id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
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demo-leaderboard/results | demo-leaderboard | 2023-11-21T17:22:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T17:22:56Z | 2023-11-21T17:12:08.000Z | 2023-11-21T17:12:08 | Entry not found | [
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/chihayafuru | BangumiBase | 2023-11-22T08:52:47Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-22T08:52:47Z | 2023-11-21T17:21:25.000Z | 2023-11-21T17:21:25 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Chihayafuru
This is the image base of bangumi Chihayafuru, we detected 58 characters, 8676 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 510 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 97 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 1030 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 509 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 459 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 172 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 183 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 84 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 287 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 60 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 26 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 18 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 177 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 182 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 71 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 26 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 32 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 106 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 423 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 74 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 59 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 81 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 92 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 36 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 149 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 1169 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 279 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 56 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 854 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 47 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 99 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 72 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 51 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 135 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 37 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 74 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 34 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 37 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 85 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 21 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 33 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 76 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 25 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 45 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 69 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 10 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 36 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 12 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 35 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 15 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 78 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 20 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 14 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 18 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 20 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 19 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 131 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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lmceschini/lucao-face | lmceschini | 2023-11-21T17:32:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T17:32:16Z | 2023-11-21T17:28:42.000Z | 2023-11-21T17:28:42 | Entry not found | [
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joaobfm/ryan_ia_dataset_a | joaobfm | 2023-11-21T17:34:27Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-21T17:34:27Z | 2023-11-21T17:33:51.000Z | 2023-11-21T17:33:51 | ---
license: openrail
---
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PeterLawrence/inova8.schema.1 | PeterLawrence | 2023-11-24T16:32:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-24T16:32:28Z | 2023-11-21T17:38:13.000Z | 2023-11-21T17:38:13 | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 75425
num_examples: 85
download_size: 10401
dataset_size: 75425
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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cyz2727327/lab_GS_YC | cyz2727327 | 2023-11-21T17:46:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T17:46:14Z | 2023-11-21T17:46:14.000Z | 2023-11-21T17:46:14 | Entry not found | [
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kimjisoobkkai/EsioIA | kimjisoobkkai | 2023-11-21T17:55:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T17:55:43Z | 2023-11-21T17:49:22.000Z | 2023-11-21T17:49:22 | Entry not found | [
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BangumiBase/sousounofrieren | BangumiBase | 2023-11-21T19:10:10Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-21T19:10:10Z | 2023-11-21T17:56:03.000Z | 2023-11-21T17:56:03 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sousou No Frieren
This is the image base of bangumi Sousou no Frieren, we detected 19 characters, 1826 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 31 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 20 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 85 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 10 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 14 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 78 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 39 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 218 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 8 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 6 | [Download](11/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 12 | 570 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 448 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 43 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 7 | [Download](16/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 17 | 24 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 125 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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reach-vb/common_voice_14_0 | reach-vb | 2023-11-22T14:58:58Z | 0 | 0 | common-voice | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1M<n<100M",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | 2023-11-22T14:58:58Z | 2023-11-21T18:27:15.000Z | 2023-11-21T18:27:15 | ---
pretty_name: Common Voice Corpus 14
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
- hu
- hy-AM
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nan-tw
- ne-NP
- nl
- nn-NO
- oc
- or
- pa-IN
- pl
- ps
- pt
- quy
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sq
- sr
- sv-SE
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yo
- yue
- zgh
- zh-CN
- zh-HK
- zh-TW
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 1M<n<100M
source_datasets:
- extended|common_voice
task_categories:
- speech-processing
task_ids:
- automatic-speech-recognition
paperswithcode_id: common-voice
extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."
---
# Dataset Card for Common Voice Corpus 14
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 28117 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 18651 validated hours in 112 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_14_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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Kana31/Imbeca_DatasetRemaster | Kana31 | 2023-11-21T18:51:17Z | 0 | 0 | null | [
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relhousieny/share_bike_test | relhousieny | 2023-11-21T19:01:41Z | 0 | 0 | null | [
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---
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Refic/RickSanchez | Refic | 2023-11-21T19:22:57Z | 0 | 0 | null | [
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license: unlicense
---
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nayohan/DialogueGeneration | nayohan | 2023-11-21T19:28:48Z | 0 | 0 | null | [
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configs:
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path: data/train-*
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path: data/validation-*
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dataset_info:
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download_size: 10205746
dataset_size: 24186968
---
# Dataset Card for "DialogueGeneration"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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configs:
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---
# Dataset Card for "DialogueHistoryGeneration"
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open-llm-leaderboard/details_CoruNethron__neu-sai-it1_public | open-llm-leaderboard | 2023-11-21T19:34:14Z | 0 | 0 | null | [
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pretty_name: Evaluation run of CoruNethron/neu-sai-it1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CoruNethron/neu-sai-it1](https://huggingface.co/CoruNethron/neu-sai-it1) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_CoruNethron__neu-sai-it1_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-21T19:30:24.351070](https://huggingface.co/datasets/open-llm-leaderboard/details_CoruNethron__neu-sai-it1_public/blob/main/results_2023-11-21T19-30-24.351070.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\": 0.5949297319149666,\n\
\ \"acc_stderr\": 0.03274268078653866,\n \"acc_norm\": 0.6054937730425815,\n\
\ \"acc_norm_stderr\": 0.03355540671285046,\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677154,\n \"mc2\": 0.5148628224777658,\n\
\ \"mc2_stderr\": 0.015540287053669583,\n \"em\": 0.3584312080536913,\n\
\ \"em_stderr\": 0.004910934869746984,\n \"f1\": 0.4530736157718142,\n\
\ \"f1_stderr\": 0.004671764766418761\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5708191126279863,\n \"acc_stderr\": 0.014464085894870653,\n\
\ \"acc_norm\": 0.6126279863481229,\n \"acc_norm_stderr\": 0.01423587248790987\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6184027086237801,\n\
\ \"acc_stderr\": 0.00484785754695748,\n \"acc_norm\": 0.8138816968731328,\n\
\ \"acc_norm_stderr\": 0.0038840668811314745\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\
\ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\
\ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6452830188679245,\n \"acc_stderr\": 0.02944517532819959,\n\
\ \"acc_norm\": 0.6452830188679245,\n \"acc_norm_stderr\": 0.02944517532819959\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\
\ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\
\ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\": 0.47,\n\
\ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|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-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\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929775,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929775\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
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: {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\
\ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
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\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"\
acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7064516129032258,\n\
\ \"acc_stderr\": 0.025906087021319295,\n \"acc_norm\": 0.7064516129032258,\n\
\ \"acc_norm_stderr\": 0.025906087021319295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n\
\ \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.03524390844511781,\n\
\ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.03524390844511781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5923076923076923,\n \"acc_stderr\": 0.024915243985987847,\n\
\ \"acc_norm\": 0.5923076923076923,\n \"acc_norm_stderr\": 0.024915243985987847\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3,\n \"acc_stderr\": 0.027940457136228405,\n \"acc_norm\"\
: 0.3,\n \"acc_norm_stderr\": 0.027940457136228405\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\
: {\n \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150016,\n\
\ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150016\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8073394495412844,\n \"acc_stderr\": 0.016909276884936066,\n \"\
acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.016909276884936066\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39351851851851855,\n \"acc_stderr\": 0.03331747876370312,\n \"\
acc_norm\": 0.39351851851851855,\n \"acc_norm_stderr\": 0.03331747876370312\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8137254901960784,\n \"acc_stderr\": 0.02732547096671632,\n \"\
acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.02732547096671632\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229962,\n \
\ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229962\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516304,\n \"\
acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516304\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\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\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8020434227330779,\n\
\ \"acc_stderr\": 0.01424887354921756,\n \"acc_norm\": 0.8020434227330779,\n\
\ \"acc_norm_stderr\": 0.01424887354921756\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.02607431485165708,\n\
\ \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.02607431485165708\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31620111731843575,\n\
\ \"acc_stderr\": 0.015551673652172554,\n \"acc_norm\": 0.31620111731843575,\n\
\ \"acc_norm_stderr\": 0.015551673652172554\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156847,\n\
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156847\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464496,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464496\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6635802469135802,\n \"acc_stderr\": 0.02628973494595293,\n\
\ \"acc_norm\": 0.6635802469135802,\n \"acc_norm_stderr\": 0.02628973494595293\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \
\ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4380704041720991,\n\
\ \"acc_stderr\": 0.01267190278256765,\n \"acc_norm\": 0.4380704041720991,\n\
\ \"acc_norm_stderr\": 0.01267190278256765\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5735294117647058,\n \"acc_stderr\": 0.030042615832714864,\n\
\ \"acc_norm\": 0.5735294117647058,\n \"acc_norm_stderr\": 0.030042615832714864\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6290849673202614,\n \"acc_stderr\": 0.019542101564854125,\n \
\ \"acc_norm\": 0.6290849673202614,\n \"acc_norm_stderr\": 0.019542101564854125\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6693877551020408,\n \"acc_stderr\": 0.030116426296540603,\n\
\ \"acc_norm\": 0.6693877551020408,\n \"acc_norm_stderr\": 0.030116426296540603\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\
\ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\
\ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677154,\n \"mc2\": 0.5148628224777658,\n\
\ \"mc2_stderr\": 0.015540287053669583\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7750591949486977,\n \"acc_stderr\": 0.011735043564126735\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.3584312080536913,\n \
\ \"em_stderr\": 0.004910934869746984,\n \"f1\": 0.4530736157718142,\n \
\ \"f1_stderr\": 0.004671764766418761\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.02880970432145565,\n \"acc_stderr\": 0.004607484283767454\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CoruNethron/neu-sai-it1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|arc:challenge|25_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|drop|3_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|gsm8k|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hellaswag|10_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T19-30-24.351070.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-21T19-30-24.351070.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- '**/details_harness|winogrande|5_2023-11-21T19-30-24.351070.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-21T19-30-24.351070.parquet'
- config_name: results
data_files:
- split: 2023_11_21T19_30_24.351070
path:
- results_2023-11-21T19-30-24.351070.parquet
- split: latest
path:
- results_2023-11-21T19-30-24.351070.parquet
---
# Dataset Card for Evaluation run of CoruNethron/neu-sai-it1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CoruNethron/neu-sai-it1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CoruNethron/neu-sai-it1](https://huggingface.co/CoruNethron/neu-sai-it1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_CoruNethron__neu-sai-it1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-21T19:30:24.351070](https://huggingface.co/datasets/open-llm-leaderboard/details_CoruNethron__neu-sai-it1_public/blob/main/results_2023-11-21T19-30-24.351070.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": {
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"acc_stderr": 0.03274268078653866,
"acc_norm": 0.6054937730425815,
"acc_norm_stderr": 0.03355540671285046,
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677154,
"mc2": 0.5148628224777658,
"mc2_stderr": 0.015540287053669583,
"em": 0.3584312080536913,
"em_stderr": 0.004910934869746984,
"f1": 0.4530736157718142,
"f1_stderr": 0.004671764766418761
},
"harness|arc:challenge|25": {
"acc": 0.5708191126279863,
"acc_stderr": 0.014464085894870653,
"acc_norm": 0.6126279863481229,
"acc_norm_stderr": 0.01423587248790987
},
"harness|hellaswag|10": {
"acc": 0.6184027086237801,
"acc_stderr": 0.00484785754695748,
"acc_norm": 0.8138816968731328,
"acc_norm_stderr": 0.0038840668811314745
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901408,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901408
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
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"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6452830188679245,
"acc_stderr": 0.02944517532819959,
"acc_norm": 0.6452830188679245,
"acc_norm_stderr": 0.02944517532819959
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6944444444444444,
"acc_stderr": 0.03852084696008534,
"acc_norm": 0.6944444444444444,
"acc_norm_stderr": 0.03852084696008534
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.3,
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},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6242774566473989,
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"acc_norm": 0.6242774566473989,
"acc_norm_stderr": 0.036928207672648664
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.29411764705882354,
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"acc_norm_stderr": 0.04533838195929775
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
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},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.502127659574468,
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"acc_norm_stderr": 0.03268572658667492
},
"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.3835978835978836,
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},
"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.7064516129032258,
"acc_norm_stderr": 0.025906087021319295
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm_stderr": 0.035083705204426656
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.6386554621848739,
"acc_norm_stderr": 0.031204691225150016
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
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"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
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"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8073394495412844,
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"acc_norm": 0.8073394495412844,
"acc_norm_stderr": 0.016909276884936066
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"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.39351851851851855,
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"acc_norm": 0.39351851851851855,
"acc_norm_stderr": 0.03331747876370312
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
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"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.02732547096671632
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"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
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"harness|hendrycksTest-human_aging|5": {
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},
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.6635802469135802,
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.8,
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727668,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727668
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677154,
"mc2": 0.5148628224777658,
"mc2_stderr": 0.015540287053669583
},
"harness|winogrande|5": {
"acc": 0.7750591949486977,
"acc_stderr": 0.011735043564126735
},
"harness|drop|3": {
"em": 0.3584312080536913,
"em_stderr": 0.004910934869746984,
"f1": 0.4530736157718142,
"f1_stderr": 0.004671764766418761
},
"harness|gsm8k|5": {
"acc": 0.02880970432145565,
"acc_stderr": 0.004607484283767454
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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0.24433399736881... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bkn1/AIVOICES | bkn1 | 2023-11-21T19:58:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T19:58:57Z | 2023-11-21T19:53:30.000Z | 2023-11-21T19:53:30 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
severo/doc-image-1 | severo | 2023-11-21T21:29:35Z | 0 | 0 | null | [
"size_categories:n<1K",
"region:us"
] | 2023-11-21T21:29:35Z | 2023-11-21T20:02:57.000Z | 2023-11-21T20:02:57 | ---
size_categories:
- n<1K
---
# [doc] image dataset 1
This dataset contains 4 jpeg files at the root. | [
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0.347106575965881... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Admin08077/oo | Admin08077 | 2023-11-21T20:37:29Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-21T20:37:29Z | 2023-11-21T20:35:49.000Z | 2023-11-21T20:35:49 | ---
license: other
license_name: citibankdemo
license_link: LICENSE
---
| [
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Admin08077/oo1 | Admin08077 | 2023-11-21T20:41:01Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-21T20:41:01Z | 2023-11-21T20:40:14.000Z | 2023-11-21T20:40:14 | ---
license: other
license_name: citibankdemo
license_link: LICENSE
---
| [
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Oumar199/Nalohou_climatic_time_series | Oumar199 | 2023-11-23T14:56:51Z | 0 | 0 | null | [
"task_categories:time-series-forecasting",
"language:en",
"region:us"
] | 2023-11-23T14:56:51Z | 2023-11-21T20:49:51.000Z | 2023-11-21T20:49:51 | ---
task_categories:
- time-series-forecasting
language:
- en
pretty_name: Sub-Saharan-Time-Series-Forecasting
--- | [
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JACINTO223/ZORO | JACINTO223 | 2023-11-21T21:01:35Z | 0 | 0 | null | [
"license:openrail",
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] | 2023-11-21T21:01:35Z | 2023-11-21T21:00:06.000Z | 2023-11-21T21:00:06 | ---
license: openrail
---
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ctoraman/large-scale-hate-speech | ctoraman | 2023-11-21T21:18:26Z | 0 | 0 | null | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"language:tr",
"license:cc",
"hate-speech",
"hatespeech",
"hate-speech-detection",
"hatespeechdetection",
"region:us"
] | 2023-11-21T21:18:26Z | 2023-11-21T21:07:33.000Z | 2023-11-21T21:07:33 | ---
license: cc
task_categories:
- text-classification
language:
- en
- tr
tags:
- hate-speech
- hatespeech
- hate-speech-detection
- hatespeechdetection
pretty_name: h
size_categories:
- 100K<n<1M
---
This repository contains the utilized dataset in the LREC 2022 paper "Large-Scale Hate Speech Detection with Cross-Domain Transfer". This study mainly focuses hate speech detection in Turkish and English. In addition, domain transfer success between hate domains is also examined.
There are two dataset versions.
Dataset v1: The original dataset that includes 100,000 tweets per English and Turkish, published in LREC 2022. The annotations with more than 60% agreement are included.
Dataset v2: A more reliable dataset version that includes 68,597 tweets for English and 60,310 for Turkish. The annotations with more than 80% agreement are included.
For more details: https://github.com/avaapm/hatespeech/ | [
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severo/doc-image-2 | severo | 2023-11-21T21:26:13Z | 0 | 0 | null | [
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size_categories:
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---
# [doc] image dataset 2
This dataset contains 4 image files at the root, using 4 different image formats: jpeg, png, tiff, webp. | [
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sujanevo/human-body | sujanevo | 2023-11-21T21:15:19Z | 0 | 0 | null | [
"size_categories:n<1K",
"language:en",
"health",
"body parts",
"region:us"
] | 2023-11-21T21:15:19Z | 2023-11-21T21:14:20.000Z | 2023-11-21T21:14:20 | ---
language:
- en
tags:
- health
- body parts
size_categories:
- n<1K
--- | [
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TheGreatP/minhavoz2 | TheGreatP | 2023-11-21T21:17:12Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-21T21:17:12Z | 2023-11-21T21:16:03.000Z | 2023-11-21T21:16:03 | ---
license: openrail
---
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ctoraman/BilCat-news-classification | ctoraman | 2023-11-21T21:46:22Z | 0 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:tr",
"license:cc",
"news-classification",
"text-classification",
"news-categorization",
"text-categorization",
"news-articles",
"region:us"
] | 2023-11-21T21:46:22Z | 2023-11-21T21:25:00.000Z | 2023-11-21T21:25:00 | ---
license: cc
task_categories:
- text-classification
language:
- tr
tags:
- news-classification
- text-classification
- news-categorization
- text-categorization
- news-articles
size_categories:
- 1K<n<10K
---
BilCat: Bilkent Text Classification (News Categorization) Dataset
7540 Turkish news articles (Milliyet and TRT merged) with category labels (Dunya, Ekonomi, Politika, KulturSanat, Saglik, Spor, Turkiye, Yazarlar).
Column header is the first line.
Other details are at https://github.com/BilkentInformationRetrievalGroup/BilCat/
Citation:
C. Toraman, F. Can and S. Koçberber. Developing a text categorization template for Turkish news portals. 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, 2011, pp. 379-383. DOI: 10.1109/INISTA.2011.5946096 | [
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ajmangus/qm_mixture_1.0e | ajmangus | 2023-11-28T02:41:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T02:41:40Z | 2023-11-21T21:26:14.000Z | 2023-11-21T21:26:14 | ---
dataset_info:
features:
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num_examples: 59997
download_size: 19912524
dataset_size: 86624391
configs:
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data_files:
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path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
| [
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NoobPROBR/JoaoStephanini | NoobPROBR | 2023-11-21T21:27:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-21T21:27:29Z | 2023-11-21T21:26:16.000Z | 2023-11-21T21:26:16 | ---
license: openrail
---
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ajmangus/qm_alice_mixture_1.0e | ajmangus | 2023-11-28T02:42:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T02:42:37Z | 2023-11-21T21:26:27.000Z | 2023-11-21T21:26:27 | ---
dataset_info:
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dtype: int64
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sequence: string
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download_size: 7642902
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configs:
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data_files:
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path: data/train-*
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path: data/validation-*
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path: data/test-*
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| [
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DeliberatorArchiver/movie_binaries_0012 | DeliberatorArchiver | 2023-11-22T22:08:58Z | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2023-11-22T22:08:58Z | 2023-11-21T21:35:04.000Z | 2023-11-21T21:35:04 | ---
license: cc-by-nc-nd-4.0
---
| [
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ctoraman/deprem-tweet-dataset | ctoraman | 2023-11-21T21:55:37Z | 0 | 0 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:tr",
"license:cc",
"disaster-relief",
"disaster",
"earthquake",
"tweets",
"deprem",
"tweet-classification",
"ner",
"arxiv:2302.13403",
"region:us"
] | 2023-11-21T21:55:37Z | 2023-11-21T21:52:08.000Z | 2023-11-21T21:52:08 | ---
license: cc
task_categories:
- text-classification
- token-classification
language:
- tr
tags:
- disaster-relief
- disaster
- earthquake
- tweets
- deprem
- tweet-classification
- ner
size_categories:
- 1K<n<10K
---
Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster
The annotated dataset is given at dataset.tsv. We annotate 1,000 tweets in Turkish if tweets call for help (i.e. request rescue, supply, or donation), and their entity tags (person, city, address, status).
Column Name Description
label Human annotation if tweet calls for help (binary classification)
entities Human annotation of entity tags (i.e. person, city, address, and status). The format is [START_INDEX]:[END_INDEX]%[TAG_TYPE].
tweet_id Tweet ID from Twitter API.
Other details can be found at https://github.com/avaapm/deprem
Citation
If you make use of this dataset, please cite following paper.
@misc{toraman2023earthquake,
doi = {10.48550/ARXIV.2302.13403},
url = {https://arxiv.org/abs/2302.13403},
author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan},
keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
} | [
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ctoraman/mide22-misinfo | ctoraman | 2023-11-21T22:01:28Z | 0 | 0 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"language:tr",
"license:cc",
"misinformation-detection",
"misinformation",
"disinformation",
"disinformation-detection",
"misinfo",
"fakenews",
"fake-news",
"tweets",
"arxiv:2210.05401",
"region:us"
] | 2023-11-21T22:01:28Z | 2023-11-21T21:56:31.000Z | 2023-11-21T21:56:31 | ---
license: cc
language:
- en
- tr
task_categories:
- text-classification
tags:
- misinformation-detection
- misinformation
- disinformation
- disinformation-detection
- misinfo
- fakenews
- fake-news
- tweets
size_categories:
- 10K<n<100K
---
Mide22 Dataset published at "Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees"
The dataset is composed of 10,348 tweets: 5,284 for English and 5,064 for Turkish. Tweets in the dataset cover different topics: the Russia-Ukraine war, COVID-19 pandemic, Refugees, and additional miscellaneous events. Three misinformation label of the tweet are also given. Since we follow Twitter's Terms and Conditions, we publish tweet IDs not the tweet content directly. Explanations of the columns of the file are as follows:
Column Name Description
Topic Topic of the tweet: Ukraine, Covid, Refugees or Misc
Event Event of the tweet: EN01-EN40 in English and TR01-TR40 in Turkish
Label Label of the tweet: True, False, or Other
Tweet_id Twitter ID of the tweet
Other details are at https://github.com/avaapm/mide22/
Citation
If you make use of this dataset, please cite following paper.
@misc{toraman2022good,
title={Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees},
author={Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Fazli Can},
year={2022},
eprint={2210.05401},
archivePrefix={arXiv},
primaryClass={cs.SI}
} | [
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hotal/emergency_combined_prompt | hotal | 2023-11-21T22:04:00Z | 0 | 1 | null | [
"region:us"
] | 2023-11-21T22:04:00Z | 2023-11-21T22:03:57.000Z | 2023-11-21T22:03:57 | ---
dataset_info:
features:
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dtype: string
splits:
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num_bytes: 27433336.0
num_examples: 26488
download_size: 4827292
dataset_size: 27433336.0
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emergency_combined_prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ctoraman/tweet-topic-detection | ctoraman | 2023-11-21T22:21:04Z | 0 | 0 | null | [
"task_categories:text-classification",
"language:en",
"license:cc",
"tweet-classification",
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"tweets",
"tweet-length",
"region:us"
] | 2023-11-21T22:21:04Z | 2023-11-21T22:11:11.000Z | 2023-11-21T22:11:11 | ---
license: cc
task_categories:
- text-classification
language:
- en
tags:
- tweet-classification
- topic-detection
- topic-classification
- topics
- tweets
- tweet-length
---
Published tweet dataset used in "Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs" includes tweet id and corresponding topic number.
Topic numbers encoded as follows:
Topic Topic Number
BLM Movement 0
Covid-19 1
K-Pop 2
Bollywood 3
Gaming 4
U.S. Politics 5
Out-of-Topic 6
In total, there are 354,310 tweet instances.
More details can be found at https://github.com/avaapm/ECIR2021/
Citation
If you make use of these tools, please cite following paper.
@inproceedings{DBLP:conf/ecir/SahinucT21,
author = {Furkan {\c{S}}ahinu{\c{c}} and Cagri Toraman},
title = {Tweet Length Matters: {A} Comparative Analysis on Topic Detection in Microblogs},
booktitle = {Advances in Information Retrieval - 43rd European Conference on {IR} Research, {ECIR} 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
volume = {12657},
pages = {471--478},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-72240-1\_50},
doi = {10.1007/978-3-030-72240-1\_50},
} | [
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0.16123341023921... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pavitemple/youtube-data | pavitemple | 2023-11-21T22:13:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T22:13:22Z | 2023-11-21T22:13:16.000Z | 2023-11-21T22:13:16 | Entry not found | [
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pavitemple/youtube-videos | pavitemple | 2023-11-21T22:14:38Z | 0 | 0 | null | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pavitemple/test-videos | pavitemple | 2023-11-28T19:56:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-28T19:56:22Z | 2023-11-21T22:15:20.000Z | 2023-11-21T22:15:20 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kaue123456/PopeyeOrlandoDrummond | Kaue123456 | 2023-11-21T22:46:46Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-21T22:46:46Z | 2023-11-21T22:45:53.000Z | 2023-11-21T22:45:53 | ---
license: openrail
---
| [
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someone13574/fictional-worlds-v2-seeds | someone13574 | 2023-11-21T23:14:11Z | 0 | 0 | null | [
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-21T23:14:11Z | 2023-11-21T23:04:00.000Z | 2023-11-21T23:04:00 | ---
license: apache-2.0
size_categories:
- 10K<n<100K
language:
- en
---
# Worldbuilding Seeds
30k (three per topic) seed phrases for worldbuilding or synthetic datasets. Filtering for quality and format (should be free of anything but the seeds).
#### Details
Generated with Zephyr-beta-7b, mirostat sampling with tau=8.0.
#### Prompt
Fewshot prompting + an instruction was used for generation. Topics are from wikipedia vital article titles (level 4). Some topics were removed due to inability to generate good seeds. Three seeds are generated at the same time per topic because it makes more diverse outputs than prompting for one.
```
<|user|>
You are fantasy world seed creator. You create short sentences which describe the core concept which a fictional fantasy world is built around. They should be unique and varied, taking inspiration from a random topic. The seed should describe what makes the world at large special in the simplest way possible, and shouldn't meantion real world proper nouns. For each topic I say after this, create 3 seeds which take different approaches. Make the worlds different from our own.
Topic: Surgery </s>
<|assistant|>
Ether blades sculpt fate.
Bloodline tattoos reveal ancestral memories.
Healing crystals forge immortal connections. </s>
<|user|>
Topic: Carl Friedrich Gauss </s>
<|assistant|>
Equations reshape the fabric of reality.
Arcane symbols unlock interdimensional gateways.
Numerical patterns summon celestial guardians. </s>
<|user|>
Topic: Thailand </s>
<|assistant|>
Elemental monks maintain cosmic balance.
Celestial elephants carry realms on their tusks.
Floating temples bridge mortal and divine. </s>
<|user|>
Topic: Phoenicia </s>
<|assistant|>
Winged ships navigate skies of aether.
Elemental sails harness storms for propulsion.
Stellar traders weave constellations into tales. </s>
<|user|>
Topic: Calligraphy </s>
<|assistant|>
Inked verses rewrite the past.
Tattooed scrolls reveal hidden destinies.
Glyphs on skin awaken dormant powers. </s>
<|user|>
Topic: Al-Khwarizmi </s>
<|assistant|>
Ciphered scripts unlock ancient gateways.
Algorithmic gardens grow sentient equations.
Time travel guided by numeric formulas. </s>
<|user|>
Topic: {topic} </s>
<|assistant|>
``` | [
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-0.529763400554657,
0.1900273710489273... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gnumanth/git-tips | gnumanth | 2023-11-21T23:23:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-21T23:23:30Z | 2023-11-21T23:15:23.000Z | 2023-11-21T23:15:23 | ---
dataset_info:
features:
- name: tip
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 7206.0898203592815
num_examples: 83
- name: test
num_bytes: 2951.8922155688624
num_examples: 34
download_size: 10514
dataset_size: 10157.982035928144
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# git-tips
> Most commonly used git tips and tricks.
This is a dataset from [git-tips](https://github.com/git-tips/tips) | [
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-0.13788565993309... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cointegrated/taiga_stripped_stihi | cointegrated | 2023-11-23T09:48:44Z | 0 | 1 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:1M<n<10M",
"language:ru",
"license:cc-by-sa-3.0",
"taiga",
"tayga",
"region:us"
] | 2023-11-23T09:48:44Z | 2023-11-21T23:37:19.000Z | 2023-11-21T23:37:19 | ---
dataset_info:
features:
- name: text
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 14185482821
num_examples: 9157973
download_size: 7745419481
dataset_size: 14185482821
license: cc-by-sa-3.0
language:
- ru
tags:
- taiga
- tayga
size_categories:
- 1M<n<10M
task_categories:
- text-generation
- fill-mask
---
# Dataset Card for "taiga_stripped_stihi"
This is a subset of the Taiga corpus (https://tatianashavrina.github.io/taiga_site), derived from the `stihi` source (a.k.a. "Poetry").
The dataset consists of plain texts, without morphological and syntactic annotation or metainformation. Apart from stripping the annotations, the texts were not modified.
For more details and analysis, and for the texts with annotation or metadata, please refer to website of the corpus.
Other subsets of Taiga: [proza](https://huggingface.co/datasets/cointegrated/taiga_stripped_proza) (fiction)
and [other sources](https://huggingface.co/datasets/cointegrated/taiga_stripped_rest) (news, subtitles, and social media).
License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
brantfetter/primary | brantfetter | 2023-11-21T23:41:30Z | 0 | 0 | null | [
"license:cc-by-nd-4.0",
"region:us"
] | 2023-11-21T23:41:30Z | 2023-11-21T23:41:30.000Z | 2023-11-21T23:41:30 | ---
license: cc-by-nd-4.0
---
| [
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c0smic1atte/rap | c0smic1atte | 2023-11-22T11:18:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T11:18:27Z | 2023-11-22T00:07:38.000Z | 2023-11-22T00:07:38 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Bluebomber182/Branch-From-Trolls | Bluebomber182 | 2023-11-22T00:13:09Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-22T00:13:09Z | 2023-11-22T00:10:17.000Z | 2023-11-22T00:10:17 | ---
license: unknown
---
| [
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-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Bluebomber182/Poppy-From-Trolls | Bluebomber182 | 2023-11-22T00:16:07Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-22T00:16:07Z | 2023-11-22T00:13:30.000Z | 2023-11-22T00:13:30 | ---
license: unknown
---
| [
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-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Cem13/test0 | Cem13 | 2023-11-22T00:39:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T00:39:56Z | 2023-11-22T00:39:56.000Z | 2023-11-22T00:39:56 | Entry not found | [
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-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Birchlabs/sdxl-latents-ffhq | Birchlabs | 2023-11-22T22:39:32Z | 0 | 0 | null | [
"arxiv:1812.04948",
"region:us"
] | 2023-11-22T22:39:32Z | 2023-11-22T00:49:38.000Z | 2023-11-22T00:49:38 | [https://github.com/NVlabs/ffhq-dataset](FFHQ) samples encoded to float16 SDXL latents via [Ollin VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix).
Dataset created using [this script](https://github.com/Birch-san/sdxl-diffusion-decoder/blob/main/script/make_sdxl_latent_dataset.py).
VAE encoder used NATTEN attention, kernel size 17.
Didn't bother saving mean & logvar, because variance is low enough it's not worth the doubling of filesize to retain.
Sampled from diagonal gaussian distribution, saved the resulting latents.
Also kept the original image.
Schema/usage:
```python
from typing import TypedDict, Iterator
from webdataset import WebDataset
Sample = TypedDict('Sample', {
'__key__': str,
'__url__': str,
'img.png': bytes, # PIL image, serialized. 1024*1024px
'latent.pth': bytes, # FloatTensor, serialized. 128*128 latents
})
it: Iterator[Sample] = WebDataset('{00000..00035}.tar')
for sample in it:
pass
```
```
# avg/val.pt (mean):
[-2.8982300758361816, -0.9609659910202026, 0.2416578084230423, -0.307400107383728]
# avg/sq.pt:
[65.80902099609375, 32.772762298583984, 36.080204010009766, 25.072017669677734]
# std
# (sq - val**2)**.5
[7.5768914222717285, 5.643518924713135, 6.001816749572754, 4.997751712799072]
# 1/std
[0.13198024034500122, 0.17719440162181854, 0.16661621630191803, 0.2000899761915207]
```
## Flickr-Faces-HQ Dataset (FFHQ)
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN):
> **A Style-Based Generator Architecture for Generative Adversarial Networks**<br>
> Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)<br>
> https://arxiv.org/abs/1812.04948
The dataset consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from [Flickr](https://www.flickr.com/), thus inheriting all the biases of that website, and automatically aligned and cropped using [dlib](http://dlib.net/). Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally [Amazon Mechanical Turk](https://www.mturk.com/) was used to remove the occasional statues, paintings, or photos of photos.
Please note that this dataset is not intended for, and should not be used for, development or improvement of facial recognition technologies. For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/)
## Licenses
The individual images were published in Flickr by their respective authors under either [Creative Commons BY 2.0](https://creativecommons.org/licenses/by/2.0/), [Creative Commons BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/), [Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/), [Public Domain CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), or [U.S. Government Works](http://www.usa.gov/copyright.shtml) license. All of these licenses allow **free use, redistribution, and adaptation for non-commercial purposes**. However, some of them require giving **appropriate credit** to the original author, as well as **indicating any changes** that were made to the images. The license and original author of each image are indicated in the metadata.
* [https://creativecommons.org/licenses/by/2.0/](https://creativecommons.org/licenses/by/2.0/)
* [https://creativecommons.org/licenses/by-nc/2.0/](https://creativecommons.org/licenses/by-nc/2.0/)
* [https://creativecommons.org/publicdomain/mark/1.0/](https://creativecommons.org/publicdomain/mark/1.0/)
* [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/)
* [http://www.usa.gov/copyright.shtml](http://www.usa.gov/copyright.shtml)
The dataset itself (including JSON metadata, download script, and documentation) is made available under [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license by NVIDIA Corporation. You can **use, redistribute, and adapt it for non-commercial purposes**, as long as you (a) give appropriate credit by **citing our paper**, (b) **indicate any changes** that you've made, and (c) distribute any derivative works **under the same license**.
* [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/)
| [
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0.023063126951456... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kevv17/Chster | Kevv17 | 2023-11-22T00:58:27Z | 0 | 0 | null | [
"license:cc",
"region:us"
] | 2023-11-22T00:58:27Z | 2023-11-22T00:58:27.000Z | 2023-11-22T00:58:27 | ---
license: cc
---
| [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
m111styd4y/marininhasena | m111styd4y | 2023-11-22T01:00:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-22T01:00:29Z | 2023-11-22T00:59:10.000Z | 2023-11-22T00:59:10 | ---
license: openrail
---
| [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
typeof/alphas2 | typeof | 2023-11-22T01:02:44Z | 0 | 0 | null | [
"region:us"
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deboramachadoandrade/sft_dataset_rlaif | deboramachadoandrade | 2023-11-22T01:08:21Z | 0 | 0 | null | [
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jseims/sft_dataset_rlaif | jseims | 2023-11-22T01:10:13Z | 0 | 0 | null | [
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JuanKO/sft_dataset_rlaif | JuanKO | 2023-11-22T01:20:19Z | 0 | 0 | null | [
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someone13574/fictional-worlds-v2-geography | someone13574 | 2023-11-23T01:43:58Z | 0 | 0 | null | [
"license:apache-2.0",
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] | 2023-11-23T01:43:58Z | 2023-11-22T01:23:36.000Z | 2023-11-22T01:23:36 | ---
license: apache-2.0
---
Intermediate step for the fictional-worlds-v2 dataset. Under construction. | [
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Vezora/test2x | Vezora | 2023-11-22T01:59:50Z | 0 | 0 | null | [
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license: apache-2.0
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mediabiasgroup/bias_lexicon | mediabiasgroup | 2023-11-22T02:08:25Z | 0 | 0 | null | [
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"region:us"
] | 2023-11-22T02:08:25Z | 2023-11-22T02:00:30.000Z | 2023-11-22T02:00:30 | ---
license: cc-by-nc-4.0
---
# Description:
The bias_lexicon file is a comprehensive dictionary of biased words. This lexicon is designed to assist in identifying and analyzing biased language in various texts. The dictionary encompasses a wide range of words that are often associated with biased expressions, including those related to gender, race, age, and other social categories.
# Usage:
This resource can be pivotal for crafting features in natural language processing (NLP) tasks, sentiment analysis, and in developing models that aim to detect or mitigate biased language. It's particularly useful in research and applications focusing on ethical AI and fair representation in language models. | [
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nampdn-ai/VNReasoningEval | nampdn-ai | 2023-11-23T14:16:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-23T14:16:24Z | 2023-11-22T02:01:32.000Z | 2023-11-22T02:01:32 | The Vietnamese Cognitive Perspective Analysis and Reasoning Evaluation (CPARE) Dataset is a unique collection designed to evaluate and enhance understanding of theory of mind through the lens of reasoning.
This dataset is tailored for research in cognitive science, artificial intelligence, and psychology, providing scenarios that require understanding of diverse perspectives and mental states of a Language Model. | [
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kamakani/sft_dataset_rlaif | kamakani | 2023-11-22T02:02:56Z | 0 | 0 | null | [
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lillybak/sft_dataset_rlaif | lillybak | 2023-11-22T02:19:14Z | 0 | 0 | null | [
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Vinnyh589/cantorescolecao | Vinnyh589 | 2023-11-24T20:43:59Z | 0 | 0 | null | [
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Nickkkkkk/Beatrix | Nickkkkkk | 2023-11-22T02:39:19Z | 0 | 0 | null | [
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license: openrail
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Deni016/iagoku | Deni016 | 2023-11-22T23:12:16Z | 0 | 0 | null | [
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typeof/alphas3 | typeof | 2023-11-23T16:29:58Z | 0 | 0 | null | [
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Fael123/ModelMCPOZErodo | Fael123 | 2023-11-22T02:58:01Z | 0 | 0 | null | [
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license: openrail
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errolseo/requests | errolseo | 2023-11-24T00:16:32Z | 0 | 0 | null | [
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errolseo/results | errolseo | 2023-11-24T07:51:44Z | 0 | 0 | null | [
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Mzh666/DONG1 | Mzh666 | 2023-11-22T05:25:02Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-22T05:25:02Z | 2023-11-22T03:14:53.000Z | 2023-11-22T03:14:53 | ---
license: mit
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Alucard1681/Vozdoazir | Alucard1681 | 2023-11-22T03:19:11Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-22T03:19:11Z | 2023-11-22T03:18:07.000Z | 2023-11-22T03:18:07 | ---
license: openrail
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Praghxx/Litlegiela | Praghxx | 2023-11-22T03:22:53Z | 0 | 0 | null | [
"license:openrail",
"region:us"
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gagan3012/finner | gagan3012 | 2023-11-22T03:26:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T03:26:59Z | 2023-11-22T03:26:56.000Z | 2023-11-22T03:26:56 | ---
dataset_info:
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xidol/indo | xidol | 2023-11-22T03:47:15Z | 0 | 0 | null | [
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caiosoares26/freefiri | caiosoares26 | 2023-11-22T04:08:27Z | 0 | 0 | null | [
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"region:us"
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license: openrail
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Yeobin/ShareGPT_tokenized | Yeobin | 2023-11-22T04:07:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T04:07:54Z | 2023-11-22T04:07:54.000Z | 2023-11-22T04:07:54 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
xidol/3DH | xidol | 2023-11-24T10:56:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-24T10:56:50Z | 2023-11-22T04:12:49.000Z | 2023-11-22T04:12:49 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AmelieSchreiber/interaction_pairs | AmelieSchreiber | 2023-11-22T04:23:39Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-22T04:23:39Z | 2023-11-22T04:16:18.000Z | 2023-11-22T04:16:18 | ---
license: mit
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Am4nu3l/amharic-language-voices | Am4nu3l | 2023-11-22T04:40:09Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-22T04:40:09Z | 2023-11-22T04:36:44.000Z | 2023-11-22T04:36:44 | ---
license: mit
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DollyDayko/RetriBooru | DollyDayko | 2023-11-22T06:10:00Z | 0 | 0 | null | [
"task_categories:image-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | 2023-11-22T06:10:00Z | 2023-11-22T04:47:37.000Z | 2023-11-22T04:47:37 | ---
license: mit
task_categories:
- image-to-image
language:
- en
size_categories:
- 100K<n<1M
---
# Retrieving Conditions from Reference Images for Diffusion Models
This is the HuggingFace Dataset repo for RetriBooru dataset, containing json metadata (coming soon!). Please navigate to our [GitHub Project Page](https://haorantang.github.io/retribooru/) for details about the proposed dataset and method. | [
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-0.06172534450888634,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ppxscal/wikigraph_pairs | ppxscal | 2023-11-22T05:12:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T05:12:28Z | 2023-11-22T04:50:25.000Z | 2023-11-22T04:50:25 | ---
dataset_info:
features:
- name: Query Alias
dtype: string
- name: Query
dtype: string
- name: Relation
dtype: string
- name: Content
dtype: string
- name: Content Alias
dtype: string
splits:
- name: train
num_bytes: 16467352698
num_examples: 4553783
download_size: 4844940237
dataset_size: 16467352698
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
pairs extracted from https://deepgraphlearning.github.io/project/wikidata5m | [
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0.0190535914152860... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sankovic/shozz | sankovic | 2023-11-22T04:52:20Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-22T04:52:20Z | 2023-11-22T04:51:46.000Z | 2023-11-22T04:51:46 | ---
license: openrail
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dangkhoadl/ICASSP2024-Acoustic_Scattering_AI-Noninvasive_Object_Classifications | dangkhoadl | 2023-11-22T10:40:22Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-22T10:40:22Z | 2023-11-22T05:12:08.000Z | 2023-11-22T05:12:08 | ---
license: apache-2.0
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ACCC1380/openl | ACCC1380 | 2023-11-22T05:19:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T05:19:27Z | 2023-11-22T05:19:04.000Z | 2023-11-22T05:19:04 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Deojoandco/capstone_fromgpt_without_gold_v3 | Deojoandco | 2023-11-22T05:19:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T05:19:13Z | 2023-11-22T05:19:10.000Z | 2023-11-22T05:19:10 | ---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
splits:
- name: test
num_bytes: 20712
num_examples: 12
download_size: 22423
dataset_size: 20712
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.3366600573062... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
deepghs/quality_rlhf | deepghs | 2023-11-24T08:59:25Z | 0 | 0 | null | [
"task_categories:reinforcement-learning",
"license:openrail",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-11-24T08:59:25Z | 2023-11-22T05:28:57.000Z | 2023-11-22T05:28:57 | ---
license: openrail
task_categories:
- reinforcement-learning
tags:
- art
- not-for-all-audiences
--- | [
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-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ErhaChen/game_icon_diablo_style | ErhaChen | 2023-11-22T05:41:30Z | 0 | 0 | null | [
"task_categories:text-to-image",
"license:apache-2.0",
"game icon",
"diablo",
"style",
"lora",
"region:us"
] | 2023-11-22T05:41:30Z | 2023-11-22T05:34:02.000Z | 2023-11-22T05:34:02 | ---
license: apache-2.0
task_categories:
- text-to-image
tags:
- game icon
- diablo
- style
- lora
--- | [
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0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/rwbyhyousetsuteikoku | BangumiBase | 2023-11-22T07:20:23Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-22T07:20:23Z | 2023-11-22T05:34:44.000Z | 2023-11-22T05:34:44 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Rwby - Hyousetsu Teikoku
This is the image base of bangumi RWBY - Hyousetsu Teikoku, we detected 29 characters, 2529 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 229 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 49 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 13 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 38 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 76 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 18 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 14 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 10 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 550 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 19 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 9 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 322 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 25 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 55 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 177 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 376 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 114 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 10 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 6 | [Download](23/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 24 | 72 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 23 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 7 | [Download](26/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 27 | 14 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 174 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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0.1531178057193756,
0.2299799621105194,
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0.6568326354026794,
0.48964816331863403,
-0.9566623568534851,
-0.8703991174697876,
-0.6761183142662048,
0.5061876773834229... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/goldenkamuy | BangumiBase | 2023-11-22T12:02:49Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-22T12:02:49Z | 2023-11-22T05:35:28.000Z | 2023-11-22T05:35:28 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Golden Kamuy
This is the image base of bangumi Golden Kamuy, we detected 44 characters, 8914 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2560 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 737 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 50 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1259 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 95 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 250 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 227 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 379 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 178 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 243 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 39 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 69 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 110 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 63 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 219 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 24 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 36 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 1180 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 54 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 45 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 185 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 151 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 27 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 16 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 42 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 42 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 55 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 14 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 58 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 33 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 24 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 53 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 49 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 15 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 49 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 38 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 15 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 53 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 24 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 83 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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0.5357326865196228,
-0.9242834448814392,
-0.8643443584442139,
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0.51477086544... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
adowu/polish_sentences | adowu | 2023-11-23T03:29:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-23T03:29:38Z | 2023-11-22T05:59:46.000Z | 2023-11-22T05:59:46 | Entry not found | [
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KADUZADA/MANIVELA | KADUZADA | 2023-11-22T06:03:25Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-22T06:03:25Z | 2023-11-22T06:02:56.000Z | 2023-11-22T06:02:56 | ---
license: openrail
---
| [
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sankovic/shozzz | sankovic | 2023-11-22T06:13:25Z | 0 | 0 | null | [
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alexandonian/VideoInstruct-Dataset | alexandonian | 2023-11-22T10:24:41Z | 0 | 0 | null | [
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] | 2023-11-22T10:24:41Z | 2023-11-22T06:16:25.000Z | 2023-11-22T06:16:25 | Entry not found | [
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Subhadeep/English_IITM_Check_dataset_en_pseudo_labelled | Subhadeep | 2023-11-22T10:57:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T10:57:00Z | 2023-11-22T06:19:25.000Z | 2023-11-22T06:19:25 | ---
dataset_info:
config_name: en
features:
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dtype:
audio:
sampling_rate: 16000
- name: path
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dtype: string
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sequence: int64
splits:
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num_bytes: 618348530.525
num_examples: 3009
download_size: 606619139
dataset_size: 618348530.525
configs:
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data_files:
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path: en/train-*
---
| [
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CryptoBear/dataset-cryptobear | CryptoBear | 2023-11-22T06:51:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T06:51:18Z | 2023-11-22T06:51:18.000Z | 2023-11-22T06:51:18 | Entry not found | [
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sabasazad/sft_dataset_rlaif | sabasazad | 2023-11-22T07:17:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T07:17:41Z | 2023-11-22T06:54:22.000Z | 2023-11-22T06:54:22 | ---
dataset_info:
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splits:
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num_examples: 5
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configs:
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data_files:
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---
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deepghs/anime_style_ages | deepghs | 2023-11-28T14:34:55Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-28T14:34:55Z | 2023-11-22T06:58:00.000Z | 2023-11-22T06:58:00 | ---
license: openrail
---
| [
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collabora/indic-superb | collabora | 2023-11-22T08:13:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T08:13:35Z | 2023-11-22T07:35:07.000Z | 2023-11-22T07:35:07 | ---
configs:
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data_files:
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dataset_info:
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num_examples: 872
download_size: 46065024050
dataset_size: 48368858261.64
---
# Dataset Card for "indic-superb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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dipudl/ms-marco-llama-gptq-prompts | dipudl | 2023-11-22T13:06:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T13:06:27Z | 2023-11-22T07:41:35.000Z | 2023-11-22T07:41:35 | ---
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configs:
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---
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bahiags/Briggs_flow | bahiags | 2023-11-22T07:50:05Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-22T07:50:05Z | 2023-11-22T07:48:46.000Z | 2023-11-22T07:48:46 | ---
license: openrail
---
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c0smic1atte/krap1 | c0smic1atte | 2023-11-22T07:51:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T07:51:55Z | 2023-11-22T07:51:55.000Z | 2023-11-22T07:51:55 | Entry not found | [
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