq-ze_zh.jsonl"
- config_name: sq-zh_cn
data_files: "data/sq-zh_cn.jsonl"
- config_name: sq-zh_tw
data_files: "data/sq-zh_tw.jsonl"
- config_name: sr-sv
data_files: "data/sr-sv.jsonl"
- config_name: sr-ta
data_files: "data/sr-ta.jsonl"
- config_name: sr-te
data_files: "data/sr-te.jsonl"
- config_name: sr-th
data_files: "data/sr-th.jsonl"
- config_name: sr-tl
data_files: "data/sr-tl.jsonl"
- config_name: sr-tr
data_files: "data/sr-tr.jsonl"
- config_name: sr-uk
data_files: "data/sr-uk.jsonl"
- config_name: sr-ur
data_files: "data/sr-ur.jsonl"
- config_name: sr-vi
data_files: "data/sr-vi.jsonl"
- config_name: sr-ze_en
data_files: "data/sr-ze_en.jsonl"
- config_name: sr-ze_zh
data_files: "data/sr-ze_zh.jsonl"
- config_name: sr-zh_cn
data_files: "data/sr-zh_cn.jsonl"
- config_name: sr-zh_tw
data_files: "data/sr-zh_tw.jsonl"
- config_name: sv-ta
data_files: "data/sv-ta.jsonl"
- config_name: sv-te
data_files: "data/sv-te.jsonl"
- config_name: sv-th
data_files: "data/sv-th.jsonl"
- config_name: sv-tl
data_files: "data/sv-tl.jsonl"
- config_name: sv-tr
data_files: "data/sv-tr.jsonl"
- config_name: sv-uk
data_files: "data/sv-uk.jsonl"
- config_name: sv-ur
data_files: "data/sv-ur.jsonl"
- config_name: sv-vi
data_files: "data/sv-vi.jsonl"
- config_name: sv-ze_en
data_files: "data/sv-ze_en.jsonl"
- config_name: sv-ze_zh
data_files: "data/sv-ze_zh.jsonl"
- config_name: sv-zh_cn
data_files: "data/sv-zh_cn.jsonl"
- config_name: sv-zh_tw
data_files: "data/sv-zh_tw.jsonl"
- config_name: ta-te
data_files: "data/ta-te.jsonl"
- config_name: ta-th
data_files: "data/ta-th.jsonl"
- config_name: ta-tr
data_files: "data/ta-tr.jsonl"
- config_name: ta-vi
data_files: "data/ta-vi.jsonl"
- config_name: ta-ze_en
data_files: "data/ta-ze_en.jsonl"
- config_name: ta-ze_zh
data_files: "data/ta-ze_zh.jsonl"
- config_name: ta-zh_cn
data_files: "data/ta-zh_cn.jsonl"
- config_name: ta-zh_tw
data_files: "data/ta-zh_tw.jsonl"
- config_name: te-th
data_files: "data/te-th.jsonl"
- config_name: te-tr
data_files: "data/te-tr.jsonl"
- config_name: te-vi
data_files: "data/te-vi.jsonl"
- config_name: te-ze_en
data_files: "data/te-ze_en.jsonl"
- config_name: te-zh_cn
data_files: "data/te-zh_cn.jsonl"
- config_name: te-zh_tw
data_files: "data/te-zh_tw.jsonl"
- config_name: th-tl
data_files: "data/th-tl.jsonl"
- config_name: th-tr
data_files: "data/th-tr.jsonl"
- config_name: th-uk
data_files: "data/th-uk.jsonl"
- config_name: th-ur
data_files: "data/th-ur.jsonl"
- config_name: th-vi
data_files: "data/th-vi.jsonl"
- config_name: th-ze_en
data_files: "data/th-ze_en.jsonl"
- config_name: th-ze_zh
data_files: "data/th-ze_zh.jsonl"
- config_name: th-zh_cn
data_files: "data/th-zh_cn.jsonl"
- config_name: th-zh_tw
data_files: "data/th-zh_tw.jsonl"
- config_name: tl-tr
data_files: "data/tl-tr.jsonl"
- config_name: tl-uk
data_files: "data/tl-uk.jsonl"
- config_name: tl-vi
data_files: "data/tl-vi.jsonl"
- config_name: tl-zh_cn
data_files: "data/tl-zh_cn.jsonl"
- config_name: tl-zh_tw
data_files: "data/tl-zh_tw.jsonl"
- config_name: tr-uk
data_files: "data/tr-uk.jsonl"
- config_name: tr-ur
data_files: "data/tr-ur.jsonl"
- config_name: tr-vi
data_files: "data/tr-vi.jsonl"
- config_name: tr-ze_en
data_files: "data/tr-ze_en.jsonl"
- config_name: tr-ze_zh
data_files: "data/tr-ze_zh.jsonl"
- config_name: tr-zh_cn
data_files: "data/tr-zh_cn.jsonl"
- config_name: tr-zh_tw
data_files: "data/tr-zh_tw.jsonl"
- config_name: uk-ur
data_files: "data/uk-ur.jsonl"
- config_name: uk-vi
data_files: "data/uk-vi.jsonl"
- config_name: uk-ze_en
data_files: "data/uk-ze_en.jsonl"
- config_name: uk-ze_zh
data_files: "data/uk-ze_zh.jsonl"
- config_name: uk-zh_cn
data_files: "data/uk-zh_cn.jsonl"
- config_name: uk-zh_tw
data_files: "data/uk-zh_tw.jsonl"
- config_name: ur-vi
data_files: "data/ur-vi.jsonl"
- config_name: ur-zh_cn
data_files: "data/ur-zh_cn.jsonl"
- config_name: ur-zh_tw
data_files: "data/ur-zh_tw.jsonl"
- config_name: vi-ze_en
data_files: "data/vi-ze_en.jsonl"
- config_name: vi-ze_zh
data_files: "data/vi-ze_zh.jsonl"
- config_name: vi-zh_cn
data_files: "data/vi-zh_cn.jsonl"
- config_name: vi-zh_tw
data_files: "data/vi-zh_tw.jsonl"
- config_name: pt_br-ro
data_files: "data/pt_br-ro.jsonl"
- config_name: pt_br-ru
data_files: "data/pt_br-ru.jsonl"
- config_name: pt_br-si
data_files: "data/pt_br-si.jsonl"
- config_name: pt_br-sk
data_files: "data/pt_br-sk.jsonl"
- config_name: pt_br-sl
data_files: "data/pt_br-sl.jsonl"
- config_name: pt_br-sq
data_files: "data/pt_br-sq.jsonl"
- config_name: pt_br-sr
data_files: "data/pt_br-sr.jsonl"
- config_name: pt_br-sv
data_files: "data/pt_br-sv.jsonl"
- config_name: pt_br-ta
data_files: "data/pt_br-ta.jsonl"
- config_name: pt_br-te
data_files: "data/pt_br-te.jsonl"
- config_name: pt_br-th
data_files: "data/pt_br-th.jsonl"
- config_name: pt_br-tl
data_files: "data/pt_br-tl.jsonl"
- config_name: pt_br-tr
data_files: "data/pt_br-tr.jsonl"
- config_name: pt_br-uk
data_files: "data/pt_br-uk.jsonl"
- config_name: pt_br-ur
data_files: "data/pt_br-ur.jsonl"
- config_name: pt_br-vi
data_files: "data/pt_br-vi.jsonl"
- config_name: pt_br-ze_en
data_files: "data/pt_br-ze_en.jsonl"
- config_name: pt_br-ze_zh
data_files: "data/pt_br-ze_zh.jsonl"
- config_name: pt_br-zh_cn
data_files: "data/pt_br-zh_cn.jsonl"
- config_name: pt_br-zh_tw
data_files: "data/pt_br-zh_tw.jsonl"
- config_name: ze_en-ze_zh
data_files: "data/ze_en-ze_zh.jsonl"
- config_name: ze_en-zh_cn
data_files: "data/ze_en-zh_cn.jsonl"
- config_name: ze_en-zh_tw
data_files: "data/ze_en-zh_tw.jsonl"
- config_name: ze_zh-zh_cn
data_files: "data/ze_zh-zh_cn.jsonl"
- config_name: ze_zh-zh_tw
data_files: "data/ze_zh-zh_tw.jsonl"
- config_name: zh_cn-zh_tw
data_files: "data/zh_cn-zh_tw.jsonl"
---
|
ruslanmv/icliniq-7k | ---
configs:
- config_name: default
dataset_info:
features:
- name: input
dtype: string
- name: answer_icliniq
dtype: string
- name: answer_chatgpt
dtype: string
- name: answer_chatdoctor
dtype: string
splits:
- name: train
num_bytes: 16962106
num_examples: 7321
download_size: 9373080
dataset_size: 16962106
---
# Dataset Card for "ChatDoctor-iCliniq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jagostoned/Ashley | ---
license: apache-2.0
---
|
zhangshuoming/c_x86_O0_anghabench_augment1_json_cleaned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3551555713.8653345
num_examples: 2406026
download_size: 834668509
dataset_size: 3551555713.8653345
---
# Dataset Card for "c_x86_O0_anghabench_augment1_json_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
InstaDeepAI/human_reference_genome | ---
tags:
- DNA
- Genomics
- Nucleotide
pretty_name: Human Reference Genome
---
# Dataset Card for the human reference genome
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1)
### Dataset Summary
The Human reference genome dataset was constructed by considering all autosomal and sex chromosomes sequences from reference assembly [GRCh38/hg38](https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26) and reaches a total of 3.2 billion nucleotides.
### Supported Tasks and Leaderboards
This dataset has been used as a pre-training corpus for the Nucleotide Transformers models. Depending on the configuration used, each sequence is 6,200 or 12,200 base pase pairs long. If the dataset is iterated without being shuffled, the first 100 nucleotides of a sequence are the same as the last 100 base pairs of the previous sequence, and the last 100 nucleotides are the same as the first 100 base pairs of the next sequence. During training, this allows for randomly selecting a nucleotide between the first 200 nucleotides of the sequence and start the tokenization from this nucleotide. That way, all the chromosome is covered and the model sees different tokens for a given sequence at each epoch.
### Languages
DNA
## Dataset Structure
[N/A]
### Data Instances
For each instance, there is a string representing the sequence, a string indicating the chromosome, and two integers representing the index of the first and last nucleotide respectively. An instance is shown below:
```python
{'sequence': 'CATCTGCAGGTGTCTGACTTCCAGCAACTGCTGGCCTGTGCCAGGGTGCAAGCTGAGCACTGGAGTGGAGTTTTCCTGTGGAGAGGAGCCATGCCTAGAGTGGGATGGGCCATTGTTCATCTTCTGGCCCCTGTTGTCTGCATGTAACTTAATACCACAACCAGGCATAGGGGAAAGATTGGAGGAAAGATGAGTGAGAGCATCAACTTCTCTCACAACCTAGGCCAGTAAGTAGTGCTTGTGCTCATCTCCTTGGCTGTGATACGTGGCCGGCCCTCGCTCCAGCAGCTGGACCCCTACCTGCCGTCTGCTGCCATCGGAGCCCAAAGCCGGGCTGTGACTGCTCAGACCAGCCGGCTGGAGGGAGGGGCTCAGCAGGTCTGGCTTTGGCCCTGGGAGAGCAGGTGGAAGATCAGGCAGGCCATCGCTGCCACAGAACCCAGTGGATTGGCCTAGGTGGGATCTCTGAGCTCAACAAGCCCTCTCTGGGTGGTAGGTGCAGAGACGGGAGGGGCAGAGCCGCAGGCACAGCCAAGAGGGCTGAAGAAATGGTAGAACGGAGCAGCTGGTGATGTGTGGGCCCACCGGCCCCAGGCTCCTGTCTCCCCCCAGGTGTGTGGTGATGCCAGGCATGCCCTTCCCCAGCATCAGGTCTCCAGAGCTGCAGAAGACGACGGCCGACTTGGATCACACTCTTGTGAGTGTCCCCAGTGTTGCAGAGGTGAGAGGAGAGTAGACAGTGAGTGGGAGTGGCGTCGCCCCTAGGGCTCTACGGGGCCGGCGTCTCCTGTCTCCTGGAGAGGCTTCGATGCCCCTCCACACCCTCTTGATCTTCCCTGTGATGTCATCTGGAGCCCTGCTGCTTGCGGTGGCCTATAAAGCCTCCTAGTCTGGCTCCAAGGCCTGGCAGAGTCTTTCCCAGGGAAAGCTACAAGCAGCAAACAGTCTGCATGGGTCATCCCCTTCACTCCCAGCTCAGAGCCCAGGCCAGGGGCCCCCAAGAAAGGCTCTGGTGGAGAACCTGTGCATGAAGGCTGTCAACCAGTCCATAGGCAAGCCTGGCTGCCTCCAGCTGGGTCGACAGACAGGGGCTGGAGAAGGGGAGAAGAGGAAAGTGAGGTTGCCTGCCCTGTCTCCTACCTGAGGCTGAGGAAGGAGAAGGGGATGCACTGTTGGGGAGGCAGCTGTAACTCAAAGCCTTAGCCTCTGTTCCCACGAAGGCAGGGCCATCAGGCACCAAAGGGATTCTGCCAGCATAGTGCTCCTGGACCAGTGATACACCCGGCACCCTGTCCTGGACACGCTGTTGGCCTGGATCTGAGCCCTGGTGGAGGTCAAAGCCACCTTTGGTTCTGCCATTGCTGCTGTGTGGAAGTTCACTCCTGCCTTTTCCTTTCCCTAGAGCCTCCACCACCCCGAGATCACATTTCTCACTGCCTTTTGTCTGCCCAGTTTCACCAGAAGTAGGCCTCTTCCTGACAGGCAGCTGCACCACTGCCTGGCGCTGTGCCCTTCCTTTGCTCTGCCCGCTGGAGACGGTGTTTGTCATGGGCCTGGTCTGCAGGGATCCTGCTACAAAGGTGAAACCCAGGAGAGTGTGGAGTCCAGAGTGTTGCCAGGACCCAGGCACAGGCATTAGTGCCCGTTGGAGAAAACAGGGGAATCCCGAAGAAATGGTGGGTCCTGGCCATCCGTGAGATCTTCCCAGGGCAGCTCCCCTCTGTGGAATCCAATCTGTCTTCCATCCTGCGTGGCCGAGGGCCAGGCTTCTCACTGGGCCTCTGCAGGAGGCTGCCATTTGTCCTGCCCACCTTCTTAGAAGCGAGACGGAGCAGACCCATCTGCTACTGCCCTTTCTATAATAACTAAAGTTAGCTGCCCTGGACTATTCACCCCCTAGTCTCAATTTAAGAAGATCCCCATGGCCACAGGGCCCCTGCCTGGGGGCTTGTCACCTCCCCCACCTTCTTCCTGAGTCATTCCTGCAGCCTTGCTCCCTAACCTGCCCCACAGCCTTGCCTGGATTTCTATCTCCCTGGCTTGGTGCCAGTTCCTCCAAGTCGATGGCACCTCCCTCCCTCTCAACCACTTGAGCAAACTCCAAGACATCTTCTACCCCAACACCAGCAATTGTGCCAAGGGCCATTAGGCTCTCAGCATGACTATTTTTAGAGACCCCGTGTCTGTCACTGAAACCTTTTTTGTGGGAGACTATTCCTCCCATCTGCAACAGCTGCCCCTGCTGACTGCCCTTCTCTCCTCCCTCTCATCCCAGAGAAACAGGTCAGCTGGGAGCTTCTGCCCCCACTGCCTAGGGACCAACAGGGGCAGGAGGCAGTCACTGACCCCGAGACGTTTGCATCCTGCACAGCTAGAGATCCTTTATTAAAAGCACACTGTTGGTTTCTGCTCAGTTCTTTATTGATTGGTGTGCCGTTTTCTCTGGAAGCCTCTTAAGAACACAGTGGCGCAGGCTGGGTGGAGCCGTCCCCCCATGGAGCACAGGCAGACAGAAGTCCCCGCCCCAGCTGTGTGGCCTCAAGCCAGCCTTCCGCTCCTTGAAGCTGGTCTCCACACAGTGCTGGTTCCGTCACCCCCTCCCAAGGAAGTAGGTCTGAGCAGCTTGTCCTGGCTGTGTCCATGTCAGAGCAACGGCCCAAGTCTGGGTCTGGGGGGGAAGGTGTCATGGAGCCCCCTACGATTCCCAGTCGTCCTCGTCCTCCTCTGCCTGTGGCTGCTGCGGTGGCGGCAGAGGAGGGATGGAGTCTGACACGCGGGCAAAGGCTCCTCCGGGCCCCTCACCAGCCCCAGGTCCTTTCCCAGAGATGCCTGGAGGGAAAAGGCTGAGTGAGGGTGGTTGGTGGGAAACCCTGGTTCCCCCAGCCCCCGGAGACTTAAATACAGGAAGAAAAAGGCAGGACAGAATTACAAGGTGCTGGCCCAGGGCGGGCAGCGGCCCTGCCTCCTACCCTTGCGCCTCATGACCAGCTTGTTGAAGAGATCCGACATCAAGTGCCCACCTTGGCTCGTGGCTCTCACTGCAACGGGAAAGCCACAGACTGGGGTGAAGAGTTCAGTCACATGCGACCGGTGACTCCCTGTCCCCACCCCCATGACACTCCCCAGCCCTCCAAGGCCACTGTGTTTCCCAGTTAGCTCAGAGCCTCAGTCGATCCCTGACCCAGCACCGGGCACTGATGAGACAGCGGCTGTTTGAGGAGCCACCTCCCAGCCACCTCGGGGCCAGGGCCAGGGTGTGCAGCACCACTGTACAATGGGGAAACTGGCCCAGAGAGGTGAGGCAGCTTGCCTGGGGTCACAGAGCAAGGCAAAAGCAGCGCTGGGTACAAGCTCAAAACCATAGTGCCCAGGGCACTGCCGCTGCAGGCGCAGGCATCGCATCACACCAGTGTCTGCGTTCACAGCAGGCATCATCAGTAGCCTCCAGAGGCCTCAGGTCCAGTCTCTAAAAATATCTCAGGAGGCTGCAGTGGCTGACCATTGCCTTGGACCGCTCTTGGCAGTCGAAGAAGATTCTCCTGTCAGTTTGAGCTGGGTGAGCTTAGAGAGGAAAGCTCCACTATGGCTCCCAAACCAGGAAGGAGCCATAGCCCAGGCAGGAGGGCTGAGGACCTCTGGTGGCGGCCCAGGGCTTCCAGCATGTGCCCTAGGGGAAGCAGGGGCCAGCTGGCAAGAGCAGGGGGTGGGCAGAAAGCACCCGGTGGACTCAGGGCTGGAGGGGAGGAGGCGATCTTGCCCAAGGCCCTCCGACTGCAAGCTCCAGGGCCCGCTCACCTTGCTCCTGCTCCTTCTGCTGCTGCTTCTCCAGCTTTCGCTCCTTCATGCTGCGCAGCTTGGCCTTGCCGATGCCCCCAGCTTGGCGGATGGACTCTAGCAGAGTGGCCAGCCACCGGAGGGGTCAACCACTTCCCTGGGAGCTCCCTGGACTGGAGCCGGGAGGTGGGGAACAGGGCAAGGAGGAAAGGCTGCTCAGGCAGGGCTGGGGAAGCTTACTGTGTCCAAGAGCCTGCTGGGAGGGAAGTCACCTCCCCTCAAACGAGGAGCCCTGCGCTGGGGAGGCCGGACCTTTGGAGACTGTGTGTGGGGGCCTGGGCACTGACTTCTGCAACCACCTGAGCGCGGGCATCCTGTGTGCAGATACTCCCTGCTTCCTCTCTAGCCCCCACCCTGCAGAGCTGGACCCCTGAGCTAGCCATGCTCTGACAGTCTCAGTTGCACACACGAGCCAGCAGAGGGGTTTTGTGCCACTTCTGGATGCTAGGGTTACACTGGGAGACACAGCAGTGAAGCTGAAATGAAAAATGTGTTGCTGTAGTTTGTTATTAGACCCCTTCTTTCCATTGGTTTAATTAGGAATGGGGAACCCAGAGCCTCACTTGTTCAGGCTCCCTCTGCCCTAGAAGTGAGAAGTCCAGAGCTCTACAGTTTGAAAACCACTATTTTATGAACCAAGTAGAACAAGATATTTGAAATGGAAACTATTCAAAAAATTGAGAATTTCTGACCACTTAACAAACCCACAGAAAATCCACCCGAGTGCACTGAGCACGCCAGAAATCAGGTGGCCTCAAAGAGCTGCTCCCACCTGAAGGAGACGCGCTGCTGCTGCTGTCGTCCTGCCTGGCGCCTTGGCCTACAGGGGCCGCGGTTGAGGGTGGGAGTGGGGGTGCACTGGCCAGCACCTCAGGAGCTGGGGGTGGTGGTGGGGGCGGTGGGGGTGGTGTTAGTACCCCATCTTGTAGGTCTGAAACACAAAGTGTGGGGTGTCTAGGGAAGAAGGTGTGTGACCAGGGAGGTCCCCGGCCCAGCTCCCATCCCAGAACCCAGCTCACCTACCTTGAGAGGCTCGGCTACCTCAGTGTGGAAGGTGGGCAGTTCTGGAATGGTGCCAGGGGCAGAGGGGGCAATGCCGGGGCCCAGGTCGGCAATGTACATGAGGTCGTTGGCAATGCCGGGCAGGTCAGGCAGGTAGGATGGAACATCAATCTCAGGCACCTGGCCCAGGTCTGGCACATAGAAGTAGTTCTCTGGGACCTGCAAGATTAGGCAGGGACATGTGAGAGGTGACAGGGACCTGCAGGGGCAGCCAACAAGACCTTGTGTGCACCTCCCATGGGTGGAATAAGGGGCCCAACAGCCTTGACTGGAGAGGAGCTCTGGCAAGGCCCTGGGCCACTGCACCTGTCTCCACCTCTGTCCCACCCCTCCCACCTGCTGTTCCAGCTGCTCTCTCTTGCTGATGGACAAGGGGGCATCAAACAGCTTCTCCTCTGTCTCTGCCCCCAGCATCACATGGGTCTTTGTTACAGCACCAGCCAGGGGGTCCAGGAAGACATACTTCTTCTACCTACAGAGGCGACATGGGGGTCAGGCAAGCTGACACCCGCTGTCCTGAGCCCATGTTCCTCTCCCACATCATCAGGGGCACAGCGTGCACTGTGGGGTCCCAGGCCTCCCGAGCCGAGCCACCCGTCACCCCCTGGCTCCTGGCCTATGTGCTGTACCTGTGTCTGATGCCCTGGGTCCCCACTAAGCCAGGCCGGGCCTCCCGCCCACACCCCTCGGCCCTGCCCTCTGGCCATACAGGTTCTCGGTGGTGTTGAAGAGCAGCAAGGAGCTGACAGAGCTGATGTTGCTGGGAAGACCCCCAAGTCCCTCTTCTGCATCGTCCTCGGGCTCCGGCTTGGTGCTCACGCACACAGGAAAGTCCTTCAGCTTCTCCTGAGAGGGCCAGGATGGCCAAGGGATGGTGAATATTTGGTGCTGGGCCTAATCAGCTGCCATCCCATCCCAGTCAGCCTCCTCTGGGGGACAGAACCCTATGGTGGCCCCGGCTCCTCCCCAGTATCCAGTCCTCCTGGTGTGTGACAGGCTATATGCGCGGCCAGCAGACCTGCAGGGCCCGCTCGTCCAGGGGGCGGTGCTTGCTCTGGATCCTGTGGCGGGGGCGTCTCTGCAGGCCAGGGTCCTGGGCGCCCGTGAAGATGGAGCCATATTCCTGCAGGCGCCCTGGAGCAGGGTACTTGGCACTGGAGAACACCTGTGGACACAGGGACAAGTCTGAGGGGGCCCCAAGAGGCTCAGAGGGCTAGGATTGCTTGGCAGGAGAGGGTGGAGTTGGAAGCCTGGGCGAGAAGAAAGCTCAAGGTACAGGTGGGCAGCAGGGCAGAGACTGGGCA',
'chromosome': '1',
'start_pos': 12000,
'end_pos': 18200}
```
### Data Fields
- `sequence`: a string containing a DNA sequence from the human reference genome
- `chromosome`: a string indicating the chromosome (1,2,...,21,X,Y)
- `start_pos`: an integer indicating the index of the sequence's first nucleotide
- `end_pos`: an integer indicating the index of the sequence's last nucleotide
### Data Splits
The Human reference genome dataset has 3 splits: train, validation, and test. Below are the statistics for the dataset.
```
| Dataset Split | Number of Instances in Split (6kb) | Number of Instances in Split (12kb) |
| ------------- | ------------------------------------------- | -------------------------------------------------------------- |
| Train | 498,444 | 249,222 |
| Validation | 7,784 | 3,892 |
| Test | 8,469 | 4,234 |
```
## Dataset Creation
[N/A]
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
The data consists of sequences cut from the chromosomes found in the [GRCh38/hg38](https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26) human reference genome.
#### Who are the source language producers?
[N/A]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
[N/A]
### Licensing Information
[N/A]
### Citation Information
```bibtex
@article{dalla2023nucleotide,
title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza Revilla, Javier and Lopez Carranza, Nicolas and Henryk Grywaczewski, Adam and Oteri, Francesco and Dallago, Christian and Trop, Evan and Sirelkhatim, Hassan and Richard, Guillaume and others},
journal={bioRxiv},
pages={2023--01},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
``` |
alisson40889/crix | ---
license: openrail
---
|
distilled-from-one-sec-cv12/chunk_82 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1366097344
num_examples: 266192
download_size: 1396040971
dataset_size: 1366097344
---
# Dataset Card for "chunk_82"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JuanKO/sft_dataset_rlaif | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 9271
num_examples: 5
download_size: 19196
dataset_size: 9271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TheGreatP/vampirodoidaoV1 | ---
license: openrail
---
|
MartinKu/whalley_dataset_ver2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: TEXT
dtype: string
splits:
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num_bytes: 1426305
num_examples: 2200
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num_bytes: 1426305
num_examples: 2200
- name: test
num_bytes: 1426305
num_examples: 2200
download_size: 1887594
dataset_size: 4278915
---
# Dataset Card for "whalley_dataset_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alisson40889/chelechele | ---
license: openrail
---
|
autoevaluate/autoeval-eval-MicPie__QA_bias-v2_TEST-MicPie__QA_bias-v2_TEST-e54ae6-1669159074 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- MicPie/QA_bias-v2_TEST
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-350m_eval
metrics: []
dataset_name: MicPie/QA_bias-v2_TEST
dataset_config: MicPie--QA_bias-v2_TEST
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-350m_eval
* Dataset: MicPie/QA_bias-v2_TEST
* Config: MicPie--QA_bias-v2_TEST
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. |
nthakur/miracl-raft-instruct | ---
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splits:
- name: train
num_bytes: 1114154
num_examples: 616
download_size: 441880
dataset_size: 1114154
- config_name: te
features:
- name: output
list:
- name: model
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
- name: query_id
dtype: string
- name: doc_ids
sequence: string
- name: positive_ids
sequence: string
- name: negative_ids
sequence: 'null'
splits:
- name: train
num_bytes: 4083245
num_examples: 1003
download_size: 1294119
dataset_size: 4083245
- config_name: th
features:
- name: output
list:
- name: model
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
- name: query_id
dtype: string
- name: doc_ids
sequence: string
- name: positive_ids
sequence: string
- name: negative_ids
sequence: 'null'
splits:
- name: train
num_bytes: 11672646
num_examples: 2556
download_size: 4007556
dataset_size: 11672646
- config_name: zh
features:
- name: output
list:
- name: model
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
- name: query_id
dtype: string
- name: doc_ids
sequence: string
- name: positive_ids
sequence: string
- name: negative_ids
sequence: 'null'
splits:
- name: train
num_bytes: 2469288
num_examples: 1029
download_size: 1362216
dataset_size: 2469288
configs:
- config_name: ar
data_files:
- split: train
path: ar/train-*
- config_name: bn
data_files:
- split: train
path: bn/train-*
- config_name: en
data_files:
- split: train
path: en/train-*
- config_name: es
data_files:
- split: train
path: es/train-*
- config_name: fa
data_files:
- split: train
path: fa/train-*
- config_name: fi
data_files:
- split: train
path: fi/train-*
- config_name: fr
data_files:
- split: train
path: fr/train-*
- config_name: hi
data_files:
- split: train
path: hi/train-*
- config_name: id
data_files:
- split: train
path: id/train-*
- config_name: ja
data_files:
- split: train
path: ja/train-*
- config_name: ko
data_files:
- split: train
path: ko/train-*
- config_name: ru
data_files:
- split: train
path: ru/train-*
- config_name: sw
data_files:
- split: train
path: sw/train-*
- config_name: te
data_files:
- split: train
path: te/train-*
- config_name: th
data_files:
- split: train
path: th/train-*
- config_name: zh
data_files:
- split: train
path: zh/train-*
---
# Dataset Card for "miracl-raft-instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
malhajar/arc-tr | ---
license: mit
task_categories:
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
language:
- tr
size_categories:
- 10K<n<100K
paperswithcode_id: arc
pretty_name: arc
annotations_creators:
- found
language_creators:
- found
dataset_info:
- config_name: ARC-Challenge
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 374640
num_examples: 1118
- name: test
num_bytes: 402938
num_examples: 1171
- name: validation
num_bytes: 103674
num_examples: 298
- config_name: ARC-Easy
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 663076
num_examples: 2250
- name: test
num_bytes: 702861
num_examples: 2375
- name: validation
num_bytes: 168076
num_examples: 569
configs:
- config_name: ARC-Challenge
data_files:
- split: train
path: ARC-Challenge/train-*
- split: test
path: ARC-Challenge/test-*
- split: validation
path: ARC-Challenge/validation-*
- config_name: ARC-Easy
data_files:
- split: train
path: ARC-Easy/train-*
- split: test
path: ARC-Easy/test-*
- split: validation
path: ARC-Easy/validation-*
---
This Dataset is part of a series of datasets aimed at advancing Turkish LLM Developments by establishing rigid Turkish benchmarks to evaluate the performance of LLM's Produced in the Turkish Language.
# Dataset Card for arc-tr
malhajar/arc-tr is a translated version of [`arc`]( https://huggingface.co/datasets/allenai/ai2_arc) aimed specifically to be used in the [`OpenLLMTurkishLeaderboard`](https://huggingface.co/spaces/malhajar/OpenLLMTurkishLeaderboard)
This Dataset contains rigid tests extracted from the paper [`Think you have Solved Question Answering? `](https://arxiv.org/abs/1803.05457)
**Developed by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
### Data Instances
#### ARC-Challenge
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 0.83 MB
- **Total amount of disk used:** 681.67 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Buzdolabının kapısı pürüzsüz.", "Buzdolabının kapısı demir içerir.", "Buzdolabı kapısı iyi bir iletkendir.", "Buzdolabının kapısında elektrik kabloları vardır."]
},
"id": "MCAS_2009_5_6516",
"question": "Aşağıdaki ifadelerden hangisi mıknatısların neden genellikle buzdolabı kapısına yapıştığını en iyi şekilde açıklar?"
}
```
#### ARC-Easy
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 1.45 MB
- **Total amount of disk used:** 682.29 MB
An example of 'train' looks as follows.
```
{
"answerKey": "A",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["kutup sularında yüzmek", "çok miktarda balık yemek", "diğer hayvanlar tarafından avlanmak", "yüksek sıcaklığa sahip bir ortamda yaşamak"]
},
"id": "Mercury_7188563",
"question": "Belirli bir organizma, derisinin altındaki kalın yağ tabakası sayesinde bir ortamda hayatta kalabilir. Yağ tabakası hangi durumda hayatta kalma avantajına sahip olabilir?"
}
```
### Data Fields
The data fields are the same among all splits.
#### ARC-Challenge
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
#### ARC-Easy
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------------|----:|---------:|---:|
|ARC-Challenge| 1119| 299|1172|
|ARC-Easy | 2251| 570|2376|
### Citation Information
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
```
### Dataset Description
- **Language(s) (NLP):** Turkish
- **Translated from:** [arc]( https://huggingface.co/datasets/allenai/ai2_arc)
- **Paper:** [Think you have Solved Question Answering?](https://arxiv.org/abs/1803.05457)
|
jarod0411/cancer_5120_6_14 | ---
dataset_info:
features:
- name: smiles
dtype: string
- name: scaffold_smiles
dtype: string
- name: selfies
dtype: string
- name: scaffold_selfies
dtype: string
- name: QED
dtype: float64
- name: DockingScore
dtype: float64
- name: sa
dtype: float64
- name: norm_sa
dtype: float64
- name: sol
dtype: float64
- name: norm_sol
dtype: float64
- name: qed
dtype: float64
- name: dock
dtype: float64
- name: norm_dock
dtype: float64
splits:
- name: train
num_bytes: 2530008
num_examples: 5120
download_size: 1127318
dataset_size: 2530008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tdh87/mixed50-50-with-kid-stories | ---
license: apache-2.0
---
|
CyberHarem/shiina_noriko_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shiina_noriko/椎名法子 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of shiina_noriko/椎名法子 (THE iDOLM@STER: Cinderella Girls), containing 168 images and their tags.
The core tags of this character are `brown_hair, ponytail, long_hair, hair_ornament, bangs, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 168 | 168.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_noriko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 168 | 114.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_noriko_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 356 | 226.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_noriko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 168 | 157.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_noriko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 356 | 300.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_noriko_idolmastercinderellagirls/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/shiina_noriko_idolmastercinderellagirls',
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, doughnut, open_mouth, smile, one_eye_closed, ;d, blush, necklace, bag, dress |
| 1 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, earrings, dress, hair_bow, wrist_cuffs, heart, smile, blush, frills, puffy_short_sleeves, apron, purple_eyes, open_mouth, doughnut, hairclip, one_eye_closed |
| 2 | 5 |  |  |  |  |  | 1girl, apron, maid_headdress, solo, wrist_cuffs, :d, doughnut, looking_at_viewer, navel, open_mouth, blush, detached_collar, frills, heart, midriff, necktie, skirt, bikini, bow, breasts, red_eyes, thighhighs, tray, white_background |
| 3 | 16 |  |  |  |  |  | 1girl, cat_ears, paw_gloves, solo, looking_at_viewer, blush, bow, cat_tail, jingle_bell, suspenders, twintails, midriff, navel, crop_top, open_mouth, earrings, frills, purple_eyes, simple_background, black_thighhighs, fang, tail_ornament, :d, black_shorts, cat_paws, ribbon, shirt, sitting, skirt, small_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | doughnut | open_mouth | smile | one_eye_closed | ;d | blush | necklace | bag | dress | looking_at_viewer | earrings | hair_bow | wrist_cuffs | heart | frills | puffy_short_sleeves | apron | purple_eyes | hairclip | maid_headdress | :d | navel | detached_collar | midriff | necktie | skirt | bikini | bow | breasts | red_eyes | thighhighs | tray | white_background | cat_ears | paw_gloves | cat_tail | jingle_bell | suspenders | twintails | crop_top | simple_background | black_thighhighs | fang | tail_ornament | black_shorts | cat_paws | ribbon | shirt | sitting | small_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:-------------|:--------|:-----------------|:-----|:--------|:-----------|:------|:--------|:--------------------|:-----------|:-----------|:--------------|:--------|:---------|:----------------------|:--------|:--------------|:-----------|:-----------------|:-----|:--------|:------------------|:----------|:----------|:--------|:---------|:------|:----------|:-----------|:-------------|:-------|:-------------------|:-----------|:-------------|:-----------|:--------------|:-------------|:------------|:-----------|:--------------------|:-------------------|:-------|:----------------|:---------------|:-----------|:---------|:--------|:----------|:----------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | | | | X | | | | X | | | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | 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 |
|
AdapterOcean/datasci-standardized_unified | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 4474152
num_examples: 1982
download_size: 2284059
dataset_size: 4474152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "datasci-standardized_unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/e3f69dd0 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 190
num_examples: 10
download_size: 1332
dataset_size: 190
---
# Dataset Card for "e3f69dd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_double_past | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 220357
num_examples: 1040
- name: dev_mismatched
num_bytes: 259960
num_examples: 1186
- name: test_matched
num_bytes: 208168
num_examples: 964
- name: test_mismatched
num_bytes: 271803
num_examples: 1222
- name: train
num_bytes: 8183144
num_examples: 38064
download_size: 5621570
dataset_size: 9143432
---
# Dataset Card for "MULTI_VALUE_mnli_double_past"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/python3-standardized_cluster_0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 60590056
num_examples: 5633
download_size: 0
dataset_size: 60590056
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Xinbad/squad_v2_sv_v2 | ---
license: apache-2.0
---
|
Nexdata/Thai_Speech_Data_by_Mobile_Phone_Guiding | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Thai_Speech_Data_by_Mobile_Phone_Guiding
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/70?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Thai speech data (guiding) is collected from 490 Thailand native speakers and is recorded in quiet environment. The recording is rich in content, covering multiple categories such as in-car scene, smart home, speech assistant. 50 sentences for each speaker. The valid volumn is 15 hours. All texts are manual transcribed with high accuray.
For more details, please refer to the link: https://www.nexdata.ai/datasets/70?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Thai
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
CyberHarem/lux_leagueoflegends | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of lux (League of Legends)
This is the dataset of lux (League of Legends), containing 105 images and their tags.
The core tags of this character are `pink_hair, magical_girl, twintails, purple_eyes, breasts, long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 105 | 126.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lux_leagueoflegends/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 105 | 83.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lux_leagueoflegends/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 223 | 156.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lux_leagueoflegends/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 105 | 114.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lux_leagueoflegends/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 223 | 199.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lux_leagueoflegends/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lux_leagueoflegends',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 105 |  |  |  |  |  | 1girl, star_guardian_(league_of_legends), alternate_costume, star_(symbol), elbow_gloves, solo, tiara, white_gloves, alternate_hairstyle, purple_choker, alternate_hair_color, skirt, thighhighs, smile, sailor_collar, looking_at_viewer, wand |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | star_guardian_(league_of_legends) | alternate_costume | star_(symbol) | elbow_gloves | solo | tiara | white_gloves | alternate_hairstyle | purple_choker | alternate_hair_color | skirt | thighhighs | smile | sailor_collar | looking_at_viewer | wand |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------------------------|:--------------------|:----------------|:---------------|:-------|:--------|:---------------|:----------------------|:----------------|:-----------------------|:--------|:-------------|:--------|:----------------|:--------------------|:-------|
| 0 | 105 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
neelalex/raft-predictions | ---
benchmark: raft
---
# Dummy predictions for RAFT |
hvvvque2/minhavoz233 | ---
license: openrail
---
|
jamestalentium/xsum_10_rm | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: output_text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 23485.327403268886
num_examples: 10
download_size: 19056
dataset_size: 23485.327403268886
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "xsum_10_rm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
carnival13/massive_5_lang_DA3_tokenized | ---
dataset_info:
features:
- name: pass_label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 419259395
num_examples: 552890
download_size: 127212717
dataset_size: 419259395
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "massive_5_lang_DA3_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HippoLite/PneumoniaHippo | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 3500970321.536
num_examples: 11712
download_size: 2465721553
dataset_size: 3500970321.536
---
# Dataset Card for "PneumoniaHippo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TeamSODA/mcl_signal_processing_attacks_whisper_commonvoice | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': 0-benign
'1': 1-kenan
'2': 2-yeehaw
'3': 3-imaginary_clipping
splits:
- name: train
num_bytes: 86186133.0
num_examples: 200
download_size: 84525602
dataset_size: 86186133.0
---
# Dataset Card for "mcl_signal_processing_attacks_whisper_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
GEM-submissions/lewtun__this-is-a-test-name__1655666361 | ---
benchmark: gem
type: prediction
submission_name: This is a test name
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test name
|
FanChen0116/19100_chat_50x_slot_pvi | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-time
'2': B-date
'3': B-last_name
'4': B-people
'5': I-date
'6': I-people
'7': I-last_name
'8': I-first_name
'9': B-first_name
'10': B-time
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 297191
num_examples: 1632
- name: validation
num_bytes: 5405
num_examples: 32
- name: test
num_bytes: 646729
num_examples: 3731
download_size: 49055
dataset_size: 949325
---
# Dataset Card for "19100_chat_50x_slot_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbitropy/bquac_raw | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: story
dtype: string
- name: questions
sequence: string
- name: source
dtype: string
- name: en_questions
sequence: string
- name: questions_scores
sequence: float64
- name: story_list_scores
sequence: float64
- name: story_score
dtype: float64
- name: id
dtype: int64
- name: en_story
dtype: string
- name: answers
sequence: string
- name: answers_scores
sequence: float64
- name: en_answer_spans
sequence: string
- name: en_answers
sequence: string
splits:
- name: train
num_bytes: 130408141
num_examples: 11567
- name: validation
num_bytes: 12370875
num_examples: 1000
download_size: 64073608
dataset_size: 142779016
---
Contains all the quac translated unfiltered conversations with scores |
kdtv/kk | ---
license: mit
---
|
Seongill/NQ_5_adversary_v3 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: num_advs
dtype: int64
splits:
- name: train
num_bytes: 14326455
num_examples: 3610
download_size: 7639062
dataset_size: 14326455
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BDARUI03/TrainingLLM | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_Toten5__Marcoroni-v3-neural-chat-v3-3-Slerp | ---
pretty_name: Evaluation run of Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp](https://huggingface.co/Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp)\
\ 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_Toten5__Marcoroni-v3-neural-chat-v3-3-Slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-11T22:21:10.174265](https://huggingface.co/datasets/open-llm-leaderboard/details_Toten5__Marcoroni-v3-neural-chat-v3-3-Slerp/blob/main/results_2023-12-11T22-21-10.174265.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\": 0.6497954997030103,\n\
\ \"acc_stderr\": 0.03218797050617161,\n \"acc_norm\": 0.6495568440119162,\n\
\ \"acc_norm_stderr\": 0.032855200604616566,\n \"mc1\": 0.47613219094247244,\n\
\ \"mc1_stderr\": 0.017483547156961574,\n \"mc2\": 0.6270127709181503,\n\
\ \"mc2_stderr\": 0.015065515223932825\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6621160409556314,\n \"acc_stderr\": 0.013822047922283512,\n\
\ \"acc_norm\": 0.6877133105802048,\n \"acc_norm_stderr\": 0.013542598541688067\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6793467436765585,\n\
\ \"acc_stderr\": 0.004657738398900938,\n \"acc_norm\": 0.8654650468034256,\n\
\ \"acc_norm_stderr\": 0.003405288007233203\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.028049186315695248,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.028049186315695248\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\
\ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\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\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.025467149045469557,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.025467149045469557\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\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\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\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229872,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229872\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8458715596330275,\n\
\ \"acc_stderr\": 0.015480826865374307,\n \"acc_norm\": 0.8458715596330275,\n\
\ \"acc_norm_stderr\": 0.015480826865374307\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n\
\ \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.02093019318517933\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\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371802\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\": 0.42681564245810055,\n\
\ \"acc_stderr\": 0.016542401954631917,\n \"acc_norm\": 0.42681564245810055,\n\
\ \"acc_norm_stderr\": 0.016542401954631917\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967287,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967287\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45371577574967403,\n\
\ \"acc_stderr\": 0.012715404841277738,\n \"acc_norm\": 0.45371577574967403,\n\
\ \"acc_norm_stderr\": 0.012715404841277738\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495144,\n \
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495144\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.763265306122449,\n \"acc_stderr\": 0.02721283588407316,\n\
\ \"acc_norm\": 0.763265306122449,\n \"acc_norm_stderr\": 0.02721283588407316\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827075\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\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.47613219094247244,\n\
\ \"mc1_stderr\": 0.017483547156961574,\n \"mc2\": 0.6270127709181503,\n\
\ \"mc2_stderr\": 0.015065515223932825\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8074191002367798,\n \"acc_stderr\": 0.011082538847491906\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7179681576952237,\n \
\ \"acc_stderr\": 0.012394926584335688\n }\n}\n```"
repo_url: https://huggingface.co/Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp
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_11T22_21_10.174265
path:
- '**/details_harness|arc:challenge|25_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|gsm8k|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hellaswag|10_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T22-21-10.174265.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-11T22-21-10.174265.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- '**/details_harness|winogrande|5_2023-12-11T22-21-10.174265.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-11T22-21-10.174265.parquet'
- config_name: results
data_files:
- split: 2023_12_11T22_21_10.174265
path:
- results_2023-12-11T22-21-10.174265.parquet
- split: latest
path:
- results_2023-12-11T22-21-10.174265.parquet
---
# Dataset Card for Evaluation run of Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp](https://huggingface.co/Toten5/Marcoroni-v3-neural-chat-v3-3-Slerp) 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_Toten5__Marcoroni-v3-neural-chat-v3-3-Slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-11T22:21:10.174265](https://huggingface.co/datasets/open-llm-leaderboard/details_Toten5__Marcoroni-v3-neural-chat-v3-3-Slerp/blob/main/results_2023-12-11T22-21-10.174265.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": 0.6497954997030103,
"acc_stderr": 0.03218797050617161,
"acc_norm": 0.6495568440119162,
"acc_norm_stderr": 0.032855200604616566,
"mc1": 0.47613219094247244,
"mc1_stderr": 0.017483547156961574,
"mc2": 0.6270127709181503,
"mc2_stderr": 0.015065515223932825
},
"harness|arc:challenge|25": {
"acc": 0.6621160409556314,
"acc_stderr": 0.013822047922283512,
"acc_norm": 0.6877133105802048,
"acc_norm_stderr": 0.013542598541688067
},
"harness|hellaswag|10": {
"acc": 0.6793467436765585,
"acc_stderr": 0.004657738398900938,
"acc_norm": 0.8654650468034256,
"acc_norm_stderr": 0.003405288007233203
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.028049186315695248,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.028049186315695248
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
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"acc_stderr": 0.012394926584335688
}
}
```
## 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. -->
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## 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] |
Izazk/Sequence-of-action-prediction-mind2web | ---
license: mit
---
|
gmltnwwkd/test1 | ---
dataset_info:
features:
- name: path
dtype: string
- name: sentence
dtype: string
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 1439737561.5255475
num_examples: 287
- name: test
num_bytes: 553424360.4744525
num_examples: 124
download_size: 1911438374
dataset_size: 1993161922.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "test1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_KnutJaegersberg__LLongMA-3b-LIMA | ---
pretty_name: Evaluation run of KnutJaegersberg/LLongMA-3b-LIMA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/LLongMA-3b-LIMA](https://huggingface.co/KnutJaegersberg/LLongMA-3b-LIMA)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__LLongMA-3b-LIMA\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T12:59:36.364632](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__LLongMA-3b-LIMA/blob/main/results_2023-10-27T12-59-36.364632.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.00027736144573357115,\n \"f1\": 0.04566589765100663,\n\
\ \"f1_stderr\": 0.0012269345796283918,\n \"acc\": 0.3184065922558586,\n\
\ \"acc_stderr\": 0.007527358968906723\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573357115,\n\
\ \"f1\": 0.04566589765100663,\n \"f1_stderr\": 0.0012269345796283918\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \
\ \"acc_stderr\": 0.00151457356122455\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6337805840568271,\n \"acc_stderr\": 0.013540144376588896\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/LLongMA-3b-LIMA
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|arc:challenge|25_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T12_59_36.364632
path:
- '**/details_harness|drop|3_2023-10-27T12-59-36.364632.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T12-59-36.364632.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T12_59_36.364632
path:
- '**/details_harness|gsm8k|5_2023-10-27T12-59-36.364632.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T12-59-36.364632.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hellaswag|10_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T20:09:53.352642.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T20:09:53.352642.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T20:09:53.352642.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T12_59_36.364632
path:
- '**/details_harness|winogrande|5_2023-10-27T12-59-36.364632.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T12-59-36.364632.parquet'
- config_name: results
data_files:
- split: 2023_09_03T20_09_53.352642
path:
- results_2023-09-03T20:09:53.352642.parquet
- split: 2023_10_27T12_59_36.364632
path:
- results_2023-10-27T12-59-36.364632.parquet
- split: latest
path:
- results_2023-10-27T12-59-36.364632.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/LLongMA-3b-LIMA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/LLongMA-3b-LIMA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/LLongMA-3b-LIMA](https://huggingface.co/KnutJaegersberg/LLongMA-3b-LIMA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__LLongMA-3b-LIMA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T12:59:36.364632](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__LLongMA-3b-LIMA/blob/main/results_2023-10-27T12-59-36.364632.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573357115,
"f1": 0.04566589765100663,
"f1_stderr": 0.0012269345796283918,
"acc": 0.3184065922558586,
"acc_stderr": 0.007527358968906723
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573357115,
"f1": 0.04566589765100663,
"f1_stderr": 0.0012269345796283918
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.00151457356122455
},
"harness|winogrande|5": {
"acc": 0.6337805840568271,
"acc_stderr": 0.013540144376588896
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Dahoas/rm_instruct_helpful_preferences | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 151767503
num_examples: 85161
- name: test
num_bytes: 9924509
num_examples: 5538
download_size: 97731490
dataset_size: 161692012
---
# Dataset Card for "rm_instruct_helpful_preferences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aintech/vdf_configs | ---
license: apache-2.0
---
|
nullne/test | ---
license: apache-2.0
---
|
hackathon-somos-nlp-2023/DiagTrast | ---
dataset_info:
features:
- name: Sintoma
dtype: string
- name: Padecimiento
dtype: string
- name: Padecimiento_cat
dtype: int64
- name: Sintoma_limpia
dtype: string
splits:
- name: train
num_bytes: 524464
num_examples: 1333
download_size: 232511
dataset_size: 524464
task_categories:
- text-classification
language:
- es
size_categories:
- 1K<n<10K
license: mit
tags:
- mental
- medical
- disorder
pretty_name: DiagTrast
---
# Dataset Card for "DiagTrast"
## Table of Content
- [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)
- [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)
- [Team members](#team-members)
## Dataset Description
### Dataset Summary
For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:
- Narcissistic personality disorder.
- Histrionic personality disorder.
- Borderline personality disorder.
- Antisocial personality disorder.
- Schizotypal personality disorder.
### Supported Tasks and Leaderboards
- text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.
### Languages
This dataset of statements is in Spanish only.
## Dataset Structure
### Data Instances
A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).
The following is a JSON-formatted example of a typical case in this dataset:
```
{
'Sintoma': "Su comportamiento es a menudo extraño y excéntrico, como llevar ropa que no coincide o actuar de una manera inapropiada en situaciones sociales.",
'Padecimiento': "Trastornos de la personalidad esquizotípica",
'Padecimiento_cat': 2,
'Sintoma_limpia ': "comportamiento menudo extraño excentrico llevar ropa coincide actuar manera inapropiada situaciones sociales"
}
```
### Data Fields
- `Sintoma`: a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
- `Padecimiento`: a string that indicates the disorder according to DSM-5.
- `Padecimiento_cat`: an integer representing the `Padecimiento` field, this field can be used as a label in a text-classification model.
- `Sintoma_Limpia`: a string, this field is the clean text of the `Sintoma` field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.
### Data Splits
The data were not split into training and test subsets, instead having a single set with the following distribution:
| Disorder | Records |
| - | - |
| Narcissistic personality disorder| 250 |
| Histrionic personality disorder | 250 |
| Borderline personality disorder | 358 |
| Antisocial personality disorder | 250 |
| Schizotypal personality disorder | 225 |
## Dataset Creation
### Curation Rationale
It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.
### Source Data
Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.
### Annotations
#### Annotation process
The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.
### Discussion of Biases
It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.
### Other Known Limitations
We have only generated data for 5 of the disorders described in the DSM-5.
## Team members
- [Alberto Martín Garrido](https://huggingface.co/Stremie)
- [Edgar Mencia](https://huggingface.co/edmenciab)
- [Miguel Ángel Solís Orozco](https://huggingface.co/homosapienssapiens)
- [Jose Carlos Vílchez Villegas](https://huggingface.co/JCarlos) |
Decre99/Youtube_Links | ---
license: mit
language:
- it
task_categories:
- text-classification
tags:
- code
pretty_name: Test
--- |
mstz/acute_inflammation | ---
language:
- en
tags:
- acute_inflammation
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Acute Inflammation
size_categories:
- 100<n<1K
task_categories:
- tabular-classification
configs:
- inflammation
- nephritis
- bladder
---
# Acute Inflammation
The [Acute Inflammation dataset](https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations) from the [UCI ML repository](https://archive-beta.ics.uci.edu).
Predict whether the patient has an acute inflammation.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| inflammation | Binary classification | Does the patient have an acute inflammation? |
| nephritis | Binary classification | Does the patient have a nephritic pelvis? |
| bladder | Binary classification | Does the patient have bladder inflammation? |
nephritis
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/acute_inflammation", "inflammation")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
| **Feature** | **Type** |
|---------------------------------------|---------------|
| `temperature` | `[float64]` |
| `has_nausea` | `[bool]` |
| `has_lumbar_pain` | `[bool]` |
| `has_urine_pushing` | `[bool]` |
| `has_micturition_pains` | `[bool]` |
| `has_burnt_urethra` | `[bool]` |
| `has_inflammed_bladder` | `[bool]` |
| `has_nephritis_of_renal_pelvis` | `[bool]` |
| `has_acute_inflammation` | `[int8]` | |
zolak/twitter_dataset_79_1713172864 | ---
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: 294613
num_examples: 710
download_size: 150185
dataset_size: 294613
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xlangai/ubuntu_arm | ---
license: apache-2.0
---
|
safetyllm/daily_conversations | ---
license: cdla-sharing-1.0
task_categories:
- text-generation
language:
- en
tags:
- daily-conversation
- large-language-model
- conversation-completion
size_categories:
- 10K<n<100K
---
This dataset is synthetically generated using ChatGPT 3.5 to contain two-person multi-turn daily conversations with a various of topics (e.g.
travel, food, music, movie/TV, education, hobbies, family, sports, technology, books, etc.) Originally, this dataset is used to train
[QuicktypeGPT](https://github.com/chaoluond/quicktypeGPT/tree/main), which is a GPT model to assist auto complete conversations.
Here is the full list of [topics](https://github.com/chaoluond/quicktypeGPT/blob/main/training_data/topics.txt) the conversation may cover. |
irds/beir_fever_dev | ---
pretty_name: '`beir/fever/dev`'
viewer: false
source_datasets: ['irds/beir_fever']
task_categories:
- text-retrieval
---
# Dataset Card for `beir/fever/dev`
The `beir/fever/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/fever/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=6,666
- `qrels`: (relevance assessments); count=8,079
- For `docs`, use [`irds/beir_fever`](https://huggingface.co/datasets/irds/beir_fever)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/beir_fever_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/beir_fever_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Thorne2018Fever,
title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",
author = "Thorne, James and
Vlachos, Andreas and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N18-1074",
doi = "10.18653/v1/N18-1074",
pages = "809--819"
}
@article{Thakur2021Beir,
title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
journal= "arXiv preprint arXiv:2104.08663",
month = "4",
year = "2021",
url = "https://arxiv.org/abs/2104.08663",
}
```
|
KnutJaegersberg/trilobite | ---
license: cc-by-nc-4.0
---
|
clarin-knext/hotpotqa-pl-qrels | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl |
open-llm-leaderboard/details_fionazhang__fine-tune-mistral-long-merge | ---
pretty_name: Evaluation run of fionazhang/fine-tune-mistral-long-merge
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fionazhang/fine-tune-mistral-long-merge](https://huggingface.co/fionazhang/fine-tune-mistral-long-merge)\
\ 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_fionazhang__fine-tune-mistral-long-merge\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-01T18:38:59.873135](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-long-merge/blob/main/results_2024-02-01T18-38-59.873135.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\": 0.6310974603167736,\n\
\ \"acc_stderr\": 0.03251926531091339,\n \"acc_norm\": 0.6372662374519631,\n\
\ \"acc_norm_stderr\": 0.03317893564792818,\n \"mc1\": 0.2937576499388005,\n\
\ \"mc1_stderr\": 0.015945068581236614,\n \"mc2\": 0.4393573192333758,\n\
\ \"mc2_stderr\": 0.014110064746912822\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5767918088737202,\n \"acc_stderr\": 0.01443803622084803,\n\
\ \"acc_norm\": 0.628839590443686,\n \"acc_norm_stderr\": 0.014117971901142824\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6363274248157738,\n\
\ \"acc_stderr\": 0.004800728138792393,\n \"acc_norm\": 0.8361880103565027,\n\
\ \"acc_norm_stderr\": 0.003693484894179418\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \
\ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\"\
: 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"\
acc\": 0.6641509433962264,\n \"acc_stderr\": 0.029067220146644826,\n \
\ \"acc_norm\": 0.6641509433962264,\n \"acc_norm_stderr\": 0.029067220146644826\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n\
\ \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n\
\ \"acc_norm_stderr\": 0.038009680605548594\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\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\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\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\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137282,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137282\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\
\ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\
\ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\
\ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.02423353229775873,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.02423353229775873\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465397,\n \
\ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465397\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010354,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010354\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\"\
: 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7941176470588235,\n\
\ \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n\
\ \"acc_norm_stderr\": 0.028379449451588667\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7552742616033755,\n \"acc_stderr\": 0.02798569938703643,\n\
\ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.02798569938703643\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\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\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.03083349114628124,\n\
\ \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.03083349114628124\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973133,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973133\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\
\ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39217877094972065,\n\
\ \"acc_stderr\": 0.01632906107320744,\n \"acc_norm\": 0.39217877094972065,\n\
\ \"acc_norm_stderr\": 0.01632906107320744\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n\
\ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464482,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464482\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.025329888171900926,\n\
\ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.025329888171900926\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44198174706649285,\n\
\ \"acc_stderr\": 0.01268397251359881,\n \"acc_norm\": 0.44198174706649285,\n\
\ \"acc_norm_stderr\": 0.01268397251359881\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\
\ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\
\ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\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\": 0.2937576499388005,\n\
\ \"mc1_stderr\": 0.015945068581236614,\n \"mc2\": 0.4393573192333758,\n\
\ \"mc2_stderr\": 0.014110064746912822\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7892659826361483,\n \"acc_stderr\": 0.011462046419710674\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36087945413191813,\n \
\ \"acc_stderr\": 0.013228626753925138\n }\n}\n```"
repo_url: https://huggingface.co/fionazhang/fine-tune-mistral-long-merge
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-38-59.873135.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-01T18-38-59.873135.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- '**/details_harness|winogrande|5_2024-02-01T18-38-59.873135.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-01T18-38-59.873135.parquet'
- config_name: results
data_files:
- split: 2024_02_01T18_38_59.873135
path:
- results_2024-02-01T18-38-59.873135.parquet
- split: latest
path:
- results_2024-02-01T18-38-59.873135.parquet
---
# Dataset Card for Evaluation run of fionazhang/fine-tune-mistral-long-merge
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [fionazhang/fine-tune-mistral-long-merge](https://huggingface.co/fionazhang/fine-tune-mistral-long-merge) 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_fionazhang__fine-tune-mistral-long-merge",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-01T18:38:59.873135](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-long-merge/blob/main/results_2024-02-01T18-38-59.873135.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": 0.6310974603167736,
"acc_stderr": 0.03251926531091339,
"acc_norm": 0.6372662374519631,
"acc_norm_stderr": 0.03317893564792818,
"mc1": 0.2937576499388005,
"mc1_stderr": 0.015945068581236614,
"mc2": 0.4393573192333758,
"mc2_stderr": 0.014110064746912822
},
"harness|arc:challenge|25": {
"acc": 0.5767918088737202,
"acc_stderr": 0.01443803622084803,
"acc_norm": 0.628839590443686,
"acc_norm_stderr": 0.014117971901142824
},
"harness|hellaswag|10": {
"acc": 0.6363274248157738,
"acc_stderr": 0.004800728138792393,
"acc_norm": 0.8361880103565027,
"acc_norm_stderr": 0.003693484894179418
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6641509433962264,
"acc_stderr": 0.029067220146644826,
"acc_norm": 0.6641509433962264,
"acc_norm_stderr": 0.029067220146644826
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7083333333333334,
"acc_stderr": 0.038009680605548594,
"acc_norm": 0.7083333333333334,
"acc_norm_stderr": 0.038009680605548594
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"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": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.7552742616033755,
"acc_norm_stderr": 0.02798569938703643
},
"harness|hendrycksTest-human_aging|5": {
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"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596914,
"acc_norm": 0.7633587786259542,
"acc_norm_stderr": 0.03727673575596914
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.04330043749650743,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.04330043749650743
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8098159509202454,
"acc_stderr": 0.03083349114628124,
"acc_norm": 0.8098159509202454,
"acc_norm_stderr": 0.03083349114628124
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_stderr": 0.013816335389973133,
"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973133
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.7138728323699421,
"acc_norm_stderr": 0.02433214677913413
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.39217877094972065,
"acc_norm_stderr": 0.01632906107320744
},
"harness|hendrycksTest-nutrition|5": {
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"acc_stderr": 0.0242886194660461,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.0242886194660461
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.684887459807074,
"acc_stderr": 0.026385273703464482,
"acc_norm": 0.684887459807074,
"acc_norm_stderr": 0.026385273703464482
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7067901234567902,
"acc_norm_stderr": 0.025329888171900926
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.44198174706649285,
"acc_norm_stderr": 0.01268397251359881
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_stderr": 0.02815637344037142,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.02815637344037142
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6503267973856209,
"acc_stderr": 0.01929196189506638,
"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.01929196189506638
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.044612721759105085,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.044612721759105085
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.0289205832206756,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.0289205832206756
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.02768691358801302,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.02768691358801302
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.034873508801977704,
"acc_norm": 0.86,
"acc_norm_stderr": 0.034873508801977704
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
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"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2937576499388005,
"mc1_stderr": 0.015945068581236614,
"mc2": 0.4393573192333758,
"mc2_stderr": 0.014110064746912822
},
"harness|winogrande|5": {
"acc": 0.7892659826361483,
"acc_stderr": 0.011462046419710674
},
"harness|gsm8k|5": {
"acc": 0.36087945413191813,
"acc_stderr": 0.013228626753925138
}
}
```
## 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
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#### 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. -->
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## Glossary [optional]
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## Dataset Card Contact
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meta-math/GSM8K_zh | ---
license: mit
task_categories:
- question-answering
language:
- en
- zh
tags:
- math
- math-qa
- chinese-math-qa
size_categories:
- n<1K
---
# Dataset
`GSM8K_zh` is a dataset for mathematical reasoning in Chinese, question-answer pairs are translated from GSM8K (https://github.com/openai/grade-school-math/tree/master) by `GPT-3.5-Turbo` with few-shot prompting.
The dataset consists of 7473 training samples and 1319 testing samples. The former is for **supervised fine-tuning**, while the latter is for **evaluation**.
for training samples, `question_zh` and `answer_zh` are question and answer keys, respectively;
for testing samples, only the translated questions are provided (`question_zh`).
# Citation
If you find the `GSM8K_zh` dataset useful for your projects/papers, please cite the following paper.
```bibtex
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
``` |
senhorsapo/ciano | ---
license: openrail
---
|
open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16 | ---
pretty_name: Evaluation run of TheBloke/Platypus-30B-SuperHOT-8K-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Platypus-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 60 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-18T16:25:34.320244](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-18T16%3A25%3A34.320244.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\": 0.23647488823331855,\n\
\ \"acc_stderr\": 0.030908567573023033,\n \"acc_norm\": 0.23771978116158754,\n\
\ \"acc_norm_stderr\": 0.030923042741200276,\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.471292004765754,\n\
\ \"mc2_stderr\": 0.01664156844910162\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.21843003412969283,\n \"acc_stderr\": 0.012074291605700987,\n\
\ \"acc_norm\": 0.2568259385665529,\n \"acc_norm_stderr\": 0.0127669237941168\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2731527584146584,\n\
\ \"acc_stderr\": 0.004446680081493746,\n \"acc_norm\": 0.3082055367456682,\n\
\ \"acc_norm_stderr\": 0.004608082815535489\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2074074074074074,\n\
\ \"acc_stderr\": 0.035025531706783186,\n \"acc_norm\": 0.2074074074074074,\n\
\ \"acc_norm_stderr\": 0.035025531706783186\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.032790004063100515,\n\
\ \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.032790004063100515\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.22641509433962265,\n \"acc_stderr\": 0.025757559893106748,\n\
\ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.025757559893106748\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171453,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171453\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.02865917937429232,\n\
\ \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.02865917937429232\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.036001056927277716,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.036001056927277716\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.040061680838488746,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.040061680838488746\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25483870967741934,\n\
\ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.25483870967741934,\n\
\ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.18226600985221675,\n \"acc_stderr\": 0.02716334085964515,\n\
\ \"acc_norm\": 0.18226600985221675,\n \"acc_norm_stderr\": 0.02716334085964515\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\
: 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.18686868686868688,\n \"acc_stderr\": 0.02777253333421898,\n \"\
acc_norm\": 0.18686868686868688,\n \"acc_norm_stderr\": 0.02777253333421898\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.02951928261681723,\n\
\ \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.02951928261681723\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.021444547301560476,\n\
\ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.021444547301560476\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2037037037037037,\n \"acc_stderr\": 0.024556172219141265,\n \
\ \"acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.024556172219141265\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715494,\n\
\ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715494\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.17218543046357615,\n \"acc_stderr\": 0.030826136961962396,\n \"\
acc_norm\": 0.17218543046357615,\n \"acc_norm_stderr\": 0.030826136961962396\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1981651376146789,\n \"acc_stderr\": 0.017090573804217885,\n \"\
acc_norm\": 0.1981651376146789,\n \"acc_norm_stderr\": 0.017090573804217885\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2037037037037037,\n \"acc_stderr\": 0.02746740180405799,\n \"\
acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.02746740180405799\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.28270042194092826,\n \"acc_stderr\": 0.02931281415395592,\n\
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.02931281415395592\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3094170403587444,\n\
\ \"acc_stderr\": 0.031024411740572203,\n \"acc_norm\": 0.3094170403587444,\n\
\ \"acc_norm_stderr\": 0.031024411740572203\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.29770992366412213,\n \"acc_stderr\": 0.04010358942462203,\n\
\ \"acc_norm\": 0.29770992366412213,\n \"acc_norm_stderr\": 0.04010358942462203\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.2515964240102171,\n\
\ \"acc_stderr\": 0.015517322365529619,\n \"acc_norm\": 0.2515964240102171,\n\
\ \"acc_norm_stderr\": 0.015517322365529619\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.023083658586984204,\n\
\ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.023083658586984204\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n\
\ \"acc_stderr\": 0.014265554192331144,\n \"acc_norm\": 0.23910614525139665,\n\
\ \"acc_norm_stderr\": 0.014265554192331144\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22875816993464052,\n \"acc_stderr\": 0.024051029739912255,\n\
\ \"acc_norm\": 0.22875816993464052,\n \"acc_norm_stderr\": 0.024051029739912255\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2347266881028939,\n\
\ \"acc_stderr\": 0.024071805887677048,\n \"acc_norm\": 0.2347266881028939,\n\
\ \"acc_norm_stderr\": 0.024071805887677048\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.02289916291844581,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.02289916291844581\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23049645390070922,\n \"acc_stderr\": 0.025123739226872405,\n \
\ \"acc_norm\": 0.23049645390070922,\n \"acc_norm_stderr\": 0.025123739226872405\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24967405475880053,\n\
\ \"acc_stderr\": 0.011054538377832318,\n \"acc_norm\": 0.24967405475880053,\n\
\ \"acc_norm_stderr\": 0.011054538377832318\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.1948529411764706,\n \"acc_stderr\": 0.024060599423487428,\n\
\ \"acc_norm\": 0.1948529411764706,\n \"acc_norm_stderr\": 0.024060599423487428\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \
\ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.17959183673469387,\n \"acc_stderr\": 0.024573293589585637,\n\
\ \"acc_norm\": 0.17959183673469387,\n \"acc_norm_stderr\": 0.024573293589585637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\
\ \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.23880597014925373,\n\
\ \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.27710843373493976,\n\
\ \"acc_stderr\": 0.034843315926805875,\n \"acc_norm\": 0.27710843373493976,\n\
\ \"acc_norm_stderr\": 0.034843315926805875\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2573099415204678,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.2573099415204678,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.471292004765754,\n\
\ \"mc2_stderr\": 0.01664156844910162\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|arc:challenge|25_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hellaswag|10_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T16:25:34.320244.parquet'
---
# Dataset Card for Evaluation run of TheBloke/Platypus-30B-SuperHOT-8K-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Platypus-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 60 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-18T16:25:34.320244](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-18T16%3A25%3A34.320244.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": 0.23647488823331855,
"acc_stderr": 0.030908567573023033,
"acc_norm": 0.23771978116158754,
"acc_norm_stderr": 0.030923042741200276,
"mc1": 0.2178702570379437,
"mc1_stderr": 0.014450846714123892,
"mc2": 0.471292004765754,
"mc2_stderr": 0.01664156844910162
},
"harness|arc:challenge|25": {
"acc": 0.21843003412969283,
"acc_stderr": 0.012074291605700987,
"acc_norm": 0.2568259385665529,
"acc_norm_stderr": 0.0127669237941168
},
"harness|hellaswag|10": {
"acc": 0.2731527584146584,
"acc_stderr": 0.004446680081493746,
"acc_norm": 0.3082055367456682,
"acc_norm_stderr": 0.004608082815535489
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2074074074074074,
"acc_stderr": 0.035025531706783186,
"acc_norm": 0.2074074074074074,
"acc_norm_stderr": 0.035025531706783186
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.20394736842105263,
"acc_stderr": 0.032790004063100515,
"acc_norm": 0.20394736842105263,
"acc_norm_stderr": 0.032790004063100515
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.22641509433962265,
"acc_stderr": 0.025757559893106748,
"acc_norm": 0.22641509433962265,
"acc_norm_stderr": 0.025757559893106748
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2361111111111111,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.2361111111111111,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.2,
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"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.04220773659171453,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.04220773659171453
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.25957446808510637,
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"acc_norm": 0.25957446808510637,
"acc_norm_stderr": 0.02865917937429232
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.040969851398436695,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.040969851398436695
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.2482758620689655,
"acc_norm_stderr": 0.036001056927277716
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.20899470899470898,
"acc_norm_stderr": 0.02094048156533486
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.040061680838488746,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.040061680838488746
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.16,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.25483870967741934,
"acc_norm_stderr": 0.024790118459332208
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.02716334085964515,
"acc_norm": 0.18226600985221675,
"acc_norm_stderr": 0.02716334085964515
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm_stderr": 0.02777253333421898
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21243523316062177,
"acc_stderr": 0.02951928261681723,
"acc_norm": 0.21243523316062177,
"acc_norm_stderr": 0.02951928261681723
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_stderr": 0.021444547301560476,
"acc_norm": 0.23333333333333334,
"acc_norm_stderr": 0.021444547301560476
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.2037037037037037,
"acc_norm_stderr": 0.024556172219141265
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21428571428571427,
"acc_stderr": 0.026653531596715494,
"acc_norm": 0.21428571428571427,
"acc_norm_stderr": 0.026653531596715494
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.17218543046357615,
"acc_stderr": 0.030826136961962396,
"acc_norm": 0.17218543046357615,
"acc_norm_stderr": 0.030826136961962396
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.1981651376146789,
"acc_stderr": 0.017090573804217885,
"acc_norm": 0.1981651376146789,
"acc_norm_stderr": 0.017090573804217885
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.2037037037037037,
"acc_stderr": 0.02746740180405799,
"acc_norm": 0.2037037037037037,
"acc_norm_stderr": 0.02746740180405799
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25,
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"acc_norm": 0.25,
"acc_norm_stderr": 0.03039153369274154
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.28270042194092826,
"acc_stderr": 0.02931281415395592,
"acc_norm": 0.28270042194092826,
"acc_norm_stderr": 0.02931281415395592
},
"harness|hendrycksTest-human_aging|5": {
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"acc_norm": 0.3094170403587444,
"acc_norm_stderr": 0.031024411740572203
},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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"acc_norm": 0.24793388429752067,
"acc_norm_stderr": 0.039418975265163025
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_stderr": 0.04364226155841044,
"acc_norm": 0.30357142857142855,
"acc_norm_stderr": 0.04364226155841044
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2905982905982906,
"acc_stderr": 0.02974504857267404,
"acc_norm": 0.2905982905982906,
"acc_norm_stderr": 0.02974504857267404
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm_stderr": 0.015517322365529619
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.24277456647398843,
"acc_norm_stderr": 0.023083658586984204
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23910614525139665,
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"acc_norm": 0.23910614525139665,
"acc_norm_stderr": 0.014265554192331144
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.22875816993464052,
"acc_stderr": 0.024051029739912255,
"acc_norm": 0.22875816993464052,
"acc_norm_stderr": 0.024051029739912255
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2347266881028939,
"acc_stderr": 0.024071805887677048,
"acc_norm": 0.2347266881028939,
"acc_norm_stderr": 0.024071805887677048
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.02289916291844581,
"acc_norm": 0.21604938271604937,
"acc_norm_stderr": 0.02289916291844581
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23049645390070922,
"acc_stderr": 0.025123739226872405,
"acc_norm": 0.23049645390070922,
"acc_norm_stderr": 0.025123739226872405
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.24967405475880053,
"acc_stderr": 0.011054538377832318,
"acc_norm": 0.24967405475880053,
"acc_norm_stderr": 0.011054538377832318
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.1948529411764706,
"acc_norm_stderr": 0.024060599423487428
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.017812676542320657
},
"harness|hendrycksTest-public_relations|5": {
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"acc_stderr": 0.03955932861795833,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
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"acc_stderr": 0.024573293589585637,
"acc_norm": 0.17959183673469387,
"acc_norm_stderr": 0.024573293589585637
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.23880597014925373,
"acc_norm_stderr": 0.030147775935409217
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-virology|5": {
"acc": 0.27710843373493976,
"acc_stderr": 0.034843315926805875,
"acc_norm": 0.27710843373493976,
"acc_norm_stderr": 0.034843315926805875
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2573099415204678,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.2573099415204678,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2178702570379437,
"mc1_stderr": 0.014450846714123892,
"mc2": 0.471292004765754,
"mc2_stderr": 0.01664156844910162
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
MiXaiLL76/ru_book_dataset | ---
language:
- ru
dataset_info:
features:
- name: text
dtype: string
- name: text_len
dtype: int64
- name: language
dtype: string
- name: speaker_name
dtype: string
- name: duration
dtype: float64
- name: audio
dtype: audio
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 730970134.96
num_examples: 11260
download_size: 735809788
dataset_size: 730970134.96
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tr416/dataset_20231007_025331 | ---
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: 73708
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_025331"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
valituttiromero/tatiquebrabarraco | ---
license: openrail
---
|
autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761027 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: match
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-7b1
* Dataset: phpthinh/examplei
* Config: match
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model. |
CyberHarem/carole_pepper_honkai3 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of carole_pepper (Houkai 3rd)
This is the dataset of carole_pepper (Houkai 3rd), containing 73 images and their tags.
The core tags of this character are `dark_skin, bangs, dark-skinned_female, white_hair, yellow_eyes, short_hair, earrings, 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 | 73 | 108.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carole_pepper_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 73 | 56.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carole_pepper_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 177 | 121.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carole_pepper_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 73 | 92.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carole_pepper_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 177 | 176.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/carole_pepper_honkai3/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/carole_pepper_honkai3',
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 | 73 |  |  |  |  |  | 1girl, solo, looking_at_viewer, white_shirt, jacket_around_waist, bare_shoulders, black_gloves, fingerless_gloves, jewelry, blue_jacket, open_mouth, long_sleeves, shorts, :d |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_shirt | jacket_around_waist | bare_shoulders | black_gloves | fingerless_gloves | jewelry | blue_jacket | open_mouth | long_sleeves | shorts | :d |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:----------------------|:-----------------|:---------------|:--------------------|:----------|:--------------|:-------------|:---------------|:---------|:-----|
| 0 | 73 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
amitness/logits-arabic-128 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: teacher_logits
sequence:
sequence: float64
- name: teacher_indices
sequence:
sequence: int64
- name: teacher_mask_indices
sequence: int64
splits:
- name: train
num_bytes: 19440049160
num_examples: 4294918
download_size: 7814026203
dataset_size: 19440049160
---
# Dataset Card for "logits-arabic-128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-electrical_engineering-dev | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 2539
num_examples: 5
download_size: 0
dataset_size: 2539
---
# Dataset Card for "mmlu-electrical_engineering-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AnnaSmirnova/russisan_noun_definitions | ---
task_categories:
- text-classification
language:
- ru
size_categories:
- n<1K
--- |
CyberHarem/springfield_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of springfield/スプリングフィールド/春田 (Girls' Frontline)
This is the dataset of springfield/スプリングフィールド/春田 (Girls' Frontline), containing 500 images and their tags.
The core tags of this character are `long_hair, green_eyes, brown_hair, breasts, bangs, hair_between_eyes, large_breasts, ribbon, sidelocks, hair_ribbon, hair_rings`, 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 | 814.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/springfield_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 422.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/springfield_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1287 | 941.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/springfield_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 702.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/springfield_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1287 | 1.36 GiB | [Download](https://huggingface.co/datasets/CyberHarem/springfield_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/springfield_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 | 16 |  |  |  |  |  | 1girl, white_gloves, blue_jacket, solo, long_sleeves, holding_gun, white_dress, bolt_action, looking_at_viewer, simple_background, closed_mouth, neck_ribbon, smile, blush, white_background, red_ribbon |
| 1 | 10 |  |  |  |  |  | 1girl, smile, solo, blush, long_sleeves, looking_at_viewer, simple_background, white_background, neck_ribbon, white_gloves, blue_jacket, red_ribbon, shirt, upper_body, closed_mouth |
| 2 | 20 |  |  |  |  |  | 1girl, blush, looking_at_viewer, white_shirt, solo, ponytail, smile, brown_apron, orange_hair, simple_background, long_sleeves, white_background, open_mouth, upper_body, alternate_costume, collared_shirt, holding_tray |
| 3 | 6 |  |  |  |  |  | 1girl, blue_jacket, brown_footwear, full_body, long_sleeves, looking_at_viewer, neck_ribbon, white_background, white_gloves, blush, lace-up_boots, red_ribbon, simple_background, solo, white_dress, knee_boots, closed_mouth, smile, standing |
| 4 | 13 |  |  |  |  |  | blush, cleavage, looking_at_viewer, official_alternate_costume, white_bikini, 1girl, smile, solo, navel, o-ring_bikini, o-ring_top, simple_background, white_background, thighs, blue_ribbon, closed_mouth, sarong |
| 5 | 5 |  |  |  |  |  | 1girl, blush, cleavage, navel, official_alternate_costume, sarong, see-through, solo, sun_hat, tied_shirt, white_shirt, hand_on_headwear, looking_at_viewer, simple_background, sunglasses, eyewear_hang, hat_flower, hat_ribbon, open_mouth, stomach, white_background, :d, closed_mouth, collared_shirt, cowboy_shot, highleg_bikini, o-ring_bikini, o-ring_top, sleeves_rolled_up, wet, white_bikini, white_headwear |
| 6 | 14 |  |  |  |  |  | blue_sky, blush, cleavage, day, looking_at_viewer, navel, official_alternate_costume, outdoors, smile, white_bikini, 1girl, solo, cloud, ocean, sun_hat, beach, open_mouth, collarbone, o-ring_bikini, sarong, flower, o-ring_top, thighs, bare_shoulders, closed_mouth, leaning_forward, stomach, sunglasses |
| 7 | 17 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, nipples, completely_nude, sex, solo_focus, penis, navel, sweat, vaginal, heart, collarbone, cum_in_pussy, cowgirl_position, girl_on_top, simple_background, ass, ponytail, uncensored, white_background, looking_at_viewer, lying |
| 8 | 7 |  |  |  |  |  | 1girl, black_dress, blush, cape, cleavage, looking_at_viewer, official_alternate_costume, solo, witch_hat, elbow_gloves, halloween_costume, smile, choker, basket, black_gloves, candy, pantyhose, simple_background, open_mouth |
| 9 | 20 |  |  |  |  |  | 1girl, official_alternate_costume, bare_shoulders, blue_dress, looking_at_viewer, solo, black_gloves, smile, blush, hair_flower, cleavage, closed_mouth, collarbone, hair_bun, simple_background |
| 10 | 10 |  |  |  |  |  | 1girl, blush, official_alternate_costume, white_sweater, smile, looking_at_viewer, ribbed_sweater, solo, belt, pantyhose, hair_over_shoulder, single_braid, black_gloves, long_sleeves, brown_footwear, brown_skirt, christmas, hooded_cape, knee_boots, sitting, turtleneck |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | white_gloves | blue_jacket | solo | long_sleeves | holding_gun | white_dress | bolt_action | looking_at_viewer | simple_background | closed_mouth | neck_ribbon | smile | blush | white_background | red_ribbon | shirt | upper_body | white_shirt | ponytail | brown_apron | orange_hair | open_mouth | alternate_costume | collared_shirt | holding_tray | brown_footwear | full_body | lace-up_boots | knee_boots | standing | cleavage | official_alternate_costume | white_bikini | navel | o-ring_bikini | o-ring_top | thighs | blue_ribbon | sarong | see-through | sun_hat | tied_shirt | hand_on_headwear | sunglasses | eyewear_hang | hat_flower | hat_ribbon | stomach | :d | cowboy_shot | highleg_bikini | sleeves_rolled_up | wet | white_headwear | blue_sky | day | outdoors | cloud | ocean | beach | collarbone | flower | bare_shoulders | leaning_forward | 1boy | hetero | nipples | completely_nude | sex | solo_focus | penis | sweat | vaginal | heart | cum_in_pussy | cowgirl_position | girl_on_top | ass | uncensored | lying | black_dress | cape | witch_hat | elbow_gloves | halloween_costume | choker | basket | black_gloves | candy | pantyhose | blue_dress | hair_flower | hair_bun | white_sweater | ribbed_sweater | belt | hair_over_shoulder | single_braid | brown_skirt | christmas | hooded_cape | sitting | turtleneck |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:--------------|:-------|:---------------|:--------------|:--------------|:--------------|:--------------------|:--------------------|:---------------|:--------------|:--------|:--------|:-------------------|:-------------|:--------|:-------------|:--------------|:-----------|:--------------|:--------------|:-------------|:--------------------|:-----------------|:---------------|:-----------------|:------------|:----------------|:-------------|:-----------|:-----------|:-----------------------------|:---------------|:--------|:----------------|:-------------|:---------|:--------------|:---------|:--------------|:----------|:-------------|:-------------------|:-------------|:---------------|:-------------|:-------------|:----------|:-----|:--------------|:-----------------|:--------------------|:------|:-----------------|:-----------|:------|:-----------|:--------|:--------|:--------|:-------------|:---------|:-----------------|:------------------|:-------|:---------|:----------|:------------------|:------|:-------------|:--------|:--------|:----------|:--------|:---------------|:-------------------|:--------------|:------|:-------------|:--------|:--------------|:-------|:------------|:---------------|:--------------------|:---------|:---------|:---------------|:--------|:------------|:-------------|:--------------|:-----------|:----------------|:-----------------|:-------|:---------------------|:---------------|:--------------|:------------|:--------------|:----------|:-------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | X | | | X | X | | | | X | X | | | X | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | | | X | | | | | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | X | | | | | X | X | X | | | X | X | | | | X | | | | X | | X | | | | | | | X | X | X | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 14 |  |  |  |  |  | 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 | 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 | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | | X | | | | | X | X | | | X | X | | | | | | | | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 9 | 20 |  |  |  |  |  | X | | | X | | | | | X | X | X | | X | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | | | | | | | | | | |
| 10 | 10 |  |  |  |  |  | X | | | X | X | | | | X | | | | X | X | | | | | | | | | | | | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | X | X | X |
|
yunus-emre/sentence_completion | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: endings
sequence: string
- name: ctx
dtype: string
- name: label
dtype: int64
- name: activity_label
dtype: string
splits:
- name: test
num_bytes: 1594
num_examples: 6
download_size: 4043
dataset_size: 1594
---
# Dataset Card for "sentence_completion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wraps/instruct-morse-en | ---
license: apache-2.0
language:
- en
pretty_name: instruct-morse
---
# Instruct-morse-en Dataset
This dataset contains instructions in English for the task of Morse code encoding and decoding. The dataset is generated using mixtral and morsify for encoding and decoding respectively. |
jordonpeter01/fuego-20230902-041902-c6d36e | ---
tags:
- fuego
fuego:
id: 20230902-041902-c6d36e
status: done
script: run_glue.py
requirements_file: requirements.txt
space_id: jordonpeter01/fuego-20230902-041902-c6d36e
space_hardware: cpu-basic
github_repo_id: huggingface/transformers
github_repo_branch: main
github_repo_sha: 0afa5071bd84e44301750fdc594e33db102cf374
---
|
GamblerYu/eth_tx_cls | ---
license: apache-2.0
---
|
DynamicSuperb/SpeakerVerification_VCTK | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: file2
dtype: string
- name: audio2
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 68176919.76
num_examples: 200
download_size: 69904206
dataset_size: 68176919.76
---
# Dataset Card for "SpeakerVerification_VCTK"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mtkinit/SD | ---
pretty_name: SD
---
# SD
Created from AIOD platform |
abhiram973/Llama2medic2 | ---
license: apache-2.0
---
|
joey234/mmlu-high_school_physics-neg-answer | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_answer
dtype: string
splits:
- name: test
num_bytes: 66580
num_examples: 151
download_size: 37589
dataset_size: 66580
---
# Dataset Card for "mmlu-high_school_physics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pharaouk/libritts_r | ---
license: cc-by-4.0
task_categories:
- text-to-speech
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: dev
data_files:
- split: dev.clean
path: "data/dev.clean/dev.clean*.parquet"
- config_name: clean
data_files:
- split: dev.clean
path: "data/dev.clean/dev.clean*.parquet"
- split: test.clean
path: "data/test.clean/test.clean*.parquet"
- split: train.clean.100
path: "data/train.clean.100/train.clean.100*.parquet"
- split: train.clean.360
path: "data/train.clean.360/train.clean.360*.parquet"
- config_name: other
data_files:
- split: dev.other
path: "data/dev.other/dev.other*.parquet"
- split: test.other
path: "data/test.other/test.other*.parquet"
- split: train.other.500
path: "data/train.other.500/train.other.500*.parquet"
- config_name: all
data_files:
- split: dev.clean
path: "data/dev.clean/dev.clean*.parquet"
- split: dev.other
path: "data/dev.other/dev.other*.parquet"
- split: test.clean
path: "data/test.clean/test.clean*.parquet"
- split: test.other
path: "data/test.other/test.other*.parquet"
- split: train.clean.100
path: "data/train.clean.100/train.clean.100*.parquet"
- split: train.clean.360
path: "data/train.clean.360/train.clean.360*.parquet"
- split: train.other.500
path: "data/train.other.500/train.other.500*.parquet"
---
# Dataset Card for LibriTTS-R
<!-- Provide a quick summary of the dataset. -->
LibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus
(http://www.openslr.org/60/) which is a multi-speaker English corpus of approximately
585 hours of read English speech at 24kHz sampling rate, published in 2019.
## Overview
This is the LibriTTS-R dataset, adapted for the `datasets` library.
## Usage
### Splits
There are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):
- dev.clean
- dev.other
- test.clean
- test.other
- train.clean.100
- train.clean.360
- train.other.500
### Configurations
There are 3 configurations, each which limits the splits the `load_dataset()` function will download.
The default configuration is "all".
- "dev": only the "dev.clean" split (good for testing the dataset quickly)
- "clean": contains only "clean" splits
- "other": contains only "other" splits
- "all": contains only "all" splits
### Example
Loading the `clean` config with only the `train.clean.360` split.
```
load_dataset("blabble-io/libritts_r", "clean", split="train.clean.100")
```
Streaming is also supported.
```
load_dataset("blabble-io/libritts_r", streaming=True)
```
### Columns
```
{
"audio": datasets.Audio(sampling_rate=24_000),
"text_normalized": datasets.Value("string"),
"text_original": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"path": datasets.Value("string"),
"chapter_id": datasets.Value("string"),
"id": datasets.Value("string"),
}
```
### Example Row
```
{
'audio': {
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'array': ...,
'sampling_rate': 24000
},
'text_normalized': 'How quickly he disappeared!"',
'text_original': 'How quickly he disappeared!"',
'speaker_id': '3081',
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/5551a515e85b9e463062524539c2e1cb52ba32affe128dffd866db0205248bdd/LibriTTS_R/dev-clean/3081/166546/3081_166546_000028_000002.wav',
'chapter_id': '166546',
'id': '3081_166546_000028_000002'
}
```
## Dataset Details
### Dataset Description
- **License:** CC BY 4.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Homepage:** https://www.openslr.org/141/
- **Paper:** https://arxiv.org/abs/2305.18802
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```
@ARTICLE{Koizumi2023-hs,
title = "{LibriTTS-R}: A restored multi-speaker text-to-speech corpus",
author = "Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding,
Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani,
Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur",
abstract = "This paper introduces a new speech dataset called
``LibriTTS-R'' designed for text-to-speech (TTS) use. It is
derived by applying speech restoration to the LibriTTS
corpus, which consists of 585 hours of speech data at 24 kHz
sampling rate from 2,456 speakers and the corresponding
texts. The constituent samples of LibriTTS-R are identical
to those of LibriTTS, with only the sound quality improved.
Experimental results show that the LibriTTS-R ground-truth
samples showed significantly improved sound quality compared
to those in LibriTTS. In addition, neural end-to-end TTS
trained with LibriTTS-R achieved speech naturalness on par
with that of the ground-truth samples. The corpus is freely
available for download from
\textbackslashurl\{http://www.openslr.org/141/\}.",
month = may,
year = 2023,
copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
archivePrefix = "arXiv",
primaryClass = "eess.AS",
eprint = "2305.18802"
}
``` |
davanstrien/notebooks_on_the_hub_raw | ---
dataset_info:
features:
- name: date
dtype: date32
- name: repo_type
dtype: large_string
- name: user
dtype: large_string
- name: repo_id
dtype: large_string
- name: repo_notebook_count
dtype: int64
splits:
- name: train
num_bytes: 121098396
num_examples: 1842388
download_size: 0
dataset_size: 121098396
---
# Dataset Card for "notebooks_on_the_hub_raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sarin2/lima2_cous | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5514
num_examples: 39
download_size: 2495
dataset_size: 5514
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CCCCCCChy/Myfirstdataset | ---
license: mit
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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] |
luizlzg/prefeitura_rj_v2 | ---
configs:
- config_name: default
data_files:
- split: train
path: prefeitura_treino*
- split: test
path: prefeitura_teste*
- split: validation
path: prefeitura_validacao*
--- |
Pampkinus/Mr-Beast | ---
license: openrail
---
Faceset of the youtuber MrBeast, 5252 images (JPG) |
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