datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
giorgoskyriacou2009tv/Itv-1 | ---
license: apache-2.0
---
|
Nietor/dpoo | ---
license: apache-2.0
---
|
Sprakbanken/nb_samtale | ---
language:
- nb
- nn
- 'no'
license: cc0-1.0
task_categories:
- automatic-speech-recognition
tags:
- dialects
- podcasts
- live-events
- conversational
- speech
---
# Dataset Card for Sprakbanken/nb_samtale
## Dataset Description
- **Homepage:** [nb.no/sprakbanken](https://www.nb.no/sprakbanken/)
- **Repository:** [Resource catalogue, no. 85](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-85/)
- **Paper:** [NB_Samtale_About_the_corpus.pdf](https://www.nb.no/sbfil/taledata/NB_Samtale_About_the_corpus.pdf)
- **Point of Contact:** [Språkbanken](mailto:sprakbanken@nb.no)
### Dataset Summary
NB Samtale is a speech corpus made by the Language Bank at the National Library of Norway. The corpus contains orthographically transcribed speech from podcasts and recordings of live events at the National Library. The corpus is intended as an open source dataset for Automatic Speech Recognition (ASR) development, and is specifically aimed at improving ASR systems’ handle on conversational speech.
The corpus consists of 12,080 segments, a total of 24 hours transcribed speech from 69 speakers. The corpus ensures both gender and dialect variation, and speakers from five broad dialect areas are represented. Both Bokmål and Nynorsk transcriptions are present in the corpus, with Nynorsk making up approximately 25% of the transcriptions.
We greatly appreciate feedback and suggestions for improvements.
### Supported Tasks
- Automatic Speech Recognition for verbatim transcriptions of conversational speech, as well as for standardised, orthographic transcriptions.
- Speaker Diarization: The sentence segments all have a speaker ID, which is unique per speaker, and the same speaker will have the same speaker ID across source files.
- Audio classification: Each segment could be classified with one of the metadata features.
### Languages
The transcription texts are in either Norwegian bokmål or Norwegian nynorsk.
The audio is in Norwegian, in the speakers' respective dialects.
We have categorized them into five dialect areas:
Dialect area (en) | Dialect area (nb) | Counties
--- | --- | ---
Eastern Norway | Østlandet | Agder, Innlandet, Oslo, Vestfold og Telemark, Viken
Southwest Norway | Sørvestlandet | Rogaland
Western Norway | Vestlandet | Møre og Romsdal, Vestland
Central Norway | Midt-Norge |Trøndelag
Northern Norway | Nord-Norge | Nordland, Troms og Finnmark
## Dataset Structure
### Data Instances
A data point is an audio segment, including a relative path to the `.wav`-file, and the transcription. Additional information is provided about the speaker, the orthographic standard for the transcription, whether the segment overlaps with the previous or next, and the setting for the recording. The transcription also comes in 3 different normalized versions: "orthographic" (orthographically correct text, with punctuation, integer numbers, and standardized word forms), "verbatim" (with tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation) and "annotations" (as is from the annotation process, with punctuation, tags, and alternate word forms).
```
{
'source_file_id': 'nb-1',
'segment_id': '0008970-0013860',
'segment_order': 0,
'duration': 4.89,
'overlap_previous': False,
'overlap_next': False,
'speaker_id': 'P36',
'gender': 1,
'dialect': 0,
'orthography': 0,
'source_type': 0,
'file_name': 'data/train/bm/nb-1_0008970-0013860.wav',
'transcription': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'annotations': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'orthographic': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'verbatim': 'hallo og velkommen hit til Nasjonalbiblioteket',
'audio': {
'path': "data/train/bm/nb-1_0008970-0013860.wav",
'array': array([-0.00033569, 0.00222778, -0.0005188 , ..., 0.00067139,
0.00057983, 0.0005188 ]),
'sampling_rate': 16000}
}
```
### Data Fields
data field | description | Value type / example
--- | --- | ---
`source_file_id` | original file the segment appears in. | e.g. `50f-X`, `tr-X` or `nb-X`, where X is a number. (str)
`segment_id` | segment start and end timestamp. | `{starttime}-{endtime}` (str)
`segment_order` | order of segment in the original file. | (int)
`duration` | duration of segment in seconds. | (float)
`overlap_previous` | whether the beginning of the segment overlaps with the previous segment | `True` or `False` (bool)
`overlap_next` | whether the end of the segment overlaps with the next segment. | `True` or `False` (bool)
`speaker_id` | speaker ID for the speaker transcribed in the segment. | `P0` - `P69` (str)
`gender` | speaker’s binary gender (female or male), mapped to a HuggingFace datasets ClassLabel index number | `0`: f or `1`: m (int)
`dialect` | the speaker’s dialect area, as a ClassLabel index number for the areas east (e), north (n), southwest (sw), central (t), west (w). | `0`: e, `1`: n, `2`: sw, `3`: t, or `4`: w (int)
`orthography` | the written norm of the transcription, either bokmål (`bm`) or nynorsk (`nn`) as a ClassLabel index number | `0`: bm or `1`: nn (int)
`source_type` | type of recording of original file, either `live-event` or `podcast`, as a ClassLabel index number | `0`: live-event or `1`: podcast (int)
`file_name` | file name of the audio segment, without the path | `{source_file_id}_{segment_id}.wav` (str)
`transcription` | orthographic transcription text | (str)
`orthographic` | close to orthographically correct text transcription in the given `orthography` standard. Contains punctuation, numbers, and standard word forms. | (str)
`verbatim` | transcription text mapping to the uttered words as close as possible. Contains tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation. | (str)
`annotations` | transcription text "as is" from the annotation process. Contains false starts, metatags for non-linguistic noises, punctuation, and alternate word forms (`<uttered word>\<orthographic standard word>`) | (str)
`audio` | the audio segment data, with the relative file `path`, the bytes `array`, and the `sampling_rate` | (dict)
"orthographic" (orthographically correct text, with punctuation, integer numbers, and standardized word forms), "verbatim" (with tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation) and "annotations" (as is from the annotation process, with punctuation, tags, and alternate word forms).
### Data Splits
The data is split into a `train`, `validation`, and `test` set, stratified on three parameters: source type, gender and dialect.
Gender and dialect naturally refers to the gender and dialect of the speakers.
The data has not been split on speaker ID to avoid speaker overlap in the various sets because this proved impossible
while still maintaining a decent distribution of the other parameters, especially dialect variation.
The source type refers to whether the source material is one of the two podcasts (50f, tr) or
a National Library live event (nb).
The two types have different features.
The podcasts are overall good quality studio recordings with little background noise, echo and such.
The live events are recorded in rooms or reception halls at the National Library and have more background
noise, echo and inconsistent audio quality.
Many also have a live audience.
## Dataset Creation
### Source data
The audio is collected from podcasts we have been permitted to share openly – namely 50
forskere from UiT and Trondheim kommunes podkast from Trondheim municipality – as well
as some of The National Library’s own recordings of live events. The podcasts are studio
recordings, while the National Library events take place in rooms and reception halls at the
National Library, sometimes in front of an audience.
#### Who are the source language producers?
Guests and hosts of the respective recording events, either podcasts produced in a studio or lectures, debates and conversations in a public live event.
### Annotations
#### Annotation process
The recordings were segmented and transcribed in the transcription software ELAN. The
recordings were transcribed automatically using a Norwegian ASR system created by the AI-
lab at the National Library of Norway. The speech was segmented and transcribed with
speaker diarization, separating the speakers into separate transcription tiers. These
segments and transcriptions were then manually corrected by a transcriber according to a
set of guidelines. All the manual transcriptions were reviewed by a second person in order to
avoid substantial discrepancies between transcribers. Finally all the transcriptions were
spell-checked, and checked for any unwanted numbers or special characters.
See the [official dataset documentation](https://www.nb.no/sbfil/taledata/NB_Samtale_About_the_corpus.pdf) for more details.
The full set of guidelines for segmentation and transcription are given in Norwegian in [NB_Samtale_transcription_guidelines.pdf](https://www.nb.no/sbfil/taledata/NB_Samtale_transcription_guidelines.pdf).
#### Who are the annotators?
The Norwegian Language Bank (Språkbanken).
### Personal and Sensitive Information
The data fields `gender`, `dialect` and `speaker_id` pertain to the speakers themselves.
A single speaker will have the same `speaker_id` if they appear in several different source files.
## Considerations for Using the Data
### Discussion of Biases
The recordings were for the most part selected based on the gender and dialect of the
speakers to ensure gender balance and broad dialectal representation. The corpus has a
near 50/50 divide between male and female speakers (male 54%, female 46%). The
Norwegian dialects have been divided into five broad dialect areas that are all represented in
the corpus. However, Eastern Norwegian has the greatest representation at about 50%
speaker time, while the other areas fall between 8% and 20% speaker time.
## Additional Information
### Dataset Curators
The content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway.
[Marie Iversdatter Røsok](mailto:marie.rosok@nb.no), [Ingerid Løyning Dale](mailto:ingerid.dale@nb.no) and [Per Erik Solberg](mailto:per.solberg@nb.no) contributed in creating this dataset.
Thanks to the HuggingFace team for assistance.
### Licensing Information
The NB Samtale dataset is released with the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/), i.e., it is public domain and can be used for any purpose and reshared without permission.
|
adzcai/genealogy_synthetic_v2 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer0
dtype: string
- name: answer1
dtype: string
- name: answer2
dtype: string
- name: answer3
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
splits:
- name: train
num_bytes: 512290.5
num_examples: 2112
- name: test
num_bytes: 170763.5
num_examples: 704
download_size: 222511
dataset_size: 683054.0
---
# Dataset Card for "genealogy_synthetic_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mpingale/mental-health-chat-dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: questionID
dtype: int64
- name: questionTitle
dtype: string
- name: questionText
dtype: string
- name: questionLink
dtype: string
- name: topic
dtype: string
- name: therapistInfo
dtype: string
- name: therapistURL
dtype: string
- name: answerText
dtype: string
- name: upvotes
dtype: int64
- name: views
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 7642829
num_examples: 2775
download_size: 3638427
dataset_size: 7642829
---
# Dataset Card for "mental-health-chat-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
teleprint-me/function-calling | ---
license: cc-by-nc-sa-4.0
---
# Function Calling Dataset
## Description
The Function Calling Dataset is designed to facilitate the training of natural language models to understand and generate structured function calls within a conversational context. This dataset contains a collection of interactions where users engage in conversations with a virtual assistant and request specific actions or information using structured function calls.
## Dataset Contents
- Regular Conversations: The dataset includes standard conversational cues where users ask questions, make requests, or seek information.
- Structured Function Calls: Using the `[FUNC]` tag, the dataset provides examples of how to generate structured outputs, representing function calls. These function calls encompass various tasks and actions.
- Marrying Structure and Natural Language: Following each function call, there are natural language explanations or elaborations. This ensures that the dataset strikes a balance between structured and human-friendly responses.
## Usage
This dataset is intended for training and fine-tuning natural language models, enabling them to understand and generate structured function calls in response to user queries. It can be valuable for projects involving virtual assistants, chatbots, or any application where structured actions are required within a conversational context.
## License
This dataset is made available under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
---
|
swaghjal/sherlock-val | ---
---
configs:
- config_name: default
data_files:
- split: val
path: sherlock_val*
--- |
CyberHarem/tsukuyo_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of tsukuyo/大野ツクヨ/月咏 (Blue Archive)
This is the dataset of tsukuyo/大野ツクヨ/月咏 (Blue Archive), containing 209 images and their tags.
The core tags of this character are `rabbit_ears, animal_ears, long_hair, lop_rabbit_ears, hair_over_one_eye, breasts, purple_eyes, rabbit_girl, halo, purple_hair, multicolored_hair, large_breasts, colored_inner_hair, blonde_hair, black_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 | 209 | 323.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukuyo_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 209 | 273.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukuyo_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 535 | 555.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukuyo_bluearchive/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/tsukuyo_bluearchive',
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, blush, crop_top, long_sleeves, open_cardigan, purple_cardigan, purple_scarf, see-through_cleavage, serafuku, simple_background, white_background, looking_at_viewer, solo, midriff, navel, ninja, red_neckerchief, shy, blue_sailor_collar, fishnet_top, blue_skirt, closed_mouth, pleated_skirt, bandaged_leg, side_slit, two-tone_hair, white_shirt |
| 1 | 9 |  |  |  |  |  | 1girl, bandaged_leg, blush, looking_at_viewer, ninja, open_cardigan, purple_cardigan, purple_scarf, see-through_cleavage, serafuku, simple_background, solo, long_sleeves, sailor_collar, white_background, pleated_skirt, red_neckerchief, fishnet_top, open_mouth, shy, side_slit |
| 2 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, navel, nipples, sex, solo_focus, bandaged_leg, penis, pussy, spread_legs, sweat, vaginal, censored, open_mouth, completely_nude |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | crop_top | long_sleeves | open_cardigan | purple_cardigan | purple_scarf | see-through_cleavage | serafuku | simple_background | white_background | looking_at_viewer | solo | midriff | navel | ninja | red_neckerchief | shy | blue_sailor_collar | fishnet_top | blue_skirt | closed_mouth | pleated_skirt | bandaged_leg | side_slit | two-tone_hair | white_shirt | sailor_collar | open_mouth | 1boy | hetero | nipples | sex | solo_focus | penis | pussy | spread_legs | sweat | vaginal | censored | completely_nude |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:---------------|:----------------|:------------------|:---------------|:-----------------------|:-----------|:--------------------|:-------------------|:--------------------|:-------|:----------|:--------|:--------|:------------------|:------|:---------------------|:--------------|:-------------|:---------------|:----------------|:---------------|:------------|:----------------|:--------------|:----------------|:-------------|:-------|:---------|:----------|:------|:-------------|:--------|:--------|:--------------|:--------|:----------|:-----------|:------------------|
| 0 | 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 | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | X | | | X | X | X | | X | | | X | X | X | | | X | X | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | | | | | | | | | | | | X | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
keremberke/pcb-defect-segmentation | ---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/pcb-defect-segmentation" src="https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']
```
### Number of Images
```json
{'valid': 25, 'train': 128, 'test': 36}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/pcb-defect-segmentation", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8](https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8?ref=roboflow2huggingface)
### Citation
```
@misc{ defects-2q87r_dataset,
title = { Defects Dataset },
type = { Open Source Dataset },
author = { Diplom },
howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } },
url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-27 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 189 images.
Defect are annotated in COCO format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
aswin1906/github-advisory-2022 | ---
license: apache-2.0
---
|
bigscience-data/roots_ca_wikibooks | ---
language: ca
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ca_wikibooks
# wikibooks_filtered
- Dataset uid: `wikibooks_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0897 % of total
- 0.2591 % of en
- 0.0965 % of fr
- 0.1691 % of es
- 0.2834 % of indic-hi
- 0.2172 % of pt
- 0.0149 % of zh
- 0.0279 % of ar
- 0.1374 % of vi
- 0.5025 % of id
- 0.3694 % of indic-ur
- 0.5744 % of eu
- 0.0769 % of ca
- 0.0519 % of indic-ta
- 0.1470 % of indic-mr
- 0.0751 % of indic-te
- 0.0156 % of indic-bn
- 0.0476 % of indic-ml
- 0.0087 % of indic-pa
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: id
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_id
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_eu
- dedup_template_soft
- replace_newline_with_space
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-mr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-te
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-bn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ml
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-pa
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
|
mlxen/squad_v1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346108
num_examples: 87599
download_size: 14457366
dataset_size: 79346108
---
# Dataset Card for "squad_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yonischeyer/sentencesNoInput | ---
license: unknown
---
|
InBf4/mt_2024 | ---
license: cc
---
|
kwonseungchan/ai_tod | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_caption
dtype: string
- name: satilte
dtype: image
splits:
- name: train
num_bytes: 2443710809.67
num_examples: 2194
download_size: 2464553150
dataset_size: 2443710809.67
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Falah/female_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 4060495
num_examples: 10000
download_size: 474495
dataset_size: 4060495
---
# Dataset Card for "female_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
euclaise/WritingPromptsX | ---
dataset_info:
features:
- name: post_title
dtype: string
- name: body
dtype: string
- name: score
dtype: int64
- name: gilded
dtype: int64
- name: post_score
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2040557544
num_examples: 1245546
download_size: 1016138545
dataset_size: 2040557544
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc0-1.0
size_categories:
- 1M<n<10M
---
# Dataset Card for "WritingPromptsX"
Comments from r/WritingPrompts, up to 12-2022, from PushShift. Inspired by [WritingPrompts](https://huggingface.co/datasets/euclaise/writingprompts), but a bit more complete. |
NickKolok/regs-lametta-beastboost | ---
license: agpl-3.0
---
|
Back-up/dang_dg_content | ---
dataset_info:
features:
- name: id
dtype: string
- name: _index
dtype: string
- name: _score
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 401537
num_examples: 2000
download_size: 137703
dataset_size: 401537
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Reza-Madani/bert-mini-predictor-output | ---
dataset_info:
features:
- name: predictions
dtype: int64
splits:
- name: train
num_bytes: 78592
num_examples: 9824
download_size: 6138
dataset_size: 78592
---
# Dataset Card for "bert-mini-predictor-output"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nenma/autotrain-data-multiconer2-test1 | ---
task_categories:
- token-classification
---
# AutoTrain Dataset for project: multiconer2-test1
## Dataset Description
This dataset has been automatically processed by AutoTrain for project multiconer2-test1.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"robert",
"gottschalk",
"1939",
"academy",
"award",
"winner",
"and",
"founder",
"of",
"panavision"
],
"tags": [
18,
49,
62,
29,
60,
62,
62,
62,
62,
16
]
},
{
"tokens": [
"during",
"the",
"reign",
"of",
"the",
"tongzhi",
"emperor",
"(",
"r",
".",
"1861",
"\u2013",
"1875",
")",
":"
],
"tags": [
62,
62,
62,
62,
62,
18,
49,
62,
62,
62,
62,
62,
62,
62,
62
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(num_classes=63, names=['B-AnatomicalStructure', 'B-ArtWork', 'B-Artist', 'B-Athlete', 'B-CarManufacturer', 'B-Cleric', 'B-Clothing', 'B-Disease', 'B-Drink', 'B-Facility', 'B-Food', 'B-HumanSettlement', 'B-MedicalProcedure', 'B-Medication/Vaccine', 'B-MusicalGRP', 'B-MusicalWork', 'B-ORG', 'B-OtherLOC', 'B-OtherPER', 'B-OtherPROD', 'B-Politician', 'B-PrivateCorp', 'B-PublicCorp', 'B-Scientist', 'B-Software', 'B-SportsGRP', 'B-SportsManager', 'B-Station', 'B-Vehicle', 'B-VisualWork', 'B-WrittenWork', 'I-AnatomicalStructure', 'I-ArtWork', 'I-Artist', 'I-Athlete', 'I-CarManufacturer', 'I-Cleric', 'I-Clothing', 'I-Disease', 'I-Drink', 'I-Facility', 'I-Food', 'I-HumanSettlement', 'I-MedicalProcedure', 'I-Medication/Vaccine', 'I-MusicalGRP', 'I-MusicalWork', 'I-ORG', 'I-OtherLOC', 'I-OtherPER', 'I-OtherPROD', 'I-Politician', 'I-PrivateCorp', 'I-PublicCorp', 'I-Scientist', 'I-Software', 'I-SportsGRP', 'I-SportsManager', 'I-Station', 'I-Vehicle', 'I-VisualWork', 'I-WrittenWork', 'O'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2169 |
| valid | 829 |
|
autoevaluate/autoeval-eval-squad_v2-squad_v2-5d46e4-1992966290 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-base-cased-squad2
metrics: ['accuracy', 'bleu', 'precision', 'recall', 'rouge']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-base-cased-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@anchal](https://huggingface.co/anchal) for evaluating this model. |
shellypeng/cartoon-captioned-datasets-salesforce-blip | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2357028047.718
num_examples: 1907
download_size: 1774680464
dataset_size: 2357028047.718
tags:
- code
---
# Dataset Card for "cartoon-captioned-datasets-salesforce-blip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DFKI-SLT/cross_re | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CrossRE is a cross-domain dataset for relation extraction
size_categories:
- 10K<n<100K
source_datasets:
- extended|cross_ner
tags:
- cross domain
- ai
- news
- music
- literature
- politics
- science
task_categories:
- text-classification
task_ids:
- multi-class-classification
dataset_info:
- config_name: ai
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
dtype: int32
- name: entity-type
dtype: string
- name: relations
sequence:
- name: id_1-start
dtype: int32
- name: id_1-end
dtype: int32
- name: id_2-start
dtype: int32
- name: id_2-end
dtype: int32
- name: relation-type
dtype: string
- name: Exp
dtype: string
- name: Un
dtype: bool
- name: SA
dtype: bool
splits:
- name: train
num_bytes: 62411
num_examples: 100
- name: validation
num_bytes: 183717
num_examples: 350
- name: test
num_bytes: 217353
num_examples: 431
download_size: 508107
dataset_size: 463481
- config_name: literature
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
dtype: int32
- name: entity-type
dtype: string
- name: relations
sequence:
- name: id_1-start
dtype: int32
- name: id_1-end
dtype: int32
- name: id_2-start
dtype: int32
- name: id_2-end
dtype: int32
- name: relation-type
dtype: string
- name: Exp
dtype: string
- name: Un
dtype: bool
- name: SA
dtype: bool
splits:
- name: train
num_bytes: 62699
num_examples: 100
- name: validation
num_bytes: 246214
num_examples: 400
- name: test
num_bytes: 264450
num_examples: 416
download_size: 635130
dataset_size: 573363
- config_name: music
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
dtype: int32
- name: entity-type
dtype: string
- name: relations
sequence:
- name: id_1-start
dtype: int32
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dtype: int32
- name: id_2-start
dtype: int32
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dtype: int32
- name: relation-type
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- name: Exp
dtype: string
- name: Un
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- name: SA
dtype: bool
splits:
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num_examples: 100
- name: validation
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num_examples: 350
- name: test
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num_examples: 399
download_size: 726956
dataset_size: 643508
- config_name: news
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
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- name: entity-type
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- name: relations
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- name: id_1-start
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- name: id_2-start
dtype: int32
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dtype: int32
- name: relation-type
dtype: string
- name: Exp
dtype: string
- name: Un
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- name: SA
dtype: bool
splits:
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num_examples: 164
- name: validation
num_bytes: 77952
num_examples: 350
- name: test
num_bytes: 96301
num_examples: 400
download_size: 239763
dataset_size: 223355
- config_name: politics
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
dtype: int32
- name: entity-type
dtype: string
- name: relations
sequence:
- name: id_1-start
dtype: int32
- name: id_1-end
dtype: int32
- name: id_2-start
dtype: int32
- name: id_2-end
dtype: int32
- name: relation-type
dtype: string
- name: Exp
dtype: string
- name: Un
dtype: bool
- name: SA
dtype: bool
splits:
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num_examples: 101
- name: validation
num_bytes: 277633
num_examples: 350
- name: test
num_bytes: 295294
num_examples: 400
download_size: 726427
dataset_size: 648931
- config_name: science
features:
- name: doc_key
dtype: string
- name: sentence
sequence: string
- name: ner
sequence:
- name: id-start
dtype: int32
- name: id-end
dtype: int32
- name: entity-type
dtype: string
- name: relations
sequence:
- name: id_1-start
dtype: int32
- name: id_1-end
dtype: int32
- name: id_2-start
dtype: int32
- name: id_2-end
dtype: int32
- name: relation-type
dtype: string
- name: Exp
dtype: string
- name: Un
dtype: bool
- name: SA
dtype: bool
splits:
- name: train
num_bytes: 63876
num_examples: 103
- name: validation
num_bytes: 224402
num_examples: 351
- name: test
num_bytes: 249075
num_examples: 400
download_size: 594058
dataset_size: 537353
---
# Dataset Card for CrossRE
## 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
- **Repository:** [CrossRE](https://github.com/mainlp/CrossRE)
- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)
### Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.
For details, see the paper: https://arxiv.org/abs/2210.09345
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in CrossRE is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### news
- **Size of downloaded dataset files:** 0.24 MB
- **Size of the generated dataset:** 0.22 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "news-train-1",
"sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner": [
{"id-start": 0, "id-end": 0, "entity-type": "organisation"},
{"id-start": 2, "id-end": 3, "entity-type": "misc"},
{"id-start": 6, "id-end": 7, "entity-type": "misc"}
],
"relations": [
{"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
]
}
```
#### politics
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.65 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "politics-train-1",
"sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner": [
{"id-start": 8, "id-end": 9, "entity-type": "politicalparty"},
{"id-start": 15, "id-end": 20, "entity-type": "politicalparty"},
{"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
],
"relations": [
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False},
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
]
}
```
#### science
- **Size of downloaded dataset files:** 0.59 MB
- **Size of the generated dataset:** 0.54 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "science-train-1",
"sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner": [
{"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"},
{"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"},
{"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
],
"relations": []
}
```
#### music
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.64 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "music-train-1",
"sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner": [
{"id-start": 4, "id-end": 6, "entity-type": "location"},
{"id-start": 13, "id-end": 17, "entity-type": "event"}
],
"relations": [
{"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
]
}
```
#### literature
- **Size of downloaded dataset files:** 0.64 MB
- **Size of the generated dataset:** 0.57 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "literature-train-1",
"sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner": [
{"id-start": 7, "id-end": 9, "entity-type": "person"},
{"id-start": 12, "id-end": 13, "entity-type": "country"},
{"id-start": 17, "id-end": 18, "entity-type": "writer"},
{"id-start": 20, "id-end": 20, "entity-type": "writer"},
{"id-start": 26, "id-end": 27, "entity-type": "writer"},
{"id-start": 29, "id-end": 29, "entity-type": "writer"},
{"id-start": 33, "id-end": 33, "entity-type": "country"},
{"id-start": 35, "id-end": 35, "entity-type": "country"},
{"id-start": 38, "id-end": 38, "entity-type": "misc"},
{"id-start": 45, "id-end": 46, "entity-type": "person"},
{"id-start": 49, "id-end": 50, "entity-type": "misc"},
{"id-start": 55, "id-end": 55, "entity-type": "person"}
],
"relations": [
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False},
{"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False},
{"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False},
{"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
]
}
```
#### ai
- **Size of downloaded dataset files:** 0.51 MB
- **Size of the generated dataset:** 0.46 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "ai-train-1",
"sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner": [
{"id-start": 3, "id-end": 5, "entity-type": "product"},
{"id-start": 10, "id-end": 11, "entity-type": "field"},
{"id-start": 13, "id-end": 14, "entity-type": "task"},
{"id-start": 16, "id-end": 17, "entity-type": "task"},
{"id-start": 21, "id-end": 23, "entity-type": "task"},
{"id-start": 26, "id-end": 27, "entity-type": "field"},
{"id-start": 28, "id-end": 29, "entity-type": "researcher"},
{"id-start": 31, "id-end": 32, "entity-type": "researcher"},
{"id-start": 34, "id-end": 35, "entity-type": "researcher"},
{"id-start": 37, "id-end": 38, "entity-type": "researcher"},
{"id-start": 40, "id-end": 41, "entity-type": "researcher"},
{"id-start": 43, "id-end": 44, "entity-type": "researcher"},
{"id-start": 46, "id-end": 47, "entity-type": "researcher"},
{"id-start": 49, "id-end": 50, "entity-type": "researcher"}
],
"relations": [
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
]
}
```
### Data Fields
The data fields are the same among all splits.
- `doc_key`: the instance id of this sentence, a `string` feature.
- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
- `id-start`: the start index of the entity, a `int` feature.
- `id-end`: the end index of the entity, a `int` feature.
- `entity-type`: the type of the entity, a `string` feature.
- `relations`: the list of relations in this sentence, a `list` of `dict` features.
- `id_1-start`: the start index of the first entity, a `int` feature.
- `id_1-end`: the end index of the first entity, a `int` feature.
- `id_2-start`: the start index of the second entity, a `int` feature.
- `id_2-end`: the end index of the second entity, a `int` feature.
- `relation-type`: the type of the relation, a `string` feature.
- `Exp`: the explanation of the relation type assigned, a `string` feature.
- `Un`: uncertainty of the annotator, a `bool` feature.
- `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.
### Data Splits
#### Sentences
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 164 | 350 | 400 | 914 |
| politics | 101 | 350 | 400 | 851 |
| science | 103 | 351 | 400 | 854 |
| music | 100 | 350 | 399 | 849 |
| literature | 100 | 400 | 416 | 916 |
| ai | 100 | 350 | 431 | 881 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 668 | 2,151 | 2,46 | 5,265 |
#### Relations
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 175 | 300 | 396 | 871 |
| politics | 502 | 1,616 | 1,831 | 3,949 |
| science | 355 | 1,340 | 1,393 | 3,088 |
| music | 496 | 1,861 | 2,333 | 4,690 |
| literature | 397 | 1,539 | 1,591 | 3,527 |
| ai | 350 | 1,006 | 1,127 | 2,483 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 2,275 | 7,662 | 8,671 | 18,608 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{bassignana-plank-2022-crossre,
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
author = "Bassignana, Elisa and Plank, Barbara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics"
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. |
wild-chimpanzee-foundation/test | ---
dataset_info:
features:
- name: video
dtype: binary
- name: filename
dtype: string
splits:
- name: train
num_bytes: 6320274
num_examples: 5
download_size: 6278175
dataset_size: 6320274
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/fd1e16b9 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1338
dataset_size: 180
---
# Dataset Card for "fd1e16b9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pa-shk/synthetic_sberquad_zero | ---
dataset_info:
- config_name: docs
features:
- name: doc
dtype: string
splits:
- name: train
num_bytes: 18450007
num_examples: 13489
download_size: 9682240
dataset_size: 18450007
- config_name: qrels
features:
- name: query
dtype: string
- name: doc
dtype: string
- name: log_prob
dtype: float64
- name: finish_reason
dtype: string
splits:
- name: train
num_bytes: 60066567
num_examples: 40467
download_size: 11853439
dataset_size: 60066567
configs:
- config_name: docs
data_files:
- split: train
path: docs/train-*
- config_name: qrels
data_files:
- split: train
path: qrels/train-*
---
|
gryffindor-ISWS/fictional_characters_raw_data_with_images | ---
license: gpl-3.0
language:
- en
--- |
birgermoell/Italian_Parkinsons_Voice_and_Speech | ---
license: cc-by-4.0
language:
- it
---
The original dataset is located [here](https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speech)
The citation for this dataset:
```bibtex
@data{aw6b-tg17-19,
doi = {10.21227/aw6b-tg17},
url = {https://dx.doi.org/10.21227/aw6b-tg17},
author = {Dimauro, Giovanni and Girardi, Francesco},
publisher = {IEEE Dataport},
title = {Italian Parkinson's Voice and Speech},
year = {2019}
}
```
The author of the dataset requests that academic users of the dataset cite the following articles, the latter of which describes how the dataset was created:
```bibtex
@INPROCEEDINGS{7533761,
author={Dimauro, Giovanni and Caivano, Danilo and Bevilacqua, Vitoantonio and Girardi, Francesco and Napoletano, Vito},
booktitle={2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA)},
title={VoxTester, software for digital evaluation of speech changes in Parkinson disease},
year={2016},
volume={},
number={},
pages={1-6},
doi={10.1109/MeMeA.2016.7533761}
}
@ARTICLE{8070308,
author={Dimauro, Giovanni and Di Nicola, Vincenzo and Bevilacqua, Vitoantonio and Caivano, Danilo and Girardi, Francesco},
journal={IEEE Access},
title={Assessment of Speech Intelligibility in Parkinson’s Disease Using a Speech-To-Text System},
year={2017},
volume={5},
number={},
pages={22199-22208},
doi={10.1109/ACCESS.2017.2762475}
}
``` |
DBQ/Loro.Piana.Product.prices.Germany | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Loro Piana - Product-level price list
tags:
- webscraping
- ecommerce
- Loro Piana
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 390968
num_examples: 1133
download_size: 130740
dataset_size: 390968
---
# Loro Piana web scraped data
## About the website
Loro Piana operates in the **luxury fashion industry** within the Europe, Middle East, and Africa (EMEA) region, with a strong market presence in **Germany**. The luxury fashion market in this region is characterized by a high demand for **high-quality materials, craftsmanship**, and **unique designs**. The industry has seen a substantial growth in **online retail**, or **Ecommerce**, with an increasing number of consumers turning to online platforms for convenience and a wider range of options. Specifically, the dataset observed has information about the **Ecommerce product-list page (PLP)** data for Loro Piana in Germany, offering insights into their online market performance.
## Link to **dataset**
[Germany - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20Germany/r/recKScQaTx24JbIkH)
|
sorbhet/llamadata | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3628709
num_examples: 5000
download_size: 2149583
dataset_size: 3628709
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Elysr/assessedClinicalTrial | ---
task_categories:
- question-answering
- text-classification
tags:
- microbiome
- clinical trials
- translational research
- eligibility
size_categories:
- n<1K
---
The dataset centers around clinical research involving microbiome-based therapeutics, aiming to match patients with appropriate trials.
The Question contains a patient description (with interest in microbiome-based interventional trials) and the details of a clinical trial.
The Chosen answer indicates if the trial is relevant for the patient, and if the patient is likely eligible. Optionally, a note might explain the chosen answer.
**Relevance**:
* Relevant: based on the patient description, the clinician will discuss this trial with the patient.
* May be Relevant: the clinician finds aspects of a particular trial that could potentially be pertinent to the patient's situation, but not necessarily for immediate enrollment into the trial and it does not fully match the search focus. It shows ongoing research efforts exploring new therapies that could potentially benefit the patient in the future.
* Not Relevant: it is clear that this trial is not relevant for the patient.
**Patient Eligibility**:
* Eligible: yes, based on the information provided in the patient description and the clinical trial eligibility criteria, we can assert that the patient is likely eligibile to enroll in the clinical trial.
* Not eligible: no, the patient is not eligible for this trial.
* Unknown: there is not enough information to conclude if the patient is likely eligible to enroll in the trial.
* Not Assessed: the eligibility of the patient to enroll in the trial was not assessed. |
sam1120/parking-terrain | ---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 180279364.0
num_examples: 65
download_size: 51908456
dataset_size: 180279364.0
---
# Dataset Card for "parking-terrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/hatebr_por_Latn_to_cat_Latn | ---
dataset_info:
features:
- name: instagram_comments
dtype: string
- name: offensive_language
dtype: bool
- name: offensiveness_levels
dtype: int32
- name: antisemitism
dtype: bool
- name: apology_for_the_dictatorship
dtype: bool
- name: fatphobia
dtype: bool
- name: homophobia
dtype: bool
- name: partyism
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
- name: offensive_&_non-hate_speech
dtype: bool
- name: non-offensive
dtype: bool
- name: specialist_1_hate_speech
dtype: bool
- name: specialist_2_hate_speech
dtype: bool
- name: specialist_3_hate_speech
dtype: bool
splits:
- name: train
num_bytes: 381784
num_examples: 4480
- name: validation
num_bytes: 83822
num_examples: 1120
- name: test
num_bytes: 105619
num_examples: 1400
download_size: 0
dataset_size: 571225
---
# Dataset Card for "hatebr_por_Latn_to_cat_Latn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sigdelsanjog/learnushealthcare | ---
license: apache-2.0
language:
- en
tags:
- ushealthcare
- healthcare
--- |
airesearch/CMDF_VISTEC | ---
license: cc-by-4.0
---
# Financial Text Data Collection
This repository contains a collection of raw financial text data. The dataset encompasses a wide range of financial documents, including annual reports, news articles, and social media posts
## Dataset Description
The dataset is organized into the following categories:
- **Annual Reports**: Financial reports issued by companies on an annual basis, providing insights into their financial performance and strategic outlook. (แบบแสดงรายการข้อมูลประจำปี (แบบ 56-1))
- **News Articles**: Articles from reputable financial news sources
- **Social Media and Online Forums**: Text from Internet Users
## Usage
Researchers, data scientists, and developers can utilize this dataset for training langauge models as well as building corpora.
## Data Sources
The data in this repository has been sourced from publicly available sources, including but not limited to:
- Financial news websites
- Regulatory agencies
- Social Media
## License
The dataset is provided under the CC BY 4.0 License, allowing for unrestricted use and modification for both commercial and non-commercial purposes. However, users are encouraged to provide attribution to this repository if the data is used in their work.
## Contribution
Contributions to this repository are welcome. If you have additional financial text data that you would like to contribute, please submit a pull request following the guidelines outlined in [https://github.com/vistec-AI/capital_market_text_data/](https://github.com/vistec-AI/capital_market_text_data/).
## Acknowledgements
We acknowledge the sources and providers of the data included in this repository, without whom this collection would not be possible.
## Contact
For questions, suggestions, or inquiries regarding this dataset, please contact canu_pro@vistec.ac.th or open an issue in the repository. We appreciate your feedback and contributions. |
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145229 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: ['accuracy', 'rouge', 'mse']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model. |
huynguyen208/test_data | ---
license: unknown
---
|
rkhilnani96/srk_images | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 226593.0
num_examples: 7
download_size: 227211
dataset_size: 226593.0
---
# Dataset Card for "srk_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
juliojfdghdg/asdad | ---
license: openrail
---
|
Imran1/newdatasetdog | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Australian_shepherd
'1': Chihuahua
'2': French_bulldog
splits:
- name: train
num_bytes: 44691364.514
num_examples: 1786
download_size: 42351460
dataset_size: 44691364.514
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
calabi-yau-data/ws-5d | ---
license: cc-by-sa-4.0
pretty_name: Weight Systems Defining Five-Dimensional IP Lattice Polytopes
configs:
- config_name: non-reflexive
data_files:
- split: full
path: non-reflexive/*.parquet
- config_name: reflexive
data_files:
- split: full
path: reflexive/*.parquet
size_categories:
- 100B<n<1T
tags:
- physics
- math
---
# Weight Systems Defining Five-Dimensional IP Lattice Polytopes
This dataset contains all weight systems defining five-dimensional reflexive and
non-reflexive IP lattice polytopes, instrumental in the study of Calabi-Yau fourfolds in
mathematics and theoretical physics. The data was compiled by Harald Skarke and Friedrich
Schöller in [arXiv:1808.02422](https://arxiv.org/abs/1808.02422). More information is
available at the [Calabi-Yau data website](http://hep.itp.tuwien.ac.at/~kreuzer/CY/). The
dataset can be explored using the [search
frontend](http://rgc.itp.tuwien.ac.at/fourfolds/). See below for a short mathematical
exposition on the construction of polytopes.
Please cite the paper when referencing this dataset:
```
@article{Scholler:2018apc,
author = {Schöller, Friedrich and Skarke, Harald},
title = "{All Weight Systems for Calabi-Yau Fourfolds from Reflexive Polyhedra}",
eprint = "1808.02422",
archivePrefix = "arXiv",
primaryClass = "hep-th",
doi = "10.1007/s00220-019-03331-9",
journal = "Commun. Math. Phys.",
volume = "372",
number = "2",
pages = "657--678",
year = "2019"
}
```
## Dataset Details
The dataset consists of two subsets: weight systems defining reflexive (and therefore IP)
polytopes and weight systems defining non-reflexive IP polytopes. Each subset is split
into 4000 files in Parquet format. Rows within each file are sorted lexicographically by
weights. There are 185,269,499,015 weight systems defining reflexive polytopes and
137,114,261,915 defining non-reflexive polytopes, making a total of 322,383,760,930 IP
weight systems.
Each row in the dataset represents a polytope and contains the six weights defining it,
along with the vertex count, facet count, and lattice point count. The reflexive dataset
also includes the Hodge numbers \\( h^{1,1} \\), \\( h^{1,2} \\), and \\( h^{1,3} \\) of
the corresponding Calabi-Yau manifold, and the lattice point count of the dual polytope.
For any Calabi-Yau fourfold, the Euler characteristic \\( \chi \\) and the Hodge number
\\( h^{2,2} \\) can be derived as follows:
$$ \chi = 48 + 6 (h^{1,1} − h^{1,2} + h^{1,3}) $$
$$ h^{2,2} = 44 + 4 h^{1,1} − 2 h^{1,2} + 4 h^{1,3} $$
This dataset is licensed under the
[CC BY-SA 4.0 license](http://creativecommons.org/licenses/by-sa/4.0/).
### Data Fields
- `weight0` to `weight5`: Weights of the weight system defining the polytope.
- `vertex_count`: Vertex count of the polytope.
- `facet_count`: Facet count of the polytope.
- `point_count`: Lattice point count of the polytope.
- `dual_point_count`: Lattice point count of the dual polytope (only for reflexive
polytopes).
- `h11`: Hodge number \\( h^{1,1} \\) (only for reflexive polytopes).
- `h12`: Hodge number \\( h^{1,2} \\) (only for reflexive polytopes).
- `h13`: Hodge number \\( h^{1,3} \\) (only for reflexive polytopes).
## Usage
The dataset can be used without downloading it entirely, thanks to the streaming
capability of the `datasets` library. The following Python code snippet demonstrates how
to stream the dataset and print the first five rows:
```python
from datasets import load_dataset
dataset = load_dataset("calabi-yau-data/ws-5d", name="reflexive", split="full", streaming=True)
for row in dataset.take(5):
print(row)
```
When cloning the Git repository with Git Large File Storage (LFS), data files are stored
both in the Git LFS storage directory and in the working tree. To avoid occupying double
the disk space, use a filesystem that supports copy-on-write, and run the following
commands to clone the repository:
```bash
# Initialize Git LFS
git lfs install
# Clone the repository without downloading LFS files immediately
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/calabi-yau-data/ws-5d
# Change to the repository directory
cd ws-5d
# Test deduplication (optional)
git lfs dedup --test
# Download the LFS files
git lfs fetch
# Create working tree files as clones of the files in the Git LFS storage directory using
# copy-on-write functionality
git lfs dedup
```
## Construction of Polytopes
This is an introduction to the mathematics involved in the construction of polytopes
relevant to this dataset. For more details and precise definitions, consult the paper
[arXiv:1808.02422](https://arxiv.org/abs/1808.02422) and references therein.
### Polytopes
A polytope is the convex hull of a finite set of points in \\(n\\)-dimensional Euclidean
space, \\(\mathbb{R}^n\\). This means it is the smallest convex shape that contains all
these points. The minimal collection of points that define a particular polytope are its
vertices. Familiar examples of polytopes include triangles and rectangles in two
dimensions, and cubes and octahedra in three dimensions.
A polytope is considered an *IP polytope* (interior point polytope) if the origin of
\\(\mathbb{R}^n\\) is in the interior of the polytope, not on its boundary or outside it.
For any IP polytope \\(\nabla\\), its dual polytope \\(\nabla^*\\) is defined as the set
of points \\(\mathbf{y}\\) satisfying
$$
\mathbf{x} \cdot \mathbf{y}
\ge -1 \quad \text{for all } \mathbf{x} \in \nabla \;.
$$
This relationship is symmetric: the dual of the dual of an IP polytope is the polytope
itself, i.e., \\( \nabla^{**} = \nabla \\).
### Weight Systems
Weight systems provide a means to describe simple polytopes known as *simplices*. A weight
system is a tuple of real numbers. The construction process is outlined as follows:
Consider an \\(n\\)-dimensional simplex in \\(\mathbb{R}^n\\), i.e., a polytope in
\\(\mathbb{R}^n\\) with vertex count \\(n + 1\\) and \\(n\\) of its edges extending in
linearly independent directions. It is possible to position \\(n\\) of its vertices at
arbitrary (linearly independent) locations through a linear transformation. The placement
of the remaining vertex is then determined. Its position is the defining property of the
simplex. To specify the position independently of the applied linear transformation, one
can use the following equation. If \\(\mathbf{v}_0, \mathbf{v}_1, \dots, \mathbf{v}_n\\)
are the vertices of the simplex, this relation fixes one vertex in terms of the other
\\(n\\):
$$ \sum_{i=0}^n q_i \mathbf{v}_i = 0 \;, $$
where \\(q_i\\) is the tuple of real numbers, the weight system.
It is important to note that scaling all weights in a weight system by a common factor
results in an equivalent weight system that defines the same simplex.
The condition that a simplex is an IP simplex is equivalent to the condition that all
weights in its weight system are bigger than zero.
For this dataset, the focus is on a specific construction of lattice polytopes described
in subsequent sections.
### Lattice Polytopes
A lattice polytope is a polytope with vertices at the points of a regular grid, or
lattice. Using linear transformations, any lattice polytope can be transformed so that its
vertices have integer coordinates, hence they are also referred to as integral
polytopes.
The dual of a lattice with points \\(L\\) is the lattice consisting of all points
\\(\mathbf{y}\\) that satisfy
$$
\mathbf{x} \cdot \mathbf{y} \in \mathbb{Z} \quad \text{for all } \mathbf{x} \in L \;.
$$
*Reflexive polytopes* are a specific type of lattice polytope characterized by having a
dual that is also a lattice polytope, with vertices situated on the dual lattice. These
polytopes play a central role in the context of this dataset.
The weights of a lattice polytope are always rational. This characteristic enables the
rescaling of a weight system so that its weights become integers without any common
divisor. This rescaling has been performed in this dataset.
The construction of the lattice polytopes from this dataset works as follows: We start
with the simplex \\(\nabla\\), arising from a weight system as previously described. Then,
we define the polytope \\(\Delta\\) as the convex hull of the intersection of
\\(\nabla^*\\) with the points of the dual lattice. In the context of this dataset, the
polytope \\(\Delta\\) is referred to as ‘the polytope’. Correspondingly,
\\(\Delta^{\!*}\\) is referred to as ‘the dual polytope’. The lattice of \\(\nabla\\) and
\\(\Delta^{\!*}\\) is taken to be the coarsest lattice possible, such that \\(\nabla\\) is
a lattice polytope, i.e., the lattice generated by the vertices of \\(\nabla\\). This
construction is exemplified in the following sections.
A weight system is considered an IP weight system if the corresponding \\(\Delta\\) is an
IP polytope; that is, the origin is within its interior. Since only IP polytopes have
corresponding dual polytopes, this condition is essential for the polytope \\(\Delta\\) to
be classified as reflexive.
### Two Dimensions
In two dimensions, all IP weight systems define reflexive polytopes and every vertex of
\\(\nabla^*\\) lies on the dual lattice, making \\(\Delta\\) and \\(\nabla^*\\) identical.
There are exactly three IP weight systems that define two-dimensional polytopes
(polygons). Each polytope is reflexive and has three vertices and three facets (edges):
| weight system | number of points of \\(\nabla\\) | number of points of \\(\nabla^*\\) |
|--------------:|---------------------------------:|-----------------------------------:|
| (1, 1, 1) | 4 | 10 |
| (1, 1, 2) | 5 | 9 |
| (1, 2, 3) | 7 | 7 |
The polytopes and their duals are depicted below. Lattice points are indicated by dots.
<img src="pictures/ws-2d.png" style="display: block; margin-left: auto; margin-right: auto; width:520px;">
### General Dimension
In higher dimensions, the situation becomes more complex. Not all IP polytopes are
reflexive, and generally, \\(\Delta \neq \nabla^*\\).
This example shows the construction of the three-dimensional polytope \\(\Delta\\) with
weight system (2, 3, 4, 5) and its dual \\(\Delta^{\!*}\\). Lattice points lying on the
polytopes are indicated by dots. \\(\Delta\\) has 7 vertices and 13 lattice points,
\\(\Delta^{\!*}\\) also has 7 vertices, but 16 lattice points.
<img src="pictures/ws-3d-2-3-4-5.png" style="display: block; margin-left: auto; margin-right: auto; width:450px;">
The counts of reflexive single-weight-system polytopes by dimension \\(n\\) are:
| \\(n\\) | reflexive single-weight-system polytopes |
|--------:|-----------------------------------------:|
| 2 | 3 |
| 3 | 95 |
| 4 | 184,026 |
| 5 | (this dataset) 185,269,499,015 |
One should note that distinct weight systems may well lead to the same polytope (we have
not checked how often this occurs). In particular it seems that polytopes with a small
number of lattice points are generated many times.
|
ai4bharat/IndicWikiBio | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- hi
- kn
- ml
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicWikiBio
size_categories:
- 1960<n<11,502
source_datasets:
- none. Originally generated from www.wikimedia.org.
task_categories:
- conditional-text-generation
task_ids:
- conditional-text-generation-other-wikibio
---
# Dataset Card for "IndicWikiBio"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [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://indicnlp.ai4bharat.org/indicnlg-suite
- **Paper:** [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437)
- **Point of Contact:**
### Dataset Summary
The WikiBio dataset released as part of IndicNLG Suite. Each
example has four fields: id, infobox, serialized infobox and summary. We create this dataset in nine
languages including as, bn, hi, kn, ml, or, pa, ta, te. The total
size of the dataset is 57,426.
### Supported Tasks and Leaderboards
**Tasks:** WikiBio
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
## Dataset Structure
### Data Instances
One random example from the `hi` dataset is given below in JSON format.
```
{
"id": 26,
"infobox": "name_1:सी॰\tname_2:एल॰\tname_3:रुआला\toffice_1:सांसद\toffice_2:-\toffice_3:मिजोरम\toffice_4:लोक\toffice_5:सभा\toffice_6:निर्वाचन\toffice_7:क्षेत्र\toffice_8:।\toffice_9:मिजोरम\tterm_1:2014\tterm_2:से\tterm_3:2019\tnationality_1:भारतीय",
"serialized_infobox": "<TAG> name </TAG> सी॰ एल॰ रुआला <TAG> office </TAG> सांसद - मिजोरम लोक सभा निर्वाचन क्षेत्र । मिजोरम <TAG> term </TAG> 2014 से 2019 <TAG> nationality </TAG> भारतीय",
"summary": "सी॰ एल॰ रुआला भारत की सोलहवीं लोक सभा के सांसद हैं।"
}
```
### Data Fields
- `id (string)`: Unique identifier.
- `infobox (string)`: Raw Infobox.
- `serialized_infobox (string)`: Serialized Infobox as input.
- `summary (string)`: Summary of Infobox/First line of Wikipedia page.
### Data Splits
Here is the number of samples in each split for all the languages.
Language | ISO 639-1 Code | Train | Test | Val |
---------- | ---------- | ---------- | ---------- | ---------- |
Assamese | as | 1,300 | 391 | 381 |
Bengali | bn | 4,615 | 1,521 | 1,567 |
Hindi | hi | 5,684 | 1,919 | 1,853 |
Kannada | kn | 1,188 | 389 | 383 |
Malayalam | ml | 5,620 | 1,835 | 1,896 |
Oriya | or | 1,687 | 558 | 515 |
Punjabi | pa | 3,796 | 1,227 | 1,331 |
Tamil | ta | 8,169 | 2,701 | 2,632 |
Telugu | te | 2,594 | 854 | 820 |
## Dataset Creation
### Curation Rationale
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
### Source Data
None
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
### Annotations
[More information needed]
#### 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
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437",
```
### Contributions
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_0.3_seed_2 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
- name: input
dtype: string
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struct:
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- name: max_new_tokens
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- name: pad_token_id
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- name: top_k
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- name: top_p
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- name: reward_1
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- name: n_samples
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- name: reject_select
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- name: prompt
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- name: chosen
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- name: rejected
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- name: index
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- name: filtered_epoch
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- name: gen_reward
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- name: gen_response
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download_size: 699077423
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configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
sachith-surge/LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: source
dtype: string
- name: response
dtype: string
- name: llama2_status
dtype: string
- name: llama2_rating
dtype: string
- name: llama2_reason
dtype: string
splits:
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num_bytes: 2241878
num_examples: 1505
download_size: 1173351
dataset_size: 2241878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NickyNicky/function-calling_chatml_gemma_v1 | ---
dataset_info:
features:
- name: Text
dtype: string
- name: len_tokens
dtype: int64
splits:
- name: train
num_bytes: 290843143
num_examples: 100188
download_size: 100748916
dataset_size: 290843143
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
language:
- en
---
## take dataset.
```
hiyouga/glaive-function-calling-v2-sharegpt
```
## image tokens (Min: 60 Max: 2099).
## format gemma template.
```
<bos><start_of_turn>system
You are a helpful assistant with access to the following functions.
Use them if required:
<tool>
[
{
"name": "convert_currency",
"description": "Convert amount from one currency to another",
"parameters": {
"type": "object",
"properties": {
"amount": {
"type": "number",
"description": "The amount to convert"
},
"from_currency": {
"type": "string",
"description": "The currency to convert from"
},
"to_currency": {
"type": "string",
"description": "The currency to convert to"
}
},
"required": [
"amount",
"from_currency",
"to_currency"
]
}
}
]
</tool>
To use these functions respond with:
<function_call> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </function_call>
Contains properties essential for the model to respond according to the tasks:
<observation> {"arg_1": "value_1", "arg_2": "value_2", "arg_3": "value_3", ...} </observation>
Edge cases you must handle:
- If there are no functions that match the user request, you will respond politely that you cannot help.
<end_of_turn>
<start_of_turn>user
Hi, I need to convert 500 US dollars to Euros. Can you help me with that?<end_of_turn>
<start_of_turn>function_call
<function_call> {"name": "convert_currency", "arguments": {"amount": 500, "from_currency": "USD", "to_currency": "EUR"}} </function_call><end_of_turn>
<start_of_turn>observation
<observation> {"converted_amount": 425.50, "from_currency": "USD", "to_currency": "EUR"} </observation><end_of_turn>
<start_of_turn>model
Sure, 500 US dollars is approximately 425.50 Euros.<end_of_turn><eos>
==============================
==============================
==============================
==============================
<bos><start_of_turn>system
You are a helpful AI assistant.
with not access to the following functions.<end_of_turn>
<start_of_turn>user
List 5 specific mammals found only in the boreal forests of North America, that are strictly nocturnal and have a minimum lifespan of 15 years. Make sure that the selected mammals belong to different orders and families, and that they are not commonly known or frequently mentioned in popular media or publications. Additionally, provide a brief description of their habitat and feeding patterns, and include any unique physiological adaptations that allow them to survive in this environment.<end_of_turn>
<start_of_turn>model
1. Fisher (Martes pennanti) - The fisher is a carnivorous mammal that belongs to the family Mustelidae. They are found in the boreal forests of North America and are strictly nocturnal. Fishers are known for their agility and are able to climb trees to hunt prey such as squirrels and rabbits. They also feed on carrion and berries. Fishers have thick fur that helps them survive in the cold boreal forest environment.
2. Northern flying squirrel (Glaucomys sabrinus) - The northern flying squirrel is a small mammal that belongs to the family Sciuridae. They are strictly nocturnal and are found in the boreal forests of North America. They have a unique adaptation that allows them to glide through the air using a membrane of skin called a patagium. Northern flying squirrels feed on a variety of foods including nuts, seeds, and insects.
3. Moose (Alces alces) - The moose is a large herbivorous mammal that belongs to the family Cervidae. They are found in the boreal forests of North America and are strictly nocturnal. Moose feed on a variety of plants including shrubs, leaves, and bark. They have a unique adaptation in their long legs that allows them to walk through deep snow.
4. Wolverine (Gulo gulo) - The wolverine is a carnivorous mammal that belongs to the family Mustelidae. They are found in the boreal forests of North America and are strictly nocturnal. Wolverines are known for their strength and are able to take down prey much larger than themselves. They also feed on carrion and berries. Wolverines have thick fur and large paws that help them survive in the cold boreal forest environment.
5. Red fox (Vulpes vulpes) - The red fox is a carnivorous mammal that belongs to the family Canidae. They are found in the boreal forests of North America and are strictly nocturnal. Red foxes are known for their intelligence and are able to adapt to a variety of environments. They feed on a variety of prey including rodents, birds, and insects. Red foxes have a unique adaptation in their long bushy tail that helps them balance and communicate with other foxes.<end_of_turn>
<start_of_turn>user
Can you explain the concept of quantum entanglement in simple terms?<end_of_turn>
<start_of_turn>model
Quantum entanglement is a phenomenon where two particles become connected in a way that their states are correlated. This means that if you measure the state of one particle, it will instantaneously affect the state of the other particle, even if they are separated by large distances. This happens because the particles are in a superposition of states, meaning they exist in all possible states simultaneously until measured. When one particle is measured and collapses into a definite state, the entangled particle collapses into the opposite state. It's like having two coins that always end up with opposite sides when flipped, no matter how far apart they are.<end_of_turn>
<start_of_turn>user
How does the blockchain work, and what makes it secure?<end_of_turn>
<start_of_turn>model
Blockchain is a decentralized digital ledger that records transactions in a secure and transparent manner. Think of it as a chain of blocks, where each block contains a record of several transactions. These transactions are verified and added to the blockchain in a process called mining. Mining involves solving complex mathematical problems which require significant computational power. Once a problem is solved, the miner is rewarded with cryptocurrency. This incentivizes miners to maintain the security and integrity of the network.
What makes blockchain secure is its distributed nature. Each block is linked to the previous block, creating an unbreakable chain of records. Once a block is added to the chain, it cannot be altered without invalidating the entire chain. Additionally, each block in the chain contains a unique code called a hash, which ensures that any changes made to the block are easily detectable. Finally, the fact that the blockchain is maintained by a network of nodes instead of a single entity makes it resistant to attacks and manipulation.<end_of_turn><eos>
==============================
==============================
==============================
==============================
<bos><start_of_turn>system
You are a helpful AI assistant.
with not access to the following functions.<end_of_turn>
<start_of_turn>user
Create a sentence that starts with the word “industry”.<end_of_turn>
<start_of_turn>model
Industry experts predict a shift towards renewable energy sources in the coming years.<end_of_turn>
<start_of_turn>user
Can you explain the difference between deductive and inductive reasoning?<end_of_turn>
<start_of_turn>model
Deductive reasoning is a logical process where a conclusion is drawn from general premises, while inductive reasoning involves drawing a conclusion based on specific observations or evidence.<end_of_turn>
<start_of_turn>user
How can we improve critical thinking skills?<end_of_turn>
<start_of_turn>model
Some ways to improve critical thinking skills include practicing active listening, questioning assumptions, considering multiple perspectives, and engaging in reflective thinking.<end_of_turn>
<start_of_turn>user
What is the difference between correlation and causation?<end_of_turn>
<start_of_turn>model
Correlation refers to a relationship between two variables, while causation implies that one variable directly causes the other.<end_of_turn>
<start_of_turn>user
What are some ways to reduce carbon emissions?<end_of_turn>
<start_of_turn>model
Some ways to reduce carbon emissions include using renewable energy sources, improving energy efficiency, reducing waste, and promoting sustainable transportation. Additionally, individuals can make a difference by making small changes in their daily lives such as turning off lights when not in use, reducing meat consumption, and opting for public transportation or carpooling.<end_of_turn><eos>
==============================
==============================
==============================
==============================
<bos><start_of_turn>system
You are a helpful assistant with access to the following functions.
Use them if required:
<tool>
[
{
"name": "calculate_bmi",
"description": "Calculate the Body Mass Index (BMI)",
"parameters": {
"type": "object",
"properties": {
"weight": {
"type": "number",
"description": "The weight in kilograms"
},
"height": {
"type": "number",
"description": "The height in meters"
}
},
"required": [
"weight",
"height"
]
}
},
{
"name": "check_word_count",
"description": "Check the word count of a given text",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to count the words"
}
},
"required": [
"text"
]
}
}
]
</tool>
To use these functions respond with:
<function_call> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": "value_2", ...}} </function_call>
Contains properties essential for the model to respond according to the tasks:
<observation> {"arg_1": "value_1", "arg_2": "value_2", "arg_3": "value_3", ...} </observation>
Edge cases you must handle:
- If there are no functions that match the user request, you will respond politely that you cannot help.
<end_of_turn>
<start_of_turn>user
Hi, I would like to calculate my BMI. I weigh 70 kilograms and my height is 1.75 meters.<end_of_turn>
<start_of_turn>function_call
<function_call> {"name": "calculate_bmi", "arguments": {"weight": 70, "height": 1.75}} </function_call><end_of_turn>
<start_of_turn>observation
<observation> {"bmi": 22.86} </observation><end_of_turn>
<start_of_turn>model
Your Body Mass Index (BMI) is 22.86. This is considered to be within the healthy weight range.<end_of_turn>
<start_of_turn>user
That's great to hear! What about my friend? He weighs 85 kilograms and is 1.8 meters tall.<end_of_turn>
<start_of_turn>function_call
<function_call> {"name": "calculate_bmi", "arguments": {"weight": 85, "height": 1.8}} </function_call><end_of_turn>
<start_of_turn>observation
<observation> {"bmi": 26.23} </observation><end_of_turn>
<start_of_turn>model
Your friend's Body Mass Index (BMI) is 26.23. This is considered to be slightly overweight.<end_of_turn>
<start_of_turn>user
Oh, I see. Thanks for the information.<end_of_turn>
<start_of_turn>model
You're welcome! If you have any other questions, feel free to ask.<end_of_turn><eos>
```` |
kortukov/ae-gpt4-hotpotqa | ---
license: apache-2.0
---
|
nmdr/mini-physics-100 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 174361
num_examples: 100
download_size: 99349
dataset_size: 174361
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sachith-surge/orca-evaluated-falcon-gpt4-v1 | ---
dataset_info:
features:
- name: original_index
dtype: int64
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
- name: system_message
dtype: string
- name: explained_targets
dtype: string
- name: dataset_source
dtype: string
- name: falcon_status
dtype: string
- name: falcon_rating
dtype: string
- name: falcon_reason
dtype: string
- name: gpt4_status
dtype: string
- name: gpt4_rating
dtype: string
- name: gpt4_reason
dtype: string
splits:
- name: train
num_bytes: 4239431
num_examples: 2000
download_size: 1958334
dataset_size: 4239431
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "orca-evaluated-falcon-gpt4-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
renumics/mnist-outlier | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-nist
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: mnist
pretty_name: MNIST
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
- name: embedding_foundation
sequence: float32
- name: embedding_ft
sequence: float32
- name: outlier_score_ft
dtype: float64
- name: outlier_score_foundation
dtype: float64
- name: nn_image
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
splits:
- name: train
num_bytes: 404136444.0
num_examples: 60000
download_size: 472581433
dataset_size: 404136444.0
---
# Dataset Card for "mnist-outlier"
📚 This dataset is an enriched version of the [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).
The workflow is described in the medium article: [Changes of Embeddings during Fine-Tuning of Transformers](https://medium.com/@markus.stoll/changes-of-embeddings-during-fine-tuning-c22aa1615921).
## Explore the Dataset
The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) allows you to explorer this dataset. You can find a Hugging Face Space running Spotlight with this dataset here: <https://huggingface.co/spaces/renumics/mnist-outlier>.
Or you can explorer it locally:
```python
!pip install renumics-spotlight datasets
from renumics import spotlight
import datasets
ds = datasets.load_dataset("renumics/mnist-outlier", split="train")
df = ds.rename_columns({"label":"labels"}).to_pandas()
df["label_str"] = df["labels"].apply(lambda x: ds.features["label"].int2str(x))
dtypes = {
"nn_image": spotlight.Image,
"image": spotlight.Image,
"embedding_ft": spotlight.Embedding,
"embedding_foundation": spotlight.Embedding,
}
spotlight.show(
df,
dtype=dtypes,
layout="https://spotlight.renumics.com/resources/layout_pre_post_ft.json",
)
``` |
Salama1429/tarteel-ai-QuranQA | ---
annotations_creators:
- expert-generated
language:
- ar
language_creators:
- expert-generated
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: Qur'anic Reading Comprehension Dataset
size_categories:
- n<1K
- 1K<n<10K
source_datasets:
- original
tags:
- quran
- qa
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for the Qur'anic Reading Comprehension Dataset (QRCD)
## 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://sites.google.com/view/quran-qa-2022/home
- **Repository:** https://gitlab.com/bigirqu/quranqa/-/tree/main/
- **Paper:** https://dl.acm.org/doi/10.1145/3400396
- **Leaderboard:**
- **Point of Contact:** @piraka9011
### Dataset Summary
The QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are
coupled with their extracted answers to constitute 1,337 question-passage-answer triplets.
### Supported Tasks and Leaderboards
This task is evaluated as a ranking task.
To give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully
match one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure.
It is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching.
pRR is the official evaluation measure of this shared task.
We will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer.
The EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the
gold answers.
Whereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer.
To get an overall evaluation score, each of the above measures is averaged over all questions.
### Languages
Qur'anic Arabic
## Dataset Structure
### Data Instances
To simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain
one or more answers to that question, as shown below:
```json
{
"pq_id": "38:41-44_105",
"passage": "واذكر عبدنا أيوب إذ نادى ربه أني مسني الشيطان بنصب وعذاب. اركض برجلك هذا مغتسل بارد وشراب. ووهبنا له أهله ومثلهم معهم رحمة منا وذكرى لأولي الألباب. وخذ بيدك ضغثا فاضرب به ولا تحنث إنا وجدناه صابرا نعم العبد إنه أواب.",
"surah": 38,
"verses": "41-44",
"question": "من هو النبي المعروف بالصبر؟",
"answers": [
{
"text": "أيوب",
"start_char": 12
}
]
}
```
Each Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different
question.
Likewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a
different Qur’anic passage.
The source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the
Holy Qur'an in several scripting styles.
We have chosen the simple-clean text style of Tanzil version 1.0.2.
### Data Fields
* `pq_id`: Sample ID
* `passage`: Context text
* `surah`: Surah number
* `verses`: Verse range
* `question`: Question text
* `answers`: List of answers and their start character
### Data Splits
| **Dataset** | **%** | **# Question-Passage Pairs** | **# Question-Passage-Answer Triplets** |
|-------------|:-----:|:-----------------------------:|:---------------------------------------:|
| Training | 65% | 710 | 861 |
| Development | 10% | 109 | 128 |
| Test | 25% | 274 | 348 |
| All | 100% | 1,093 | 1,337 |
## 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
The QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License https://creativecommons.org/licenses/by-nd/4.0/legalcode
For a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to https://creativecommons.org/licenses/by-nd/4.0/
### Citation Information
```
@article{malhas2020ayatec,
author = {Malhas, Rana and Elsayed, Tamer},
title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an},
year = {2020},
issue_date = {November 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {19},
number = {6},
issn = {2375-4699},
url = {https://doi.org/10.1145/3400396},
doi = {10.1145/3400396},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {oct},
articleno = {78},
numpages = {21},
keywords = {evaluation, Classical Arabic}
}
```
### Contributions
Thanks to [@piraka9011](https://github.com/piraka9011) for adding this dataset.
|
dongyu0205/working-memory-capacity-of-ChatGPT | ---
license: mit
---
- arXiv: https://arxiv.org/abs/2305.03731
- Github repo: [Working Memory Capacity of ChatGPT: An Empirical Study](https://github.com/Daniel-Gong/ChatGPT_WM) |
rebhyyy/ahmed | ---
license: mit
---
|
mteb/sts13-sts | ---
language:
- en
--- |
ioxil/imagenetsubset | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 6003374.0
num_examples: 150
download_size: 6003372
dataset_size: 6003374.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "imagenetsubset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PeacefulData/Robust-HyPoradise | ---
license: apache-2.0
language_creators:
- expert-generated
task_categories:
- text-generation
tags:
- generative error correction
- large language model
- LLaMA
pretty_name: Robust HyPoradise
size_categories:
- 100K<n<1M
language:
- en
---
# HypothesesParadise
This repo releases the Robust HyPoradise dataset in paper "Large Language Models are Efficient Learners of Noise-Robust Speech Recognition."
If you consider this work would be related or useful for your research, please kindly consider to cite the work in ICLR 2024. Thank you.
```bib
@inproceedings{hu2024large,
title={Large Language Models are Efficient Learners of Noise-Robust Speech Recognition},
author={Hu, Yuchen and Chen, Chen and Yang, Chao-Han Huck and Li, Ruizhe and Zhang, Chao and Chen, Pin-Yu and Chng, Eng Siong},
booktitle={International Conference on Learning Representations},
year={2024}
}
``` |
Phando/SlimPajama-val-sampled | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: redpajama_set_name
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: validation
num_bytes: 39109042
num_examples: 9347
download_size: 23001780
dataset_size: 39109042
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
DZN222/teste1 | ---
license: openrail
---
|
KaioSan/Lucas-iluminismo | ---
license: openrail
---
|
learn3r/SDG_scimed | ---
dataset_info:
features:
- name: jargon
dtype: string
- name: definition
dtype: string
splits:
- name: train
num_bytes: 45723
num_examples: 200
download_size: 29274
dataset_size: 45723
---
# Dataset Card for "SDG_scimed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
egm517/hupd_augmented | ---
language:
- en
license:
- cc-by-sa-4.0
task_categories:
- fill-mask
- summarization
- text-classification
- token-classification
task_ids:
- masked-language-modeling
- multi-class-classification
- topic-classification
- named-entity-recognition
pretty_name: "HUPD"
tags:
- patents
---
# Dataset Card for The Harvard USPTO Patent Dataset (HUPD)
## Dataset Description
- **Homepage:** [https://patentdataset.org/](https://patentdataset.org/)
- **Repository:** [HUPD GitHub repository](https://github.com/suzgunmirac/hupd)
- **Paper:** [HUPD arXiv Submission](https://arxiv.org/abs/2207.04043)
- **Point of Contact:** Mirac Suzgun
### Dataset Summary
The Harvard USPTO Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus of English-language utility patent applications filed to the United States Patent and Trademark Office (USPTO) between January 2004 and December 2018.
### Experiments and Tasks Considered in the Paper
- **Patent Acceptance Prediction**: Given a section of a patent application (in particular, the abstract, claims, or description), predict whether the application will be accepted by the USPTO.
- **Automated Subject (IPC/CPC) Classification**: Predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.
- **Language Modeling**: Masked/autoregressive language modeling on the claims and description sections of patent applications.
- **Abstractive Summarization**: Given the claims or claims section of a patent application, generate the abstract.
### Languages
The dataset contains English text only.
### Domain
Patents (intellectual property).
### Dataset Curators
The dataset was created by Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber.
## Dataset Structure
Each patent application is defined by a distinct JSON file, named after its application number, and includes information about
the application and publication numbers,
title,
decision status,
filing and publication dates,
primary and secondary classification codes,
inventor(s),
examiner,
attorney,
abstract,
claims,
background,
summary, and
full description of the proposed invention, among other fields. There are also supplementary variables, such as the small-entity indicator (which denotes whether the applicant is considered to be a small entity by the USPTO) and the foreign-filing indicator (which denotes whether the application was originally filed in a foreign country).
In total, there are 34 data fields for each application. A full list of data fields used in the dataset is listed in the next section.
### Data Instances
Each patent application in our patent dataset is defined by a distinct JSON file (e.g., ``8914308.json``), named after its unique application number. The format of the JSON files is as follows:
```python
{
"application_number": "...",
"publication_number": "...",
"title": "...",
"decision": "...",
"date_produced": "...",
"date_published": "...",
"main_cpc_label": "...",
"cpc_labels": ["...", "...", "..."],
"main_ipcr_label": "...",
"ipcr_labels": ["...", "...", "..."],
"patent_number": "...",
"filing_date": "...",
"patent_issue_date": "...",
"abandon_date": "...",
"uspc_class": "...",
"uspc_subclass": "...",
"examiner_id": "...",
"examiner_name_last": "...",
"examiner_name_first": "...",
"examiner_name_middle": "...",
"inventor_list": [
{
"inventor_name_last": "...",
"inventor_name_first": "...",
"inventor_city": "...",
"inventor_state": "...",
"inventor_country": "..."
}
],
"abstract": "...",
"claims": "...",
"background": "...",
"summary": "...",
"full_description": "..."
}
```
## Usage
### Loading the Dataset
#### Sample (January 2016 Subset)
The following command can be used to load the `sample` version of the dataset, which contains all the patent applications that were filed to the USPTO during the month of January in 2016. This small subset of the dataset can be used for debugging and exploration purposes.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='sample',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
train_filing_start_date='2016-01-01',
train_filing_end_date='2016-01-21',
val_filing_start_date='2016-01-22',
val_filing_end_date='2016-01-31',
)
```
#### Full Dataset
If you would like to use the **full** version of the dataset, please make sure that change the `name` field from `sample` to `all`, specify the training and validation start and end dates carefully, and set `force_extract` to be `True` (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='all',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
force_extract=True,
train_filing_start_date='2011-01-01',
train_filing_end_date='2016-12-31',
val_filing_start_date='2017-01-01',
val_filing_end_date='2017-12-31',
)
```
### Google Colab Notebook
You can also use the following Google Colab notebooks to explore HUPD.
- [](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)[ HUPD Examples: Loading the Dataset](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Loading HUPD By Using HuggingFace's Libraries](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD DistilRoBERTa Model](https://colab.research.google.com/drive/11t69BWcAVXndQxAOCpKaGkKkEYJSfydT?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD T5-Small Summarization Model](https://colab.research.google.com/drive/1VkCtrRIryzev_ixDjmJcfJNK-q6Vx24y?usp=sharing)
## Dataset Creation
### Source Data
HUPD synthesizes multiple data sources from the USPTO: While the full patent application texts were obtained from the USPTO Bulk Data Storage System (Patent Application Data/XML Versions 4.0, 4.1, 4.2, 4.3, 4.4 ICE, as well as Version 1.5) as XML files, the bibliographic filing metadata were obtained from the USPTO Patent Examination Research Dataset (in February, 2021).
### Annotations
Beyond our patent decision label, for which construction details are provided in the paper, the dataset does not contain any human-written or computer-generated annotations beyond those produced by patent applicants or the USPTO.
### Data Shift
A major feature of HUPD is its structure, which allows it to demonstrate the evolution of concepts over time. As we illustrate in the paper, the criteria for patent acceptance evolve over time at different rates, depending on category. We believe this is an important feature of the dataset, not only because of the social scientific questions it raises, but also because it facilitates research on models that can accommodate concept shift in a real-world setting.
### Personal and Sensitive Information
The dataset contains information about the inventor(s) and examiner of each patent application. These details are, however, already in the public domain and available on the USPTO's Patent Application Information Retrieval (PAIR) system, as well as on Google Patents and PatentsView.
### Social Impact of the Dataset
The authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in [the paper](https://arxiv.org/abs/2207.04043).
### Impact on Underserved Communities and Discussion of Biases
The dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.
### Discussion of Biases
Section 4 of [the HUPD paper](https://arxiv.org/abs/2207.04043) provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.
- Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.
### Licensing Information
HUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International.
### Citation Information
```
@article{suzgun2022hupd,
title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K. and Kominers, Scott Duke and Shieber, Stuart M.},
year={2022},
publisher={arXiv preprint arXiv:2207.04043},
url={https://arxiv.org/abs/2207.04043},
``` |
wx44wx/three-kingdoms-blip-captions | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 20776352
num_examples: 883
download_size: 0
dataset_size: 20776352
license: cc-by-sa-4.0
language:
- en
tags:
- art
pretty_name: Three Kingdoms BLIP caption
size_categories:
- n<1K
---
# Dataset Card for "Three Kingdoms BLIP caption"
_Dataset used to train [Three Kingdoms text to image model](https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning)_
The original images were obtained from [https://kongming.net/](https://kongming.net/11/portraits/) and captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Examples
> a man with a crown on his head
> a woman in a green dress
> a man with a feather on his head
## Citation
If you use this dataset, please cite it as:
```
@misc{xin2023threekingdoms,
author = {Xin Wang},
title = {Three Kingdoms BLIP captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/wx44wx/three-kingdoms-blip-captions/}}
}
``` |
open-llm-leaderboard/details_LordNoah__Alpaca_refine_gpt2_e1_se0 | ---
pretty_name: Evaluation run of LordNoah/Alpaca_refine_gpt2_e1_se0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [LordNoah/Alpaca_refine_gpt2_e1_se0](https://huggingface.co/LordNoah/Alpaca_refine_gpt2_e1_se0)\
\ 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_LordNoah__Alpaca_refine_gpt2_e1_se0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-23T02:16:01.366997](https://huggingface.co/datasets/open-llm-leaderboard/details_LordNoah__Alpaca_refine_gpt2_e1_se0/blob/main/results_2024-01-23T02-16-01.366997.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\": 0.26714671218496217,\n\
\ \"acc_stderr\": 0.031235793020424174,\n \"acc_norm\": 0.26841740481590964,\n\
\ \"acc_norm_stderr\": 0.03206394031126415,\n \"mc1\": 0.21542227662178703,\n\
\ \"mc1_stderr\": 0.014391902652427674,\n \"mc2\": 0.37277525939261963,\n\
\ \"mc2_stderr\": 0.014133518715389181\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.26109215017064846,\n \"acc_stderr\": 0.012835523909473848,\n\
\ \"acc_norm\": 0.27303754266211605,\n \"acc_norm_stderr\": 0.013019332762635744\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3663612826130253,\n\
\ \"acc_stderr\": 0.0048082512696824295,\n \"acc_norm\": 0.4538936466839275,\n\
\ \"acc_norm_stderr\": 0.004968521608065469\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n\
\ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n\
\ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.03690677986137282,\n\
\ \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.03690677986137282\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n\
\ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.3320754716981132,\n \"acc_stderr\": 0.028985455652334388,\n\
\ \"acc_norm\": 0.3320754716981132,\n \"acc_norm_stderr\": 0.028985455652334388\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\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\"\
: 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24855491329479767,\n\
\ \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.24855491329479767,\n\
\ \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n\
\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.2978723404255319,\n \"acc_stderr\": 0.029896145682095462,\n\
\ \"acc_norm\": 0.2978723404255319,\n \"acc_norm_stderr\": 0.029896145682095462\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.30344827586206896,\n \"acc_stderr\": 0.038312260488503336,\n\
\ \"acc_norm\": 0.30344827586206896,\n \"acc_norm_stderr\": 0.038312260488503336\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2804232804232804,\n \"acc_stderr\": 0.023135287974325635,\n \"\
acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.023135287974325635\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1984126984126984,\n\
\ \"acc_stderr\": 0.035670166752768635,\n \"acc_norm\": 0.1984126984126984,\n\
\ \"acc_norm_stderr\": 0.035670166752768635\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.25161290322580643,\n \"acc_stderr\": 0.024685979286239956,\n \"\
acc_norm\": 0.25161290322580643,\n \"acc_norm_stderr\": 0.024685979286239956\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n \"\
acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.03453131801885415,\n\
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.03453131801885415\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.3484848484848485,\n \"acc_stderr\": 0.033948539651564025,\n \"\
acc_norm\": 0.3484848484848485,\n \"acc_norm_stderr\": 0.033948539651564025\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.29533678756476683,\n \"acc_stderr\": 0.03292296639155139,\n\
\ \"acc_norm\": 0.29533678756476683,\n \"acc_norm_stderr\": 0.03292296639155139\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.36153846153846153,\n \"acc_stderr\": 0.024359581465396987,\n\
\ \"acc_norm\": 0.36153846153846153,\n \"acc_norm_stderr\": 0.024359581465396987\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514566,\n \
\ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514566\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\
: 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.30642201834862387,\n\
\ \"acc_stderr\": 0.019765517220458523,\n \"acc_norm\": 0.30642201834862387,\n\
\ \"acc_norm_stderr\": 0.019765517220458523\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.029157522184605603,\n\
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.029157522184605603\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.28431372549019607,\n \"acc_stderr\": 0.031660096793998116,\n \"\
acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.031660096793998116\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.28270042194092826,\n \"acc_stderr\": 0.029312814153955917,\n \
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.029312814153955917\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.1210762331838565,\n\
\ \"acc_stderr\": 0.021894174113185737,\n \"acc_norm\": 0.1210762331838565,\n\
\ \"acc_norm_stderr\": 0.021894174113185737\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.3140495867768595,\n \"acc_stderr\": 0.042369647530410184,\n \"\
acc_norm\": 0.3140495867768595,\n \"acc_norm_stderr\": 0.042369647530410184\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\": 0.3067484662576687,\n \"acc_stderr\": 0.036230899157241474,\n\
\ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.036230899157241474\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\
\ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\
\ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.36893203883495146,\n \"acc_stderr\": 0.047776151811567386,\n\
\ \"acc_norm\": 0.36893203883495146,\n \"acc_norm_stderr\": 0.047776151811567386\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23931623931623933,\n\
\ \"acc_stderr\": 0.027951826808924336,\n \"acc_norm\": 0.23931623931623933,\n\
\ \"acc_norm_stderr\": 0.027951826808924336\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.20051085568326948,\n\
\ \"acc_stderr\": 0.014317653708594206,\n \"acc_norm\": 0.20051085568326948,\n\
\ \"acc_norm_stderr\": 0.014317653708594206\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.28901734104046245,\n \"acc_stderr\": 0.024405173935783238,\n\
\ \"acc_norm\": 0.28901734104046245,\n \"acc_norm_stderr\": 0.024405173935783238\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\
\ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\
\ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982478,\n\
\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982478\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.29260450160771706,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.29260450160771706,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.023788583551658533,\n\
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.023788583551658533\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.24822695035460993,\n \"acc_stderr\": 0.025770015644290396,\n \
\ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290396\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24445893089960888,\n\
\ \"acc_stderr\": 0.010976425013113897,\n \"acc_norm\": 0.24445893089960888,\n\
\ \"acc_norm_stderr\": 0.010976425013113897\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.19852941176470587,\n \"acc_stderr\": 0.024231013370541114,\n\
\ \"acc_norm\": 0.19852941176470587,\n \"acc_norm_stderr\": 0.024231013370541114\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.238562091503268,\n \"acc_stderr\": 0.017242385828779613,\n \
\ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.017242385828779613\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\
\ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\
\ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2163265306122449,\n \"acc_stderr\": 0.02635891633490403,\n\
\ \"acc_norm\": 0.2163265306122449,\n \"acc_norm_stderr\": 0.02635891633490403\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2736318407960199,\n\
\ \"acc_stderr\": 0.03152439186555401,\n \"acc_norm\": 0.2736318407960199,\n\
\ \"acc_norm_stderr\": 0.03152439186555401\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.21084337349397592,\n\
\ \"acc_stderr\": 0.031755547866299194,\n \"acc_norm\": 0.21084337349397592,\n\
\ \"acc_norm_stderr\": 0.031755547866299194\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.29239766081871343,\n \"acc_stderr\": 0.034886477134579215,\n\
\ \"acc_norm\": 0.29239766081871343,\n \"acc_norm_stderr\": 0.034886477134579215\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.21542227662178703,\n\
\ \"mc1_stderr\": 0.014391902652427674,\n \"mc2\": 0.37277525939261963,\n\
\ \"mc2_stderr\": 0.014133518715389181\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5588003157063931,\n \"acc_stderr\": 0.013954975072834726\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/LordNoah/Alpaca_refine_gpt2_e1_se0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|arc:challenge|25_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|gsm8k|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hellaswag|10_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T02-16-01.366997.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T02-16-01.366997.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- '**/details_harness|winogrande|5_2024-01-23T02-16-01.366997.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-23T02-16-01.366997.parquet'
- config_name: results
data_files:
- split: 2024_01_23T02_16_01.366997
path:
- results_2024-01-23T02-16-01.366997.parquet
- split: latest
path:
- results_2024-01-23T02-16-01.366997.parquet
---
# Dataset Card for Evaluation run of LordNoah/Alpaca_refine_gpt2_e1_se0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [LordNoah/Alpaca_refine_gpt2_e1_se0](https://huggingface.co/LordNoah/Alpaca_refine_gpt2_e1_se0) 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_LordNoah__Alpaca_refine_gpt2_e1_se0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-23T02:16:01.366997](https://huggingface.co/datasets/open-llm-leaderboard/details_LordNoah__Alpaca_refine_gpt2_e1_se0/blob/main/results_2024-01-23T02-16-01.366997.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": 0.26714671218496217,
"acc_stderr": 0.031235793020424174,
"acc_norm": 0.26841740481590964,
"acc_norm_stderr": 0.03206394031126415,
"mc1": 0.21542227662178703,
"mc1_stderr": 0.014391902652427674,
"mc2": 0.37277525939261963,
"mc2_stderr": 0.014133518715389181
},
"harness|arc:challenge|25": {
"acc": 0.26109215017064846,
"acc_stderr": 0.012835523909473848,
"acc_norm": 0.27303754266211605,
"acc_norm_stderr": 0.013019332762635744
},
"harness|hellaswag|10": {
"acc": 0.3663612826130253,
"acc_stderr": 0.0048082512696824295,
"acc_norm": 0.4538936466839275,
"acc_norm_stderr": 0.004968521608065469
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768081,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768081
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.03690677986137282,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.03690677986137282
},
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
SahilSN/ChatBot_v5 | ---
license: llama2
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 20543
num_examples: 92
download_size: 10158
dataset_size: 20543
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ryanrwatkins/prompts_archive | ---
license: cc-by-4.0
---
Read me. |
eugenesiow/PIRM | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: PIRM
tags:
- other-image-super-resolution
---
# Dataset Card for PIRM
## 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://github.com/roimehrez/PIRM2018
- **Repository**: https://huggingface.co/datasets/eugenesiow/PIRM
- **Paper**: https://arxiv.org/abs/1809.07517
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing.
These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc.
Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM
challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/PIRM', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_HR/1.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_LR_x2/1.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|test|
|-------|---:|---:|
|bicubic_x2|100|100|
|bicubic_x3|100|100|
|bicubic_x4|100|100|
|unknown_x4|100|100|
## 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
No annotations.
#### Who are the annotators?
No annotators.
### 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
- **Original Authors**: [Blau et al. (2018)](https://arxiv.org/abs/1809.07517)
### Licensing Information
This dataset is published under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```bibtex
@misc{blau20192018,
title={The 2018 PIRM Challenge on Perceptual Image Super-resolution},
author={Yochai Blau and Roey Mechrez and Radu Timofte and Tomer Michaeli and Lihi Zelnik-Manor},
year={2019},
eprint={1809.07517},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
|
Sesamoo/5column | ---
license: unknown
---
|
CognitiveLab/hh-rlhf-formatted-60000 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 90271988
num_examples: 60000
download_size: 47495432
dataset_size: 90271988
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Nexdata/20011_Image_Caption_Data_Of_OCR_In_Natural_Scenes | ---
license: cc-by-nc-nd-4.0
---
## Description
20,000 Image Caption Data Of OCR In Natural Scenes, including Asian and European languages, a total of 14 languages, the collection environment includes shop plaques, stop signs, posters, road signs and other scenes, including a variety of shooting angles. The description language is English, which mainly describes the text arrangement, text content, color and other information.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1288?source=Huggingface
## Data size
20,000 pictures, 20,000 descriptions
## Language distribution
Asian languages: Korean, Indonesian, Malay, Vietnamese, Thai, Chinese, Japanese European languages: French, German, Italian, Portuguese, Russian, Spanish, English
## Collection environment
including store plaques, stop signs, posters, road signs, prompts and other scenes
## Collection diversity
including 14 languages, various natural scenes, and multiple shooting angles
## Data format
image format is .jpg, text format is .txt
## Collection equipment
mobile phone, camera
## Description language
English
## Text length
in principle, 30~60 words, usually 3-5 sentences
## Main description content
text arrangement, text content, color, scene
## Main deAccuracy ratescription content
the proportion of correctly labeled images is not less than 97%
# Licensing Information
Commercial License
|
sorenmulli/da-gym-2000 | ---
dataset_info:
features:
- name: question
dtype: string
- name: option-A
dtype: string
- name: option-B
dtype: string
- name: option-C
dtype: string
- name: option-D
dtype: string
- name: task_title
dtype: string
- name: index
dtype: int64
- name: context
dtype: string
- name: correct
dtype: string
splits:
- name: train
num_bytes: 33014
num_examples: 33
download_size: 25325
dataset_size: 33014
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# [WIP] Dataset Card for "da-gym-2000"
*Please note that this dataset and dataset card both are works in progress. For now refer to the related [thesis](https://sorenmulli.github.io/thesis/thesis.pdf) for all details*
|
open-llm-leaderboard/details_Danielbrdz__Barcenas-Orca-2-7b | ---
pretty_name: Evaluation run of Danielbrdz/Barcenas-Orca-2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Danielbrdz/Barcenas-Orca-2-7b](https://huggingface.co/Danielbrdz/Barcenas-Orca-2-7b)\
\ 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_Danielbrdz__Barcenas-Orca-2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T21:43:45.109844](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-Orca-2-7b/blob/main/results_2024-02-02T21-43-45.109844.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\": 0.5591921640503429,\n\
\ \"acc_stderr\": 0.03368722175431038,\n \"acc_norm\": 0.5636482453113677,\n\
\ \"acc_norm_stderr\": 0.0343906498680081,\n \"mc1\": 0.3011015911872705,\n\
\ \"mc1_stderr\": 0.01605899902610061,\n \"mc2\": 0.43723878604016764,\n\
\ \"mc2_stderr\": 0.01447045011168886\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5136518771331058,\n \"acc_stderr\": 0.014605943429860947,\n\
\ \"acc_norm\": 0.5520477815699659,\n \"acc_norm_stderr\": 0.014532011498211676\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5764787890858395,\n\
\ \"acc_stderr\": 0.004931065434173683,\n \"acc_norm\": 0.770762796255726,\n\
\ \"acc_norm_stderr\": 0.004194830716126065\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\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.042849586397534,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.042849586397534\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03015113445777629,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03015113445777629\n },\n\
\ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.625,\n \
\ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.625,\n \
\ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.48554913294797686,\n\
\ \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.48554913294797686,\n\
\ \"acc_norm_stderr\": 0.03810871630454764\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.04303684033537314,\n \"acc_norm\": 0.2982456140350877,\n\
\ \"acc_norm_stderr\": 0.04303684033537314\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.335978835978836,\n \"acc_stderr\": 0.024326310529149135,\n \"\
acc_norm\": 0.335978835978836,\n \"acc_norm_stderr\": 0.024326310529149135\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\
\ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\
\ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6225806451612903,\n\
\ \"acc_stderr\": 0.027575960723278246,\n \"acc_norm\": 0.6225806451612903,\n\
\ \"acc_norm_stderr\": 0.027575960723278246\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n\
\ \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n\
\ },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\"\
: {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6919191919191919,\n \"acc_stderr\": 0.03289477330098616,\n \"\
acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.03289477330098616\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5256410256410257,\n \"acc_stderr\": 0.025317649726448666,\n\
\ \"acc_norm\": 0.5256410256410257,\n \"acc_norm_stderr\": 0.025317649726448666\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.29259259259259257,\n \"acc_stderr\": 0.02773896963217609,\n \
\ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.02773896963217609\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.0322529423239964,\n \
\ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.0322529423239964\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658754,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658754\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7467889908256881,\n \"acc_stderr\": 0.01864407304137504,\n \"\
acc_norm\": 0.7467889908256881,\n \"acc_norm_stderr\": 0.01864407304137504\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251745,\n \"\
acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251745\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\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\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467766,\n\
\ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467766\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6776859504132231,\n \"acc_stderr\": 0.04266416363352168,\n \"\
acc_norm\": 0.6776859504132231,\n \"acc_norm_stderr\": 0.04266416363352168\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6759259259259259,\n\
\ \"acc_stderr\": 0.045245960070300476,\n \"acc_norm\": 0.6759259259259259,\n\
\ \"acc_norm_stderr\": 0.045245960070300476\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6073619631901841,\n \"acc_stderr\": 0.03836740907831028,\n\
\ \"acc_norm\": 0.6073619631901841,\n \"acc_norm_stderr\": 0.03836740907831028\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\
\ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.4017857142857143,\n\
\ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\
\ \"acc_stderr\": 0.02466249684520982,\n \"acc_norm\": 0.8290598290598291,\n\
\ \"acc_norm_stderr\": 0.02466249684520982\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7394636015325671,\n\
\ \"acc_stderr\": 0.015696008563807082,\n \"acc_norm\": 0.7394636015325671,\n\
\ \"acc_norm_stderr\": 0.015696008563807082\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6011560693641619,\n \"acc_stderr\": 0.02636243757454654,\n\
\ \"acc_norm\": 0.6011560693641619,\n \"acc_norm_stderr\": 0.02636243757454654\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24916201117318434,\n\
\ \"acc_stderr\": 0.014465893829859926,\n \"acc_norm\": 0.24916201117318434,\n\
\ \"acc_norm_stderr\": 0.014465893829859926\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6241830065359477,\n \"acc_stderr\": 0.027732834353363937,\n\
\ \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.027732834353363937\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6463022508038585,\n\
\ \"acc_stderr\": 0.02715520810320087,\n \"acc_norm\": 0.6463022508038585,\n\
\ \"acc_norm_stderr\": 0.02715520810320087\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6512345679012346,\n \"acc_stderr\": 0.026517597724465013,\n\
\ \"acc_norm\": 0.6512345679012346,\n \"acc_norm_stderr\": 0.026517597724465013\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.38652482269503546,\n \"acc_stderr\": 0.02904919034254346,\n \
\ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.02904919034254346\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38396349413298564,\n\
\ \"acc_stderr\": 0.012421587833134231,\n \"acc_norm\": 0.38396349413298564,\n\
\ \"acc_norm_stderr\": 0.012421587833134231\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\
\ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5408496732026143,\n \"acc_stderr\": 0.020160213617222516,\n \
\ \"acc_norm\": 0.5408496732026143,\n \"acc_norm_stderr\": 0.020160213617222516\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5545454545454546,\n\
\ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.5545454545454546,\n\
\ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6857142857142857,\n \"acc_stderr\": 0.029719329422417475,\n\
\ \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.029719329422417475\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.736318407960199,\n\
\ \"acc_stderr\": 0.03115715086935557,\n \"acc_norm\": 0.736318407960199,\n\
\ \"acc_norm_stderr\": 0.03115715086935557\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\
\ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3011015911872705,\n\
\ \"mc1_stderr\": 0.01605899902610061,\n \"mc2\": 0.43723878604016764,\n\
\ \"mc2_stderr\": 0.01447045011168886\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.332827899924185,\n \
\ \"acc_stderr\": 0.01297989249659828\n }\n}\n```"
repo_url: https://huggingface.co/Danielbrdz/Barcenas-Orca-2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|arc:challenge|25_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|gsm8k|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hellaswag|10_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T21-43-45.109844.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T21-43-45.109844.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- '**/details_harness|winogrande|5_2024-02-02T21-43-45.109844.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T21-43-45.109844.parquet'
- config_name: results
data_files:
- split: 2024_02_02T21_43_45.109844
path:
- results_2024-02-02T21-43-45.109844.parquet
- split: latest
path:
- results_2024-02-02T21-43-45.109844.parquet
---
# Dataset Card for Evaluation run of Danielbrdz/Barcenas-Orca-2-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Danielbrdz/Barcenas-Orca-2-7b](https://huggingface.co/Danielbrdz/Barcenas-Orca-2-7b) 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_Danielbrdz__Barcenas-Orca-2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T21:43:45.109844](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-Orca-2-7b/blob/main/results_2024-02-02T21-43-45.109844.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": 0.5591921640503429,
"acc_stderr": 0.03368722175431038,
"acc_norm": 0.5636482453113677,
"acc_norm_stderr": 0.0343906498680081,
"mc1": 0.3011015911872705,
"mc1_stderr": 0.01605899902610061,
"mc2": 0.43723878604016764,
"mc2_stderr": 0.01447045011168886
},
"harness|arc:challenge|25": {
"acc": 0.5136518771331058,
"acc_stderr": 0.014605943429860947,
"acc_norm": 0.5520477815699659,
"acc_norm_stderr": 0.014532011498211676
},
"harness|hellaswag|10": {
"acc": 0.5764787890858395,
"acc_stderr": 0.004931065434173683,
"acc_norm": 0.770762796255726,
"acc_norm_stderr": 0.004194830716126065
},
"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": 0.562962962962963,
"acc_stderr": 0.042849586397534,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.042849586397534
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926604,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926604
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6,
"acc_stderr": 0.03015113445777629,
"acc_norm": 0.6,
"acc_norm_stderr": 0.03015113445777629
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.625,
"acc_stderr": 0.04048439222695598,
"acc_norm": 0.625,
"acc_norm_stderr": 0.04048439222695598
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.44,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.48554913294797686,
"acc_stderr": 0.03810871630454764,
"acc_norm": 0.48554913294797686,
"acc_norm_stderr": 0.03810871630454764
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201943,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201943
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46382978723404256,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.46382978723404256,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.04303684033537314,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.04303684033537314
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.335978835978836,
"acc_stderr": 0.024326310529149135,
"acc_norm": 0.335978835978836,
"acc_norm_stderr": 0.024326310529149135
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30952380952380953,
"acc_stderr": 0.04134913018303316,
"acc_norm": 0.30952380952380953,
"acc_norm_stderr": 0.04134913018303316
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6225806451612903,
"acc_stderr": 0.027575960723278246,
"acc_norm": 0.6225806451612903,
"acc_norm_stderr": 0.027575960723278246
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.37438423645320196,
"acc_stderr": 0.03405155380561952,
"acc_norm": 0.37438423645320196,
"acc_norm_stderr": 0.03405155380561952
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7393939393939394,
"acc_stderr": 0.034277431758165236,
"acc_norm": 0.7393939393939394,
"acc_norm_stderr": 0.034277431758165236
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6919191919191919,
"acc_stderr": 0.03289477330098616,
"acc_norm": 0.6919191919191919,
"acc_norm_stderr": 0.03289477330098616
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7979274611398963,
"acc_stderr": 0.02897908979429673,
"acc_norm": 0.7979274611398963,
"acc_norm_stderr": 0.02897908979429673
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5256410256410257,
"acc_stderr": 0.025317649726448666,
"acc_norm": 0.5256410256410257,
"acc_norm_stderr": 0.025317649726448666
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.29259259259259257,
"acc_stderr": 0.02773896963217609,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.02773896963217609
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5588235294117647,
"acc_stderr": 0.0322529423239964,
"acc_norm": 0.5588235294117647,
"acc_norm_stderr": 0.0322529423239964
},
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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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]
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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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kaitchup/UltraFeedback-prompt-chosen-rejected | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected
dtype: string
- name: chosen
dtype: string
splits:
- name: train
num_bytes: 40009515
num_examples: 17075
- name: test
num_bytes: 2090411
num_examples: 895
download_size: 24762705
dataset_size: 42099926
---
|
diversoailab/standard_humaneval | ---
license: mit
annotations_creators: []
language_creators:
- expert-generated
language:
- code
multilinguality:
- monolingual
pretty_name: The-Stack
size_categories:
- <1K
source_datasets: []
task_categories:
- text-generation
task_ids: []
---
|
CyberHarem/okusawa_misaki_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of okusawa_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!)
This is the dataset of okusawa_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!), containing 374 images and their tags.
The core tags of this character are `bangs, black_hair, hair_ornament, blue_eyes, hairclip, medium_hair, 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 | 374 | 396.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okusawa_misaki_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 374 | 233.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okusawa_misaki_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 842 | 510.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okusawa_misaki_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 374 | 351.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okusawa_misaki_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 842 | 720.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/okusawa_misaki_bangdream/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/okusawa_misaki_bangdream',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, grey_hoodie, hood_down, long_sleeves, looking_at_viewer, solo, clothes_writing, drawstring, hair_ribbon, hairband, red_ribbon, upper_body, smile, blush, backpack, open_mouth, white_outline, white_background |
| 1 | 14 |  |  |  |  |  | 1girl, hood_down, solo, long_sleeves, looking_at_viewer, baseball_cap, grey_hoodie, simple_background, blue_jacket, brown_hair, blush, clothes_writing, white_background, black_shorts, black_headwear, short_shorts, drawstring, holding, open_clothes, smile, upper_body |
| 2 | 23 |  |  |  |  |  | hanasakigawa_school_uniform, blush, red_ribbon, neck_ribbon, long_sleeves, sailor_dress, 1girl, smile, upper_body, white_background, solo, looking_at_viewer, brown_dress, simple_background, white_sailor_collar, 2girls, double-breasted, open_mouth |
| 3 | 6 |  |  |  |  |  | 1girl, blush, solo, upper_body, white_background, looking_at_viewer, simple_background, collarbone, hat, open_mouth, sweatdrop, smile, sweater |
| 4 | 5 |  |  |  |  |  | 1girl, black_tank_top, blush, looking_at_viewer, sweat, bare_shoulders, collarbone, medium_breasts, solo, white_background, bare_arms, cleavage, sitting, +_+, arm_up, blue_shorts, closed_mouth, hand_between_legs, shirt, short_shorts, simple_background |
| 5 | 12 |  |  |  |  |  | bear_hair_ornament, bowtie, character_hair_ornament, short_sleeves, white_gloves, blush, corset, balloon, hat_bow, hat_ribbon, open_mouth, 1girl, solo, striped_bow, :d, center_frills, confetti, earrings, looking_at_viewer, blue_bow, blue_headwear, jacket, neck_ribbon, skirt, thighhighs, top_hat, upper_body |
| 6 | 8 |  |  |  |  |  | 2girls, upper_body, blush, simple_background, white_background, light_blue_hair, long_sleeves, clothes_writing, hat, solo_focus, striped, sweatdrop, sweater |
| 7 | 8 |  |  |  |  |  | 1girl, cloud, day, looking_at_viewer, outdoors, cleavage, collarbone, solo, blue_sky, blush, medium_breasts, sidelocks, smile, navel, holding, ocean, open_clothes, short_shorts, standing, black_bikini, cowboy_shot, frilled_bikini, groin, open_mouth, upper_body, water_drop |
| 8 | 8 |  |  |  |  |  | 1girl, epaulettes, shako_cap, solo, band_uniform, sleeveless, wrist_cuffs, looking_at_viewer, white_skirt, blush, open_mouth, smile, boots, red_footwear, thighhighs, adjusting_headwear, breasts, frills |
| 9 | 11 |  |  |  |  |  | 1girl, obi, solo, hair_flower, looking_at_viewer, wide_sleeves, blue_kimono, blush, floral_print, long_sleeves, holding, smile, brown_hair, print_kimono, upper_body, yukata, closed_mouth, short_hair, fireworks, new_year, open_mouth |
| 10 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, navel, nipples, solo_focus, open_mouth, sex, small_breasts, sweat, brown_hair, completely_nude, vaginal, armpits, cum, mosaic_censoring, penis, pov, pussy, shirt_lift, short_hair, standing, tank_top |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | grey_hoodie | hood_down | long_sleeves | looking_at_viewer | solo | clothes_writing | drawstring | hair_ribbon | hairband | red_ribbon | upper_body | smile | blush | backpack | open_mouth | white_outline | white_background | baseball_cap | simple_background | blue_jacket | brown_hair | black_shorts | black_headwear | short_shorts | holding | open_clothes | hanasakigawa_school_uniform | neck_ribbon | sailor_dress | brown_dress | white_sailor_collar | 2girls | double-breasted | collarbone | hat | sweatdrop | sweater | black_tank_top | sweat | bare_shoulders | medium_breasts | bare_arms | cleavage | sitting | +_+ | arm_up | blue_shorts | closed_mouth | hand_between_legs | shirt | bear_hair_ornament | bowtie | character_hair_ornament | short_sleeves | white_gloves | corset | balloon | hat_bow | hat_ribbon | striped_bow | :d | center_frills | confetti | earrings | blue_bow | blue_headwear | jacket | skirt | thighhighs | top_hat | light_blue_hair | solo_focus | striped | cloud | day | outdoors | blue_sky | sidelocks | navel | ocean | standing | black_bikini | cowboy_shot | frilled_bikini | groin | water_drop | epaulettes | shako_cap | band_uniform | sleeveless | wrist_cuffs | white_skirt | boots | red_footwear | adjusting_headwear | breasts | frills | obi | hair_flower | wide_sleeves | blue_kimono | floral_print | print_kimono | yukata | short_hair | fireworks | new_year | 1boy | hetero | nipples | sex | small_breasts | completely_nude | vaginal | armpits | cum | mosaic_censoring | penis | pov | pussy | shirt_lift | tank_top |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:------------|:---------------|:--------------------|:-------|:------------------|:-------------|:--------------|:-----------|:-------------|:-------------|:--------|:--------|:-----------|:-------------|:----------------|:-------------------|:---------------|:--------------------|:--------------|:-------------|:---------------|:-----------------|:---------------|:----------|:---------------|:------------------------------|:--------------|:---------------|:--------------|:----------------------|:---------|:------------------|:-------------|:------|:------------|:----------|:-----------------|:--------|:-----------------|:-----------------|:------------|:-----------|:----------|:------|:---------|:--------------|:---------------|:--------------------|:--------|:---------------------|:---------|:--------------------------|:----------------|:---------------|:---------|:----------|:----------|:-------------|:--------------|:-----|:----------------|:-----------|:-----------|:-----------|:----------------|:---------|:--------|:-------------|:----------|:------------------|:-------------|:----------|:--------|:------|:-----------|:-----------|:------------|:--------|:--------|:-----------|:---------------|:--------------|:-----------------|:--------|:-------------|:-------------|:------------|:---------------|:-------------|:--------------|:--------------|:--------|:---------------|:---------------------|:----------|:---------|:------|:--------------|:---------------|:--------------|:---------------|:---------------|:---------|:-------------|:------------|:-----------|:-------|:---------|:----------|:------|:----------------|:------------------|:----------|:----------|:------|:-------------------|:--------|:------|:--------|:-------------|:-----------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 23 |  |  |  |  |  | X | | | X | X | X | | | | | X | X | X | X | | X | | X | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | | X | X | | | | | | X | X | X | | X | | X | | X | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | X | X | | | | | | | | X | | | | X | | X | | | | | X | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | 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 | 8 |  |  |  |  |  | | | | X | | | X | | | | | X | | X | | | | X | | X | | | | | | | | | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | | | | X | X | | | | | | X | X | X | | X | | | | | | | | | X | X | X | | | | | | | | X | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 8 |  |  |  |  |  | X | | | | X | X | | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 11 |  |  |  |  |  | X | | | X | X | X | | | | | | X | X | X | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 10 | 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 |
|
yzhuang/metatree_fri_c0_1000_10 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 71100
num_examples: 711
- name: validation
num_bytes: 28900
num_examples: 289
download_size: 105321
dataset_size: 100000
---
# Dataset Card for "metatree_fri_c0_1000_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nitinbhayana/keyword_ner | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 104030
num_examples: 985
download_size: 29995
dataset_size: 104030
---
# Dataset Card for "keyword_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/angelene_toarumajutsunoindex | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of angelene (To Aru Majutsu no Index)
This is the dataset of angelene (To Aru Majutsu no Index), containing 10 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
|
juanaparicio/example | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2728
num_examples: 3
download_size: 8138
dataset_size: 2728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tyzhu/find_first_sent_train_500_eval_20 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1146751
num_examples: 904
- name: validation
num_bytes: 22055
num_examples: 20
download_size: 501848
dataset_size: 1168806
---
# Dataset Card for "find_first_sent_train_500_eval_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chen661234/my_dataset | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 10467.0
num_examples: 2
download_size: 0
dataset_size: 10467.0
---
# Dataset Card for "my_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kaue123456/GokuWendelBezerra | ---
license: openrail
---
|
alexzyqi/GPT4Point | ---
license: mit
---
|
tyzhu/wiki_find_passage_train30_eval20_title | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 56836
num_examples: 80
- name: validation
num_bytes: 15614
num_examples: 20
download_size: 42736
dataset_size: 72450
---
# Dataset Card for "wiki_find_passage_train30_eval20_title"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_shahzebnaveed__NeuralHermes-2.5-Mistral-7B | ---
pretty_name: Evaluation run of shahzebnaveed/NeuralHermes-2.5-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [shahzebnaveed/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/shahzebnaveed/NeuralHermes-2.5-Mistral-7B)\
\ 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_shahzebnaveed__NeuralHermes-2.5-Mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-16T18:38:39.552249](https://huggingface.co/datasets/open-llm-leaderboard/details_shahzebnaveed__NeuralHermes-2.5-Mistral-7B/blob/main/results_2024-02-16T18-38-39.552249.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\": 0.6389813616730637,\n\
\ \"acc_stderr\": 0.032241943281319616,\n \"acc_norm\": 0.6417845405458356,\n\
\ \"acc_norm_stderr\": 0.03288190466152383,\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.01680186046667716,\n \"mc2\": 0.5229369986337806,\n\
\ \"mc2_stderr\": 0.01525073227946668\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6126279863481229,\n \"acc_stderr\": 0.014235872487909869,\n\
\ \"acc_norm\": 0.6484641638225256,\n \"acc_norm_stderr\": 0.013952413699600931\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6519617606054571,\n\
\ \"acc_stderr\": 0.004753746951620152,\n \"acc_norm\": 0.8428599880501892,\n\
\ \"acc_norm_stderr\": 0.003631889496122536\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.02854479331905533,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.02854479331905533\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\
\ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\
\ \"acc_norm_stderr\": 0.036928207672648664\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\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\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\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"\
acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\
acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.032250781083062896,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.032250781083062896\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6153846153846154,\n \"acc_stderr\": 0.024666744915187208,\n\
\ \"acc_norm\": 0.6153846153846154,\n \"acc_norm_stderr\": 0.024666744915187208\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.02803792996911499,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.02803792996911499\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565437,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565437\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.039578354719809805,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.039578354719809805\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\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\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597528\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\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608306,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608306\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3094972067039106,\n\
\ \"acc_stderr\": 0.015461169002371542,\n \"acc_norm\": 0.3094972067039106,\n\
\ \"acc_norm_stderr\": 0.015461169002371542\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824782,\n\
\ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824782\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464485,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464485\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\
\ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\
\ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\
\ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507215,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507215\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\
\ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\
\ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.01680186046667716,\n \"mc2\": 0.5229369986337806,\n\
\ \"mc2_stderr\": 0.01525073227946668\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.011646276755089688\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5595147839272175,\n \
\ \"acc_stderr\": 0.01367457213169389\n }\n}\n```"
repo_url: https://huggingface.co/shahzebnaveed/NeuralHermes-2.5-Mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|arc:challenge|25_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|gsm8k|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hellaswag|10_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-38-39.552249.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T18-38-39.552249.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- '**/details_harness|winogrande|5_2024-02-16T18-38-39.552249.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-16T18-38-39.552249.parquet'
- config_name: results
data_files:
- split: 2024_02_16T18_38_39.552249
path:
- results_2024-02-16T18-38-39.552249.parquet
- split: latest
path:
- results_2024-02-16T18-38-39.552249.parquet
---
# Dataset Card for Evaluation run of shahzebnaveed/NeuralHermes-2.5-Mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [shahzebnaveed/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/shahzebnaveed/NeuralHermes-2.5-Mistral-7B) 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_shahzebnaveed__NeuralHermes-2.5-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T18:38:39.552249](https://huggingface.co/datasets/open-llm-leaderboard/details_shahzebnaveed__NeuralHermes-2.5-Mistral-7B/blob/main/results_2024-02-16T18-38-39.552249.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": 0.6389813616730637,
"acc_stderr": 0.032241943281319616,
"acc_norm": 0.6417845405458356,
"acc_norm_stderr": 0.03288190466152383,
"mc1": 0.3598531211750306,
"mc1_stderr": 0.01680186046667716,
"mc2": 0.5229369986337806,
"mc2_stderr": 0.01525073227946668
},
"harness|arc:challenge|25": {
"acc": 0.6126279863481229,
"acc_stderr": 0.014235872487909869,
"acc_norm": 0.6484641638225256,
"acc_norm_stderr": 0.013952413699600931
},
"harness|hellaswag|10": {
"acc": 0.6519617606054571,
"acc_stderr": 0.004753746951620152,
"acc_norm": 0.8428599880501892,
"acc_norm_stderr": 0.003631889496122536
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
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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. -->
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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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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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lang-uk/recruitment-dataset-candidate-profiles-ukrainian | ---
language:
- uk
license: mit
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: Position
dtype: string
- name: Moreinfo
dtype: string
- name: Looking For
dtype: string
- name: Highlights
dtype: string
- name: Primary Keyword
dtype: string
- name: English Level
dtype: string
- name: Experience Years
dtype: float64
- name: CV
dtype: string
- name: CV_lang
dtype: string
- name: id
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 61582148
num_examples: 24230
download_size: 32043828
dataset_size: 61582148
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Djinni Dataset (Ukrainian CVs part)
## Overview
The [Djinni Recruitment Dataset](https://github.com/Stereotypes-in-LLMs/recruitment-dataset) (Ukrainian CVs part) contains 150,000 job descriptions and 230,000 anonymized candidate CVs, posted between 2020-2023 on the [Djinni](https://djinni.co/) IT job platform. The dataset includes samples in English and Ukrainian.
The dataset contains various attributes related to candidate CVs, including position titles, candidate information, candidate highlights, job search preferences, job profile types, English proficiency levels, experience years, concatenated CV text, language of CVs, and unique identifiers.
## Intended Use
The Djinni dataset is designed with versatility in mind, supporting a wide range of applications:
- **Recommender Systems and Semantic Search:** It serves as a key resource for enhancing job recommendation engines and semantic search functionalities, making the job search process more intuitive and tailored to individual preferences.
- **Advancement of Large Language Models (LLMs):** The dataset provides invaluable training data for both English and Ukrainian domain-specific LLMs. It is instrumental in improving the models' understanding and generation capabilities, particularly in specialized recruitment contexts.
- **Fairness in AI-assisted Hiring:** By serving as a benchmark for AI fairness, the Djinni dataset helps mitigate biases in AI-assisted recruitment processes, promoting more equitable hiring practices.
- **Recruitment Automation:** The dataset enables the development of tools for automated creation of resumes and job descriptions, streamlining the recruitment process.
- **Market Analysis:** It offers insights into the dynamics of Ukraine's tech sector, including the impacts of conflicts, aiding in comprehensive market analysis.
- **Trend Analysis and Topic Discovery:** The dataset facilitates modeling and classification for trend analysis and topic discovery within the tech industry.
- **Strategic Planning:** By enabling the automatic identification of company domains, the dataset assists in strategic market planning.
## BibTeX entry and citation info
*When publishing results based on this dataset please refer to:*
```bibtex
@inproceedings{djinni,
title = "Introducing the {D}jinni {R}ecruitment {D}ataset: A Corpus of Anonymized {CV}s and Job Postings",
author = "Drushchak, Nazarii and
Romanyshyn, Mariana",
booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "European Language Resources Association",
}
```
## Attribution
Special thanks to [Djinni](https://djinni.co/) for providing this invaluable dataset. Their contribution is crucial in advancing research and development in AI, machine learning, and the broader tech industry. Their effort in compiling and sharing this dataset is greatly appreciated by the community. |
Shdorsh/luxembourgish-lu | ---
license: openrail
pretty_name: Luxembourgish Language Dataset
--- |
CyberHarem/littorio_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of littorio (Kantai Collection)
This is the dataset of littorio (Kantai Collection), containing 338 images and their tags.
The core tags of this character are `long_hair, brown_hair, brown_eyes, breasts, large_breasts, ponytail, wavy_hair, hat`, 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 | 338 | 320.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/littorio_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 338 | 221.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/littorio_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 726 | 441.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/littorio_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 338 | 298.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/littorio_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 726 | 562.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/littorio_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/littorio_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 31 |  |  |  |  |  | 1girl, detached_sleeves, necktie, solo, bare_shoulders, smile, miniskirt, looking_at_viewer, thighhighs, garter_straps, zettai_ryouiki, open_mouth, machinery, turret |
| 1 | 12 |  |  |  |  |  | 1girl, bare_shoulders, detached_sleeves, necktie, smile, solo, blush, looking_at_viewer, upper_body, white_background, open_mouth, simple_background |
| 2 | 13 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, solo, cleavage, red_bikini, navel, simple_background, smile, white_background, blush, criss-cross_halter, open_mouth, cowboy_shot |
| 3 | 8 |  |  |  |  |  | 1girl, drinking_glass, hair_flower, red_bikini, solo, cleavage, drinking_straw, looking_at_viewer, white_background, hibiscus, navel, simple_background, smile, blush, holding, lemon_slice, open_mouth, cowboy_shot, hair_between_eyes, sarong |
| 4 | 8 |  |  |  |  |  | 1girl, solo, white_sweater, blush, alternate_costume, long_sleeves, looking_at_viewer, simple_background, gift_box, heart-shaped_box, holding_gift, skirt, white_background, one-hour_drawing_challenge, smile, valentine, black_footwear, full_body, hair_between_eyes, twitter_username, upper_body |
| 5 | 12 |  |  |  |  |  | santa_costume, santa_hat, 1girl, looking_at_viewer, sack, solo, white_gloves, alternate_costume, christmas, cleavage, smile, bare_shoulders, blush, open_mouth, striped_scarf, boots, red_dress |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, solo_focus, sweat, detached_sleeves, garter_straps, miniskirt, necktie, nipples, sex_from_behind, thighhighs, vaginal, bare_shoulders, clothes_lift, mosaic_censoring, naval_uniform, penis, arm_grab, bouncing_breasts, breasts_out, cum_in_pussy, full_nelson, male_pubic_hair, open_fly, panties_aside, reverse_suspended_congress, saliva, straddling, striped, symbol-shaped_pupils, tears |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | necktie | solo | bare_shoulders | smile | miniskirt | looking_at_viewer | thighhighs | garter_straps | zettai_ryouiki | open_mouth | machinery | turret | blush | upper_body | white_background | simple_background | hair_flower | cleavage | red_bikini | navel | criss-cross_halter | cowboy_shot | drinking_glass | drinking_straw | hibiscus | holding | lemon_slice | hair_between_eyes | sarong | white_sweater | alternate_costume | long_sleeves | gift_box | heart-shaped_box | holding_gift | skirt | one-hour_drawing_challenge | valentine | black_footwear | full_body | twitter_username | santa_costume | santa_hat | sack | white_gloves | christmas | striped_scarf | boots | red_dress | 1boy | hetero | solo_focus | sweat | nipples | sex_from_behind | vaginal | clothes_lift | mosaic_censoring | naval_uniform | penis | arm_grab | bouncing_breasts | breasts_out | cum_in_pussy | full_nelson | male_pubic_hair | open_fly | panties_aside | reverse_suspended_congress | saliva | straddling | striped | symbol-shaped_pupils | tears |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:----------|:-------|:-----------------|:--------|:------------|:--------------------|:-------------|:----------------|:-----------------|:-------------|:------------|:---------|:--------|:-------------|:-------------------|:--------------------|:--------------|:-----------|:-------------|:--------|:---------------------|:--------------|:-----------------|:-----------------|:-----------|:----------|:--------------|:--------------------|:---------|:----------------|:--------------------|:---------------|:-----------|:-------------------|:---------------|:--------|:-----------------------------|:------------|:-----------------|:------------|:-------------------|:----------------|:------------|:-------|:---------------|:------------|:----------------|:--------|:------------|:-------|:---------|:-------------|:--------|:----------|:------------------|:----------|:---------------|:-------------------|:----------------|:--------|:-----------|:-------------------|:--------------|:---------------|:--------------|:------------------|:-----------|:----------------|:-----------------------------|:---------|:-------------|:----------|:-----------------------|:--------|
| 0 | 31 |  |  |  |  |  | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | | | X | | X | | X | | | | X | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | X | | | X | | X | | X | | | | X | | | X | | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | | X | | X | | X | | | | | | | X | X | X | X | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | X | | | X | X | X | | X | | | | X | | | X | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | X | | X | | X | | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Mitsuki-Sakamoto/alpaca_farm-reward-model-deberta-v3-large-v2-re-eval-preference | ---
dataset_info:
config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: preference
dtype: int64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
- name: do_sample
dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
splits:
- name: val
num_bytes: 2575959
num_examples: 2000
download_size: 1232867
dataset_size: 2575959
configs:
- config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
data_files:
- split: val
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/val-*
---
# Dataset Card for "alpaca_farm-reward-model-deberta-v3-large-v2-re-eval-preference"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AngeTranslate/tom | ---
license: openrail
---
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6084 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 2520812
num_examples: 6084
- name: fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 4868154
num_examples: 6084
- name: fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 9570134
num_examples: 6084
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 2409382
num_examples: 6084
- name: fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 4649134
num_examples: 6084
- name: fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 9130589
num_examples: 6084
download_size: 4880166
dataset_size: 33148205
---
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lucadiliello/searchqa | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: 483999103
num_examples: 117384
- name: validation
num_bytes: 69647447
num_examples: 16980
download_size: 325197949
dataset_size: 553646550
---
# Dataset Card for "searchqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). |
hackathon-pln-es/readability-es-hackathon-pln-public | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: readability-es-sentences
tags:
- readability
---
# Dataset Card for [readability-es-sentences]
## Dataset Description
Compilation of short Spanish articles for readability assessment.
### Dataset Summary
This dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources:
- **Coh-Metrix-Esp corpus (Quispesaravia, et al., 2016):** collection of 100 parallel texts with simple and complex variants in Spanish. These texts include children's and adult stories to fulfill each category.
- **[kwiziq](https://www.kwiziq.com/):** a language learner assistant
- **[hablacultura.com](https://hablacultura.com/):** Spanish resources for students and teachers. We have downloaded the available content in their websites.
### Languages
Spanish
## Dataset Structure
The dataset includes 1019 text entries between 80 and 8714 characters long. The vast majority (97%) are below 4,000 characters long.
### Data Fields
The dataset is formatted as a json lines and includes the following fields:
- **Category:** when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).
- **Level:** standardized readability level: complex or simple.
- **Level-3:** standardized readability level: basic, intermediate or advanced
- **Text:** original text formatted into sentences.
Not all the entries contain usable values for `category`, `level` and `level-3`, but all of them should contain at least one of `level`, `level-3`. When the corresponding information could not be derived, we use the special `"N/A"` value to indicate so.
## Additional Information
### Licensing Information
https://creativecommons.org/licenses/by-nc-sa/4.0/
### Citation Information
Please cite this page to give credit to the authors :)
### Team
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
|
thennal/ulca_ml | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 1117843608.106
num_examples: 8614
download_size: 321100264
dataset_size: 1117843608.106
annotations_creators:
- expert-generated
language:
- ml
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ULCA ASR Dataset Malayalam Speech Corpus
size_categories:
- 1K<n<10K
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# ULCA ASR Dataset Malayalam Speech Corpus
The labelled Malayalam speech subcorpus from the larger [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus).
The speech is taken from news broadcasts, and is largely composed of short soundbites with some longer outliers.
## Dataset Description
- **Repository:** [https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus) |
JeswinMS4/code_text_classification | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sentence
dtype: string
- name: Label
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 74362
num_examples: 823
- name: test
num_bytes: 4194
num_examples: 46
- name: validation
num_bytes: 4185
num_examples: 46
download_size: 41632
dataset_size: 82741
---
# Dataset Card for "code_text_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-cnn_dailymail-3ca4a8a7-12855714 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: t5-large
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-large
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sysresearch101](https://huggingface.co/sysresearch101) for evaluating this model. |
CyberHarem/himeko_starrail | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of himeko/姫子/姬子/히메코 (Honkai: Star Rail)
This is the dataset of himeko/姫子/姬子/히메코 (Honkai: Star Rail), containing 500 images and their tags.
The core tags of this character are `red_hair, long_hair, breasts, bangs, yellow_eyes, hair_between_eyes, large_breasts, earrings, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 897.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himeko_starrail/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 456.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himeko_starrail/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1220 | 953.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himeko_starrail/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 763.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himeko_starrail/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1220 | 1.40 GiB | [Download](https://huggingface.co/datasets/CyberHarem/himeko_starrail/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/himeko_starrail',
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 | 37 |  |  |  |  |  | 1girl, white_dress, cleavage, solo, looking_at_viewer, sleeveless_dress, smile, black_gloves, jewelry, closed_mouth, mole_on_breast, single_glove, holding |
| 1 | 5 |  |  |  |  |  | 1girl, black_jacket, closed_mouth, looking_at_viewer, smile, solo, cleavage, white_dress, jewelry, signature, sky |
| 2 | 7 |  |  |  |  |  | 1girl, jewelry, looking_at_viewer, mole_on_breast, solo, closed_mouth, navel, simple_background, smile, white_background, areola_slip, black_bikini, cleavage, bare_shoulders, choker, hair_flower, sitting, yellow_flower |
| 3 | 27 |  |  |  |  |  | 1girl, nipples, blush, navel, hetero, spread_legs, sweat, 1boy, completely_nude, penis, jewelry, looking_at_viewer, sex, vaginal, cum_in_pussy, on_back, smile, open_mouth, solo_focus, indoors, mosaic_censoring, on_bed, pov, bed_sheet, stomach, thighs, black_choker, pillow |
| 4 | 6 |  |  |  |  |  | 1girl, black_bra, black_panties, cleavage, looking_at_viewer, solo, black_thighhighs, blush, huge_breasts, jewelry, mole_on_breast, navel, thighs, underwear_only, collarbone, garter_straps, lingerie, sitting, smile |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, black_choker, mosaic_censoring, penis, solo_focus, sweat, tongue_out, breasts_squeezed_together, completely_nude, jewelry, looking_at_viewer, mole_on_breast, open_mouth, paizuri, cum, hair_flower, heart, oral, yellow_flower |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, jewelry, solo_focus, uncensored, bare_shoulders, dress, nipples, saliva, blush, looking_at_viewer, medium_breasts, choker, erection, licking_penis, male_pubic_hair, testicles, tongue_out |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | white_dress | cleavage | solo | looking_at_viewer | sleeveless_dress | smile | black_gloves | jewelry | closed_mouth | mole_on_breast | single_glove | holding | black_jacket | signature | sky | navel | simple_background | white_background | areola_slip | black_bikini | bare_shoulders | choker | hair_flower | sitting | yellow_flower | nipples | blush | hetero | spread_legs | sweat | 1boy | completely_nude | penis | sex | vaginal | cum_in_pussy | on_back | open_mouth | solo_focus | indoors | mosaic_censoring | on_bed | pov | bed_sheet | stomach | thighs | black_choker | pillow | black_bra | black_panties | black_thighhighs | huge_breasts | underwear_only | collarbone | garter_straps | lingerie | tongue_out | breasts_squeezed_together | paizuri | cum | heart | oral | uncensored | dress | saliva | medium_breasts | erection | licking_penis | male_pubic_hair | testicles |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-----------|:-------|:--------------------|:-------------------|:--------|:---------------|:----------|:---------------|:-----------------|:---------------|:----------|:---------------|:------------|:------|:--------|:--------------------|:-------------------|:--------------|:---------------|:-----------------|:---------|:--------------|:----------|:----------------|:----------|:--------|:---------|:--------------|:--------|:-------|:------------------|:--------|:------|:----------|:---------------|:----------|:-------------|:-------------|:----------|:-------------------|:---------|:------|:------------|:----------|:---------|:---------------|:---------|:------------|:----------------|:-------------------|:---------------|:-----------------|:-------------|:----------------|:-----------|:-------------|:----------------------------|:----------|:------|:--------|:-------|:-------------|:--------|:---------|:-----------------|:-----------|:----------------|:------------------|:------------|
| 0 | 37 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | X | X | X | | X | | X | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 27 |  |  |  |  |  | X | | | | X | | X | | X | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | X | 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 | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | | X | | | | X | | | | | | | | | | | | | X | X | | | | X | X | X | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X |
|
alzhivida/alzhivida | ---
license: llama2
---
<s>[INST] Me gradué hace poco de la carrera de medicina ¿Me podrías aconsejar para conseguir rápidamente un puesto de trabajo? [/INST] Esto vale tanto para médicos como para cualquier otra profesión tras finalizar los estudios aniversarios y mi consejo sería preguntar a cuántas personas haya conocido mejor. En este caso, mi primera opción sería hablar con otros profesionales médicos, echar currículos en hospitales y cualquier centro de salud. En paralelo, trabajaría por mejorar mi marca personal como médico mediante un blog o formas digitales de comunicación como los vídeos. Y, para mejorar las posibilidades de encontrar trabajo, también participaría en congresos y encuentros para conseguir más contactos. Y, además de todo lo anterior, seguiría estudiando para presentarme a las oposiciones y ejercer la medicina en el sector público de mi país. </s>
|
epinnock/software-architecture-instructions | ---
dataset_info:
features:
- name: instructions
dtype: string
splits:
- name: train
num_bytes: 24799
num_examples: 210
download_size: 10040
dataset_size: 24799
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BlazeLlama/euclid_elements_eng | ---
license: apache-2.0
---
|
pphuc25/data-gpt4 | ---
dataset_info:
features:
- name: result
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 224096
num_examples: 100
download_size: 119152
dataset_size: 224096
---
# Dataset Card for "data-gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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