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beki/privy
|
beki
| 2023-04-25T21:45:06Z | 73 | 11 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:100K<n<200K",
"size_categories:300K<n<400K",
"language:en",
"license:mit",
"pii-detection",
"region:us"
] |
[
"token-classification"
] | 2022-09-16T04:41:28Z |
---
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<200K
- 300K<n<400K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
tags:
- pii-detection
train-eval-index:
- config: privy-small
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
metrics:
- type: seqeval
name: seqeval
pretty_name: Privy English
---
# Dataset Card for "privy-english"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy)
### Dataset Summary
A synthetic PII dataset generated using [Privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy), a tool which parses OpenAPI specifications and generates synthetic request payloads, searching for keywords in API schema definitions to select appropriate data providers. Generated API payloads are converted to various protocol trace formats like JSON and SQL to approximate the data developers might encounter while debugging applications.
This labelled PII dataset consists of protocol traces (JSON, SQL (PostgreSQL, MySQL), HTML, and XML) generated from OpenAPI specifications and includes 60+ PII types.
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) and PII classification.
### Label Scheme
<details>
<summary>View label scheme (26 labels for 60 PII data providers)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `PERSON`, `LOCATION`, `NRP`, `DATE_TIME`, `CREDIT_CARD`, `URL`, `IBAN_CODE`, `US_BANK_NUMBER`, `PHONE_NUMBER`, `US_SSN`, `US_PASSPORT`, `US_DRIVER_LICENSE`, `IP_ADDRESS`, `US_ITIN`, `EMAIL_ADDRESS`, `ORGANIZATION`, `TITLE`, `COORDINATE`, `IMEI`, `PASSWORD`, `LICENSE_PLATE`, `CURRENCY`, `ROUTING_NUMBER`, `SWIFT_CODE`, `MAC_ADDRESS`, `AGE` |
</details>
### Languages
English
## Dataset Structure
### Data Instances
A sample:
```
{
"full_text": "{\"full_name_female\": \"Bethany Williams\", \"NewServerCertificateName\": \"\", \"NewPath\": \"\", \"ServerCertificateName\": \"dCwMNqR\", \"Action\": \"\", \"Version\": \"u zNS zNS\"}",
"masked": "{\"full_name_female\": \"{{name_female}}\", \"NewServerCertificateName\": \"{{string}}\", \"NewPath\": \"{{string}}\", \"ServerCertificateName\": \"{{string}}\", \"Action\": \"{{string}}\", \"Version\": \"{{string}}\"}",
"spans": [
{
"entity_type": "PERSON",
"entity_value": "Bethany Williams",
"start_position": 22,
"end_position": 38
}
],
"template_id": 51889,
"metadata": null
}
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@online{WinNT,
author = {Benjamin Kilimnik},
title = {{Privy} Synthetic PII Protocol Trace Dataset},
year = 2022,
url = {https://huggingface.co/datasets/beki/privy},
}
```
### Contributions
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
arpelarpe/nota
|
arpelarpe
| 2022-10-11T07:56:49Z | 73 | 2 |
[
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:da",
"license:cc0-1.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-10-11T06:37:42Z |
---
pretty_name: Nota
license:
- cc0-1.0
language:
- da
multilinguality:
- monolingual
task_categories:
- automatic-speech-recognition
---
# Dataset Card Nota Lyd- og tekstdata
## 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)
- [Disclaimer](#disclaimer)
- [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)
## Dataset Description
- **Homepage:** https://sprogteknologi.dk/dataset/notalyd-ogtekstdata
- **Data Storage Url:** https://sprogtek-ressources.digst.govcloud.dk/nota/
- **Point of Contact:** [email protected]
### Dataset Summary
This data was created by the public institution Nota (https://nota.dk/), which is part of the Danish Ministry of Culture. Nota has a library audiobooks and audiomagazines for people with reading or sight disabilities. Nota also produces a number of audiobooks and audiomagazines themselves.
The dataset consists of .wav and .txt files from Nota's audiomagazines "Inspiration" and "Radio/TV".
The dataset has been published as a part of the initiative sprogteknologi.dk, within the Danish Agency for Digital Government (www.digst.dk).
336 GB available data, containing voice recordings and accompanying transcripts.
Each publication has been segmented into bits of 2 - 50 seconds .wav files with an accompanying transcription
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Danish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called path and its sentence.
`
{'path': '<path_to_clip>.wav', 'sentence': 'Dette er et eksempel', 'audio': {'path': <path_to_clip>.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 44100}
`
### Data Fields
path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
sentence: The sentence that was read by the speaker
### Data Splits
The material has for now only a train split. As this is very early stage of the dataset, splits might be introduced at a later stage.
## Dataset Creation
### Disclaimer
There might be smaller discrepancies between the .wav and .txt files. Therefore, there might be issues in the alignment of timestamps, text and sound files.
There are no strict rules as to how readers read aloud non-letter characters (i.e. numbers, €, $, !, ?). These symbols can be read differently throughout the dataset.
### 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 is made public and free to use. Recorded individuals has by written contract accepted and agreed to the publication of their recordings.
Other names appearing in the dataset are already publically known individuals (i.e. TV or Radio host names). Their names are not to be treated as sensitive or personal data in the context of this dataset.
## 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
https://sprogteknologi.dk/
Contact [email protected] if you have questions regarding use of data.
They gladly receive inputs and ideas on how to distribute the data.
### Licensing Information
[CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/)
###
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] |
bigbio/hprd50
|
bigbio
| 2022-12-22T15:44:46Z | 73 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:08:57Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: HPRD50
homepage:
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for HPRD50
## Dataset Description
- **Homepage:**
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE,NER
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
splitting each abstract into sentences, and in each sentence there may be entities and
interactions between those entities. In this particular dataset, entities are all
proteins and interactions are thus protein-protein interactions.
Moreover, all entities are normalized to the HPRD database. These normalized terms are
stored in each entity's 'type' attribute in the source XML. This means the dataset can
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
entity.
Because the dataset contains entities and relations, it is suitable for Named Entity
Recognition and Relation Extraction.
## Citation Information
```
@article{fundel2007relex,
title={RelEx—Relation extraction using dependency parse trees},
author={Fundel, Katrin and K{"u}ffner, Robert and Zimmer, Ralf},
journal={Bioinformatics},
volume={23},
number={3},
pages={365--371},
year={2007},
publisher={Oxford University Press}
}
```
|
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] |
potsawee/podcast_summary_assessment
|
potsawee
| 2023-05-29T23:17:15Z | 73 | 3 |
[
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"arxiv:2208.13265",
"region:us"
] | null | 2023-03-20T14:23:36Z |
---
license: cc-by-4.0
language:
- en
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: transcript
dtype: string
- name: summary
dtype: string
- name: score
dtype: string
- name: attributes
sequence: int64
- name: episode_id
dtype: string
- name: system_id
dtype: string
splits:
- name: evaluation
num_bytes: 100261033
num_examples: 3580
download_size: 11951831
dataset_size: 100261033
---
# Podcast Summary Assessment
- The description is available in our GitHub repo: https://github.com/potsawee/podcast_summary_assessment
- Paper: [Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods](https://arxiv.org/abs/2208.13265)
### Citation Information
```
@article{manakul2022podcast,
title={Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods},
author={Manakul, Potsawee and Gales, Mark JF},
journal={arXiv preprint arXiv:2208.13265},
year={2022}
}
```
|
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] |
Abzu/dolly_hhrlhf
|
Abzu
| 2023-06-04T19:33:11Z | 73 | 3 |
[
"task_categories:question-answering",
"task_categories:text2text-generation",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"question-answering",
"text2text-generation"
] | 2023-05-25T08:34:30Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 22346337.075312525
num_examples: 35205
- name: test
num_bytes: 2483137.924687476
num_examples: 3912
download_size: 16025539
dataset_size: 24829475
license: cc-by-sa-3.0
task_categories:
- question-answering
- text2text-generation
language:
- en
---
# Dataset Card for "dolly_hhrlhf"
This is the dataset from mosaic mosaicml/dolly_hhrlhf removing some duplicates found.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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maitrang/viwiki_20230901
|
maitrang
| 2023-09-02T18:29:42Z | 73 | 0 |
[
"region:us"
] | null | 2023-09-02T18:23:29Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1237663253
num_examples: 1287269
download_size: 562888266
dataset_size: 1237663253
---
# Dataset Card for "viwiki_20230901"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
indonlp/nusatranslation_mt
|
indonlp
| 2023-11-10T03:56:13Z | 73 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-09-07T12:58:44Z |
---
license: apache-2.0
---
|
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autoevaluate/autoeval-eval-tweet_eval-sentiment-45124a-38605145054
|
autoevaluate
| 2023-10-04T14:23:31Z | 73 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2023-10-04T14:20:04Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- tweet_eval
eval_info:
task: multi_class_classification
model: siberett/roberta-sentiment-analysis-finetune
metrics: []
dataset_name: tweet_eval
dataset_config: sentiment
dataset_split: train
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: siberett/roberta-sentiment-analysis-finetune
* Dataset: tweet_eval
* Config: sentiment
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@emuggins](https://huggingface.co/emuggins) for evaluating this model.
|
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mb23/GraySpectrogram
|
mb23
| 2023-11-11T23:55:18Z | 73 | 0 |
[
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"music",
"spectrogram",
"region:us"
] | null | 2023-10-07T05:47:09Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- music
- spectrogram
size_categories:
- 10K<n<100K
---
# Google/MusicCapsをスペクトログラムにしたデータ。
* <font color="red">The dataset viwer of this repository is truncated, so maybe you should see <a href="https://huggingface.co/datasets/mb23/GraySpectrotram_example">this one</a> instaed.</font>
## Dataset information
<table>
<thead>
<td>画像</td>
<td>caption</td>
<td>data_idx</td>
<td>number</td>
</thead>
<tbody>
<tr>
<td>1025px × 216px</td>
<td>音楽の説明</td>
<td>どのデータから生成されたデータか</td>
<td>5秒ずつ区切ったデータのうち、何番目か</td>
</tr>
</tbody>
</table>
## How this dataset was made
* コード：https://colab.research.google.com/drive/13m792FEoXszj72viZuBtusYRUL1z6Cu2?usp=sharing
* 参考にしたKaggle Notebook : https://www.kaggle.com/code/osanseviero/musiccaps-explorer
```python
from PIL import Image
import IPython.display
import cv2
# 1. wavファイルを解析
y, sr = librosa.load("wavファイルなど")
# 2. フーリエ変換を適用して周波数成分を取得
D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max) # librosaを用いてデータを作る
image = Image.fromarray(np.uint8(D), mode='L') # 'L'は1チャンネルのグレースケールモードを指定します
image.save('spectrogram_{}.png')
```
## Recover music(wave form) from sprctrogram
```python
im = Image.open("pngファイル")
db_ud = np.uint8(np.array(im))
amp = librosa.db_to_amplitude(db_ud)
print(amp.shape)
# (1025, 861)は20秒のwavファイルをスペクトログラムにした場合
# (1025, 431)は10秒のwavファイルをスペクトログラムにした場合
# (1025, 216)は5秒のwavファイルをスペクトログラムにした場合
y_inv = librosa.griffinlim(amp*200)
display(IPython.display.Audio(y_inv, rate=sr))
```
## Example : How to use this
* <font color="red">Subset <b>data 1300-1600</b> and <b>data 3400-3600</b> are not working now, so please get subset_name_list</n>
those were removed first</font>.
### 1 : get information about this dataset:
* copy this code~~
```python
'''
if you use GoogleColab, remove # to install packages below..
'''
#!pip install datasets
#!pip install huggingface-hub
#!huggingface-cli login
import datasets
from datasets import load_dataset
# make subset_name_list
subset_name_list = [
'data 0-200',
'data 200-600',
'data 600-1000',
'data 1000-1300',
'data 1600-2000',
'data 2000-2200',
'data 2200-2400',
'data 2400-2600',
'data 2600-2800',
'data 3000-3200',
'data 3200-3400',
'data 3600-3800',
'data 3800-4000',
'data 4000-4200',
'data 4200-4400',
'data 4400-4600',
'data 4600-4800',
'data 4800-5000',
'data 5000-5200',
'data 5200-5520'
]
# load_all_datasets
data = load_dataset("mb23/GraySpectrogram", subset_name_list[0])
for subset in subset_name_list:
# Confirm subset_list doesn't include "remove_list" datasets in the above cell.
print(subset)
new_ds = load_dataset("mb23/GraySpectrogram", subset)
new_dataset_train = datasets.concatenate_datasets([data["train"], new_ds["train"]])
new_dataset_test = datasets.concatenate_datasets([data["test"], new_ds["test"]])
# take place of data[split]
data["train"] = new_dataset_train
data["test"] = new_dataset_test
data
```
### 2 : load dataset and change to dataloader:
* You can use the code below:
* <font color="red">...but (;・∀・)I don't know whether this code works efficiently, because I haven't tried this code so far</color>
```python
import datasets
from datasets import load_dataset, DatasetDict
from torchvision import transforms
from torch.utils.data import DataLoader
# BATCH_SIZE = ???
# IMAGE_SIZE = ???
# TRAIN_SIZE = ??? # the number of training data
# TEST_SIZE = ??? # the number of test data
def load_datasets():
# Define data transforms
data_transforms = [
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(), # Scales data into [0,1]
transforms.Lambda(lambda t: (t * 2) - 1) # Scale between [-1, 1]
]
data_transform = transforms.Compose(data_transforms)
data = load_dataset("mb23/GraySpectrogram", subset_name_list[0])
for subset in subset_name_list:
# Confirm subset_list doesn't include "remove_list" datasets in the above cell.
print(subset)
new_ds = load_dataset("mb23/GraySpectrogram", subset)
new_dataset_train = datasets.concatenate_datasets([data["train"], new_ds["train"]])
new_dataset_test = datasets.concatenate_datasets([data["test"], new_ds["test"]])
# take place of data[split]
data["train"] = new_dataset_train
data["test"] = new_dataset_test
# memo:
# 特徴量上手く抽出する方法が...わからん。これは力づく。
# 本当はload_dataset()の時点で抽出したかったけど、無理そう
# リポジトリ作り直してpush_to_hub()したほうがいいかもしれない。
new_dataset = dict()
new_dataset["train"] = Dataset.from_dict({
"image" : data["train"]["image"],
"caption" : data["train"]["caption"]
})
new_dataset["test"] = Dataset.from_dict({
"image" : data["test"]["image"],
"caption" : data["test"]["caption"]
})
data = datasets.DatasetDict(new_dataset)
train = data["train"]
test = data["test"]
for idx in range(len(train["image"])):
train["image"][idx] = data_transform(train["image"][idx])
test["image"][idx] = data_transform(test["image"][idx])
train = Dataset.from_dict(train)
train = train.with_format("torch") # リスト型回避
test = Dataset.from_dict(train)
test = test.with_format("torch") # リスト型回避
# or
train_loader = DataLoader(train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
test_loader = DataLoader(test, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
return train_loader, test_loader
```
* then try this?
```
train_loader, test_loader = load_datasets()
```
|
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hippocrates/PubmedQA_train
|
hippocrates
| 2023-11-30T16:12:42Z | 73 | 0 |
[
"region:us"
] | null | 2023-10-12T16:17:31Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 701570152
num_examples: 211269
- name: valid
num_bytes: 159299
num_examples: 50
- name: test
num_bytes: 1622241
num_examples: 500
download_size: 359787344
dataset_size: 703351692
---
# Dataset Card for "PubmedQA_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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xz97/MedInstruct
|
xz97
| 2023-10-24T01:27:45Z | 73 | 5 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2310.14558",
"region:us"
] |
[
"text-generation"
] | 2023-10-19T18:45:58Z |
---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: MedInstruct
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
# Dataset Card for MedInstruct
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/XZhang97666/AlpaCare
- **Paper:**
- **Point of Contact:** [Xinlu Zhang](xinluzhang@ucsb,edu)
### Dataset Summary
**MedInstruct** encompasses:
1. **MedInstruct-52k**: A dataset comprising 52,000 medical instructions and responses. Instructions are crafted by OpenAI's GPT-4 engine, and the responses are formulated by the GPT-3.5-turbo engine.
2. **MedInstruct-test**: A set of 217 clinical craft free-form instruction evaluation tests.
3. **med_seed**: The clinician-crafted seed set as a denomination to prompt GPT-4 for task generation.
**MedInstruct-52k** can be used to conduct instruction-tuning of language models, aiming to enhance their instruction-following capability and medical proficiency. \
On the other hand, **MedInstruct-test** can sever as an evaluator of models' instruction-following ability in the medical area.
Specifically, the **MedInstruct-52k** dataset was obtained by:
*Instructions*:
- Beginning with a medical task seed set that contains 167 clinical-craft clinical tasks.
- Utilizing the GPT-4 engine for instruction data generation.
- Crafting a [prompt](https://github.com/XZhang97666/AlpaCare/blob/master/prompt/instruction_prompt.txt) to serve as the medical instruction generation guidelines for GPT-4.
- To reduce costs in task generation and introduce greater randomness, 12 instructions are generated at once. This is followed by the selection of 3 random seed instructions from the seed set.
*Responses*:
- The GPT-3.5-turbo engine is used to synthesize responses, processing each instruction individually.
Here's an illustration of an instruction-response pair:
```json
{
instruction: "Rewrite this instruction on how to use an inhaler to be more patient-friendly and less technical."
input: "Exhale deeply, engage mouthpiece of inhaler, depress canister while simultaneously inhaling long and deep, hold the breath for ten seconds, then gently exhale"
output: "Here's a more patient-friendly version of the instruction on how to use an inhaler:
1. Take a deep breath and completely breathe out.
2. Put the mouthpiece of the inhaler into your mouth and seal your lips around it properly.
3. Press down the canister while breathing in slowly and deeply at the same time.
4. Hold your breath for ten seconds. 5. Finally, breathe out gently."
}
```
## Citation:
If you think it is a useful repo, please cite the paper:
```
@misc{zhang2023alpacareinstructiontuned,
title={AlpaCare:Instruction-tuned Large Language Models for Medical Application},
author={Xinlu Zhang and Chenxin Tian and Xianjun Yang and Lichang Chen and Zekun Li and Linda Ruth Petzold},
year={2023},
eprint={2310.14558},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
PedroSampaio/fruits-360
|
PedroSampaio
| 2023-10-30T00:00:35Z | 73 | 0 |
[
"language:en",
"license:mit",
"region:us"
] | null | 2023-10-29T16:02:19Z |
---
language:
- en
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Apple Braeburn
'1': Apple Crimson Snow
'2': Apple Golden
'3': Apple Granny Smith
'4': Apple Pink Lady
'5': Apple Red
'6': Apple Red Delicious
'7': Apple Red Yellow
'8': Apricot
'9': Avocado
'10': Avocado ripe
'11': Banana
'12': Banana Lady Finger
'13': Banana Red
'14': Beetroot
'15': Blueberry
'16': Cactus fruit
'17': Cantaloupe
'18': Carambula
'19': Cauliflower
'20': Cherry
'21': Cherry Rainier
'22': Cherry Wax Black
'23': Cherry Wax Red
'24': Cherry Wax Yellow
'25': Chestnut
'26': Clementine
'27': Cocos
'28': Corn
'29': Corn Husk
'30': Cucumber Ripe
'31': Dates
'32': Eggplant
'33': Fig
'34': Ginger Root
'35': Granadilla
'36': Grape Blue
'37': Grape Pink
'38': Grape White
'39': Grapefruit Pink
'40': Grapefruit White
'41': Guava
'42': Hazelnut
'43': Huckleberry
'44': Kaki
'45': Kiwi
'46': Kohlrabi
'47': Kumquats
'48': Lemon
'49': Lemon Meyer
'50': Limes
'51': Lychee
'52': Mandarine
'53': Mango
'54': Mango Red
'55': Mangostan
'56': Maracuja
'57': Melon Piel de Sapo
'58': Mulberry
'59': Nectarine
'60': Nectarine Flat
'61': Nut Forest
'62': Nut Pecan
'63': Onion Red
'64': Onion Red Peeled
'65': Onion White
'66': Orange
'67': Papaya
'68': Passion Fruit
'69': Peach
'70': Peach Flat
'71': Pear
'72': Pear Abate
'73': Pear Forelle
'74': Pear Kaiser
'75': Pear Monster
'76': Pear Red
'77': Pear Stone
'78': Pear Williams
'79': Pepino
'80': Pepper Green
'81': Pepper Orange
'82': Pepper Red
'83': Pepper Yellow
'84': Physalis
'85': Physalis with Husk
'86': Pineapple
'87': Pineapple Mini
'88': Pitahaya Red
'89': Plum
'90': Pomegranate
'91': Pomelo Sweetie
'92': Potato Red
'93': Potato Red Washed
'94': Potato Sweet
'95': Potato White
'96': Quince
'97': Rambutan
'98': Raspberry
'99': Redcurrant
'100': Salak
'101': Strawberry
'102': Strawberry Wedge
'103': Tamarillo
'104': Tangelo
'105': Tomato
'106': Tomato Cherry Red
'107': Tomato Heart
'108': Tomato Maroon
'109': Tomato Yellow
'110': Tomato not Ripened
'111': Walnut
'112': Watermelon
splits:
- name: train
num_bytes: 329958484.13
num_examples: 67690
- name: test
num_bytes: 108519035.632
num_examples: 22688
download_size: 98330799
dataset_size: 438477519.76199996
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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Santp98/model_validation_ranked_ds
|
Santp98
| 2023-11-04T02:01:38Z | 73 | 0 |
[
"region:us"
] | null | 2023-11-04T02:01:37Z |
---
dataset_info:
features:
- name: rank_1
dtype: string
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dtype: string
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splits:
- name: train
num_bytes: 820598
num_examples: 500
download_size: 308559
dataset_size: 820598
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "model_validation_ranked_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
wetdog/parlament_parla_ecapa_emb
|
wetdog
| 2023-11-20T11:39:03Z | 73 | 0 |
[
"region:us"
] | null | 2023-11-15T13:16:16Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: path
dtype: string
- name: speaker_id
dtype: int64
- name: sentence
dtype: string
- name: gender
dtype:
class_label:
names:
'0': F
'1': M
- name: duration
dtype: float64
- name: embeddings
sequence: float64
splits:
- name: train
num_bytes: 140554656
num_examples: 78976
- name: validation
num_bytes: 3802467
num_examples: 2150
- name: test
num_bytes: 3783863
num_examples: 2138
download_size: 133275777
dataset_size: 148140986
---
# Dataset Card for "parlament_parla_ecapa_emb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/qm_bob_grader_last_1.0e
|
atmallen
| 2023-11-16T18:22:54Z | 73 | 0 |
[
"region:us"
] | null | 2023-11-16T03:26:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 14970044.0
num_examples: 200000
- name: validation
num_bytes: 1501418.0
num_examples: 20000
- name: test
num_bytes: 1502170.0
num_examples: 20000
download_size: 0
dataset_size: 17973632.0
---
# Dataset Card for "qm_bob__grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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sam1120/safety-utcustom-train-v1.0
|
sam1120
| 2023-12-01T08:39:29Z | 73 | 0 |
[
"region:us"
] | null | 2023-12-01T08:33:46Z |
---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 142272068.0
num_examples: 50
download_size: 43505313
dataset_size: 142272068.0
---
# Dataset Card for "safety-utcustom-train-v1.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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webis/args_me
|
webis
| 2022-09-21T12:09:09Z | 72 | 2 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-retrieval"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Webis args.me argument corpus
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card for the args.me corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Usage](#dataset-usage)
- [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://zenodo.org/record/4139439
- **Repository:** https://git.webis.de/code-research/arguana/args/args-framework
- **Paper:** [Building an Argument Search Engine for the Web](https://webis.de/downloads/publications/papers/wachsmuth_2017f.pdf)
- **Leaderboard:** https://touche.webis.de/
- **Point of Contact:** [Webis Group](https://webis.de/people.html)
### Dataset Summary
The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
### Dataset Usage
```python
import datasets
args = datasets.load_dataset('webis/args_me', 'corpus', streaming=True)
args_iterator = iter(args)
for arg in args_iterator:
print(args['conclusion'])
print(args['id'])
print(args['argument'])
print(args['stance'])
break
```
### Supported Tasks and Leaderboards
Document Retrieval, Argument Retrieval for Controversial Questions
### Languages
The args.me corpus is monolingual; it only includes English (mostly en-US) documents.
## Dataset Structure
### Data Instances
#### Corpus
```
{'conclusion': 'Science is the best!',
'id': 'd6517702-2019-04-18T12:36:24Z-00000-000',
'argument': 'Science is aright I guess, but Physical Education (P.E) is better. Think about it, you could sit in a classroom for and hour learning about molecular reconfiguration, or you could play football with your mates. Why would you want to learn about molecular reconfiguration anyway? I think the argument here would be based on, healthy mind or healthy body. With science being the healthy mind and P.E being the healthy body. To work this one out all you got to do is ask Steven Hawkins. Only 500 words',
'stance': 'CON'}
```
### 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
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@dataset{yamen_ajjour_2020_4139439,
author = {Yamen Ajjour and
Henning Wachsmuth and
Johannes Kiesel and
Martin Potthast and
Matthias Hagen and
Benno Stein},
title = {args.me corpus},
month = oct,
year = 2020,
publisher = {Zenodo},
version = {1.0-cleaned},
doi = {10.5281/zenodo.4139439},
url = {https://doi.org/10.5281/zenodo.4139439}
}
```
|
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] |
bigbio/bionlp_st_2011_epi
|
bigbio
| 2022-12-22T15:43:49Z | 72 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:06:49Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2011 EPI
homepage: https://github.com/openbiocorpora/bionlp-st-2011-epi
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2011 EPI
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2011-epi
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
The dataset of the Epigenetics and Post-translational Modifications (EPI) task
of BioNLP Shared Task 2011.
## Citation Information
```
@inproceedings{ohta-etal-2011-overview,
title = "Overview of the Epigenetics and Post-translational
Modifications ({EPI}) task of {B}io{NLP} Shared Task 2011",
author = "Ohta, Tomoko and
Pyysalo, Sampo and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of {B}io{NLP} Shared Task 2011 Workshop",
month = jun,
year = "2011",
address = "Portland, Oregon, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W11-1803",
pages = "16--25",
}
```
|
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bigbio/codiesp
|
bigbio
| 2022-12-22T15:44:28Z | 72 | 0 |
[
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-13T22:08:01Z |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: CodiEsp
homepage: https://temu.bsc.es/codiesp/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for CodiEsp
## Dataset Description
- **Homepage:** https://temu.bsc.es/codiesp/
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS,NER,NED
Synthetic corpus of 1,000 manually selected clinical case studies in Spanish
that was designed for the Clinical Case Coding in Spanish Shared Task, as part
of the CLEF 2020 conference.
The goal of the task was to automatically assign ICD10 codes (CIE-10, in
Spanish) to clinical case documents, being evaluated against manually generated
ICD10 codifications. The CodiEsp corpus was selected manually by practicing
physicians and clinical documentalists and annotated by clinical coding
professionals meeting strict quality criteria. They reached an inter-annotator
agreement of 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5%
for the textual reference annotation.
The final collection of 1,000 clinical cases that make up the corpus had a total
of 16,504 sentences and 396,988 words. All documents are in Spanish language and
CIE10 is the coding terminology (the Spanish version of ICD10-CM and ICD10-PCS).
The CodiEsp corpus has been randomly sampled into three subsets. The train set
contains 500 clinical cases, while the development and test sets have 250
clinical cases each. In addition to these, a collection of 176,294 abstracts
from Lilacs and Ibecs with the corresponding ICD10 codes (ICD10-CM and
ICD10-PCS) was provided by the task organizers. Every abstract has at least one
associated code, with an average of 2.5 ICD10 codes per abstract.
The CodiEsp track was divided into three sub-tracks (2 main and 1 exploratory):
- CodiEsp-D: The Diagnosis Coding sub-task, which requires automatic ICD10-CM
[CIE10-Diagnóstico] code assignment.
- CodiEsp-P: The Procedure Coding sub-task, which requires automatic ICD10-PCS
[CIE10-Procedimiento] code assignment.
- CodiEsp-X: The Explainable AI exploratory sub-task, which requires to submit
the reference to the predicted codes (both ICD10-CM and ICD10-PCS). The goal
of this novel task was not only to predict the correct codes but also to
present the reference in the text that supports the code predictions.
For further information, please visit https://temu.bsc.es/codiesp or send an
email to [email protected]
## Citation Information
```
@article{miranda2020overview,
title={Overview of Automatic Clinical Coding: Annotations, Guidelines, and Solutions for non-English Clinical Cases at CodiEsp Track of CLEF eHealth 2020.},
author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{'e}, Jordi and Krallinger, Martin},
journal={CLEF (Working Notes)},
volume={2020},
year={2020}
}
```
|
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lucadiliello/english_wikipedia
|
lucadiliello
| 2022-12-04T19:05:23Z | 72 | 1 |
[
"region:us"
] | null | 2022-12-04T18:59:49Z |
---
dataset_info:
features:
- name: filename
dtype: string
- name: maintext
dtype: string
- name: source_domain
dtype: string
- name: title
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 10569005563
num_examples: 4184712
download_size: 6144953788
dataset_size: 10569005563
---
# Dataset Card for "english_wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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suolyer/pile_openwebtext2
|
suolyer
| 2023-03-27T03:03:15Z | 72 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-26T16:38:21Z |
---
license: apache-2.0
---
|
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] |
patomp/thai-mscoco-2014-captions
|
patomp
| 2023-05-02T15:52:54Z | 72 | 0 |
[
"region:us"
] | null | 2023-04-25T10:38:36Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: filepath
dtype: string
- name: sentids
list: int32
- name: filename
dtype: string
- name: imgid
dtype: int32
- name: split
dtype: string
- name: sentences_tokens
list:
list: string
- name: sentences_raw
list: string
- name: sentences_sentid
list: int32
- name: cocoid
dtype: int32
- name: th_sentences_raw
sequence: string
splits:
- name: test
num_bytes: 819234726.0
num_examples: 5000
- name: validation
num_bytes: 807387321.0
num_examples: 5000
- name: train
num_bytes: 18882795327.165
num_examples: 113287
download_size: 20158273111
dataset_size: 20509417374.165
---
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("patomp/thai-mscoco-2014-captions")
dataset
```
output
```python
DatasetDict({
train: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 113287
})
validation: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 5000
})
test: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 5000
})
})
```
A sample
```python
dataset["validation"][0]
```
output
```python
{
"image":<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x7F6C5A83F430>,
"filepath":"COCO_val2014_000000184613.jpg",
"sentids":[474921,479322,479334,481560,483594],
"filename":"COCO_val2014_000000184613.jpg",
"imgid":2,
"split":"val",
"sentences_tokens":[
["a", "child","holding", "a","flowered","umbrella","and","petting","a","yak"],["a","young","man","holding","an","umbrella","next","to","a","herd","of","cattle"],
["a","young","boy","barefoot","holding","an","umbrella","touching","the","horn","of","a","cow"],
["a","young","boy","with","an","umbrella","who","is","touching","the","horn","of","a","cow"],
["a","boy","holding","an","umbrella","while","standing","next","to","livestock"]
],
"sentences_raw":[
"A child holding a flowered umbrella and petting a yak.",
"A young man holding an umbrella next to a herd of cattle.",
"a young boy barefoot holding an umbrella touching the horn of a cow",
"A young boy with an umbrella who is touching the horn of a cow.",
"A boy holding an umbrella while standing next to livestock."
],
"sentences_sentid":[474921,479322,479334,481560,483594],
"cocoid":184613,
"th_sentences_raw":[
"เด็กถือร่มที่มีดอกหนึ่งคันและลูบคลูบลํา",
"ชายหนุ่มคนหนึ่งถือร่มไว้ข้างๆ ฝูงวัว",
"เด็กหนุ่มคนหนึ่งเท้าเปล่าจับร่มจับแตรของวัว",
"เด็กชายที่มีร่มสัมผัสแตรของวัว",
"เด็กชายถือร่มในขณะที่ยืนถัดจากปศุสัตว์"
]
}
```
## Dataset Construction
The dataset contructed from translating the captions of [MS COCO 2014 dataset](https://huggingface.co/datasets/HuggingFaceM4/COCO) [1] to Thai by using [NMT](https://airesearch.in.th/releases/machine-translation-models/) provided by VISTEC-depa Thailand Artificial Intelligence Research Institute [2]. The translated of 3 splits (train, validation and test) dataset was published in the [Huggingface](https://huggingface.co/datasets/patomp/thai-mscoco-2014-captions).
## References
[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision – ECCV 2014, Springer International Publishing, Cham, 740–755.
[2] English-Thai Machine Translation Models. (2020, June 23). VISTEC-depa Thailand Artificial Intelligence Research Institute. https://airesearch.in.th/releases/machine-translation-models/
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gretelai/symptom_to_diagnosis
|
gretelai
| 2023-05-24T17:58:04Z | 72 | 4 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] |
[
"text-classification"
] | 2023-05-23T22:48:27Z |
---
license: apache-2.0
task_categories:
- text-classification
task_ids:
- multi-class-classification
language:
- en
tags:
- medical
pretty_name: Gretel/symptoms_to_diagnosis
size_categories:
- 10K<n<100K
---
# Dataset Summary
This dataset contains natural language descriptions of symptoms labeled with 22 corresponding diagnoses. `Gretel/symptom_to_diagnosis` provides 1065 symptom descriptions in the English language labeled with 22 diagnoses, focusing on fine-grained single-domain diagnosis.
## Data Fields
Each row contains the following fields:
* `input_text` : A string field containing symptoms
* `output_text` : A string field containing a diagnosis
Example:
```
{
"output_text": "drug reaction",
"input_text": "I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded."
}
```
## Diagnoses
This table contains the count of each diagnosis in the train and test splits.
| | Diagnosis | train.jsonl | test.jsonl |
|---:|:--------------------------------|--------------:|-------------:|
| 0 | drug reaction | 40 | 8 |
| 1 | allergy | 40 | 10 |
| 2 | chicken pox | 40 | 10 |
| 3 | diabetes | 40 | 10 |
| 4 | psoriasis | 40 | 10 |
| 5 | hypertension | 40 | 10 |
| 6 | cervical spondylosis | 40 | 10 |
| 7 | bronchial asthma | 40 | 10 |
| 8 | varicose veins | 40 | 10 |
| 9 | malaria | 40 | 10 |
| 10 | dengue | 40 | 10 |
| 11 | arthritis | 40 | 10 |
| 12 | impetigo | 40 | 10 |
| 13 | fungal infection | 39 | 9 |
| 14 | common cold | 39 | 10 |
| 15 | gastroesophageal reflux disease | 39 | 10 |
| 16 | urinary tract infection | 39 | 9 |
| 17 | typhoid | 38 | 9 |
| 18 | pneumonia | 37 | 10 |
| 19 | peptic ulcer disease | 37 | 10 |
| 20 | jaundice | 33 | 7 |
| 21 | migraine | 32 | 10 |
## Data Splits
The data is split to 80% train (853 examples, 167kb) and 20% test (212 examples, 42kb).
## Dataset Creation
Data was filtered to remove unwanted categories and updated using an LLM to create language more consistent with how a patient would describe symptoms in natural language to a doctor.
## Source Data
This dataset was adapted based on the [Symptom2Disease](https://www.kaggle.com/datasets/niyarrbarman/symptom2disease) dataset from Kaggle.
## Personal and Sensitive Information
The symptoms in this dataset were modified from their original format using an LLM and do not contain personal data.
## Limitations
This dataset is licensed Apache 2.0 and free for use.
|
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] |
zachary-shah/musdb18-spec-pix2pix
|
zachary-shah
| 2023-06-06T02:55:48Z | 72 | 0 |
[
"region:us"
] | null | 2023-06-06T02:54:43Z |
---
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 2923510938.704
num_examples: 31556
download_size: 2839469846
dataset_size: 2923510938.704
---
# Dataset Card for "musdb18-spec-pix2pix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
clarin-knext/quora-pl
|
clarin-knext
| 2023-06-07T08:16:00Z | 72 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:16:05Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
Babelscape/REDFM
|
Babelscape
| 2023-06-20T07:33:35Z | 72 | 4 |
[
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:ar",
"language:de",
"language:en",
"language:es",
"language:it",
"language:fr",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2306.09802",
"region:us"
] |
[
"token-classification"
] | 2023-06-13T16:46:41Z |
---
dataset_info:
- config_name: ar
features:
- name: docid
dtype: string
- name: title
dtype: string
- name: uri
dtype: string
- name: text
dtype: string
- name: entities
list:
- name: uri
dtype: string
- name: surfaceform
dtype: string
- name: type
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: relations
list:
- name: subject
struct:
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dtype: string
- name: surfaceform
dtype: string
- name: type
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
- name: predicate
dtype:
class_label:
names:
'0': country
'1': place of birth
'2': spouse
'3': country of citizenship
'4': instance of
'5': capital
'6': child
'7': shares border with
'8': author
'9': director
'10': occupation
'11': founded by
'12': league
'13': owned by
'14': genre
'15': named after
'16': follows
'17': headquarters location
'18': cast member
'19': manufacturer
'20': located in or next to body of water
'21': location
'22': part of
'23': mouth of the watercourse
'24': member of
'25': sport
'26': characters
'27': participant
'28': notable work
'29': replaces
'30': sibling
'31': inception
- name: object
struct:
- name: uri
dtype: string
- name: surfaceform
dtype: string
- name: type
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
splits:
- name: test
num_bytes: 521806
num_examples: 345
- name: validation
num_bytes: 577499
num_examples: 385
download_size: 3458539
dataset_size: 1099305
- config_name: de
features:
- name: docid
dtype: string
- name: title
dtype: string
- name: uri
dtype: string
- name: text
dtype: string
- name: entities
list:
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dtype: string
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dtype: string
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dtype: string
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task_categories:
- token-classification
language:
- ar
- de
- en
- es
- it
- fr
- zh
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset
This is the human-filtered dataset from the 2023 ACL paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). If you use the model, please reference this work in your paper:
@inproceedings{huguet-cabot-et-al-2023-redfm-dataset,
title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset",
author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and
Navigli, Roberto",
booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2306.09802",
}
## License
RED<sup>FM</sup> is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-sa/4.0/).
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npvinHnivqn/VietnameseDictionary
|
npvinHnivqn
| 2023-07-08T09:13:42Z | 72 | 0 |
[
"size_categories:20K<n<40K",
"language:vi",
"region:us"
] | null | 2023-07-04T13:50:48Z |
---
language:
- vi
size_categories:
- 20K<n<40K
---
- This dataset includes ~30k Vietnamese words and definitions
|
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] |
PetraAI/PetraAI
|
PetraAI
| 2023-09-14T21:04:52Z | 72 | 5 |
[
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"task_categories:sentence-similarity",
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"task_categories:audio-to-audio",
"task_categories:audio-classification",
"task_categories:voice-activity-detection",
"task_categories:depth-estimation",
"task_categories:image-classification",
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-to-image",
"task_categories:unconditional-image-generation",
"task_categories:video-classification",
"task_categories:reinforcement-learning",
"task_categories:robotics",
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"task_categories:tabular-to-text",
"task_categories:table-to-text",
"task_categories:multiple-choice",
"task_categories:text-retrieval",
"task_categories:time-series-forecasting",
"task_categories:text-to-video",
"task_categories:visual-question-answering",
"task_categories:zero-shot-image-classification",
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"language:ar",
"language:en",
"license:apache-2.0",
"chemistry",
"biology",
"finance",
"legal",
"music",
"art",
"code",
"climate",
"medical",
"doi:10.57967/hf/0945",
"region:us"
] |
[
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"translation",
"summarization",
"conversational",
"feature-extraction",
"text-generation",
"text2text-generation",
"fill-mask",
"sentence-similarity",
"text-to-speech",
"automatic-speech-recognition",
"audio-to-audio",
"audio-classification",
"voice-activity-detection",
"depth-estimation",
"image-classification",
"object-detection",
"image-segmentation",
"text-to-image",
"image-to-text",
"image-to-image",
"unconditional-image-generation",
"video-classification",
"reinforcement-learning",
"robotics",
"tabular-classification",
"tabular-regression",
"tabular-to-text",
"table-to-text",
"multiple-choice",
"text-retrieval",
"time-series-forecasting",
"text-to-video",
"visual-question-answering",
"zero-shot-image-classification",
"graph-ml"
] | 2023-08-01T01:34:38Z |
---
license: apache-2.0
task_categories:
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- translation
- summarization
- conversational
- feature-extraction
- text-generation
- text2text-generation
- fill-mask
- sentence-similarity
- text-to-speech
- automatic-speech-recognition
- audio-to-audio
- audio-classification
- voice-activity-detection
- depth-estimation
- image-classification
- object-detection
- image-segmentation
- text-to-image
- image-to-text
- image-to-image
- unconditional-image-generation
- video-classification
- reinforcement-learning
- robotics
- tabular-classification
- tabular-regression
- tabular-to-text
- table-to-text
- multiple-choice
- text-retrieval
- time-series-forecasting
- text-to-video
- visual-question-answering
- zero-shot-image-classification
- graph-ml
language:
- ar
- en
tags:
- chemistry
- biology
- finance
- legal
- music
- art
- code
- climate
- medical
pretty_name: PETRA
size_categories:
- 1M<n<10M
---
# PETRA
## Overview
PETRA is a multilingual dataset for training and evaluating AI systems on a diverse range of tasks across multiple modalities. It contains data in Arabic and English for tasks including translation, summarization, question answering, and more.
## Dataset Structure
- Data is separated by language into `/ar` and `/en` directories
- Within each language directory, data is separated by task into subdirectories
- Tasks include:
- Translation
- Summarization
- Conversational
- Feature extraction
- Zero-shot classification
- Text generation
- Fill mask
- Sentence similarity
- Text-to-speech
- Automatic speech recognition
- Text classification
- Token classification
- Table question answering
- Question answering
- Text2text generation
- Audio-to-audio
- Audio classification
- Voice activity detection
- Depth estimation
- Image classification
- Object detection
- Image segmentation
- Text-to-image
- Image-to-text
- Image-to-image
- Unconditional image generation
- Reinforcement learning
- Video classification
- Robotics
- Tabular classification
- Tabular regression
- Table-to-text
- Multiple choice
- Text retrieval
- Tabular-to-text
- Text-to-video
- Time series forecasting
- Visual question answering
- Zero-shot image classification
- Graph ML
## Dataset Tags
- code
- art
- chemistry
- biology
- finance
- legal
- music
- climate
- medical
## Dataset Size
1M < n < 10M samples
## Licenses
Apache 2.0
## Citation
If you use this dataset, please cite it as:
[cite paper, arXiv, etc]
@article{PetraAI2022PetraAI,
title={PetraAI: A Massive Multilingual Dataset for Machine Learning},
author={First Last and First Last},
journal={arXiv},
year={2022},
url={https://huggingface.co/datasets/PetraAI/PetraAI}
}
## Contact
For any questions, please reach out to [[email protected]]
# Dataset Cards
## What are Dataset Cards?
Each dataset may be documented by the `README.md` file in the repository. This file is called a **dataset card**, and the Hugging Face Hub will render its contents on the dataset’s main page. To inform users about how to responsibly use the data, it’s a good idea to include information about any potential biases within the dataset. Generally, dataset cards help users understand the contents of the dataset and give context for how the dataset should be used.
You can also add dataset metadata to your card. The metadata describes important information about a dataset such as its license, language, and size. It also contains tags to help users discover a dataset on the Hub. Tags are defined in a YAML metadata section at the top of the `README.md` file.
## Dataset card metadata
A dataset repo will render its README.md as a dataset card. To control how the Hub displays the card, you should create a YAML section in the README file to define some metadata. Start by adding three --- at the top, then include all of the relevant metadata, and close the section with another group of --- like the example below:
The metadata that you add to the dataset card enables certain interactions on the Hub. For example:
- Allow users to filter and discover datasets at https://huggingface.co/datasets.
- If you choose a license using the keywords listed in the right column of this table, the license will be displayed on the dataset page.
When creating a README.md file in a dataset repository on the Hub, use Metadata UI to fill the main metadata:
To see metadata fields, see the detailed dataset card metadata specification here.
### Dataset card creation guide
For a step-by-step guide on creating a dataset card, check out the Create a dataset card guide.
Reading through existing dataset cards, such as the ELI5 dataset card, is a great way to familiarize yourself with the common conventions.
### Linking a Paper
If the dataset card includes a link to a paper on arXiv, the Hub will extract the arXiv ID and include it in the dataset tags with the format `arxiv:<PAPER ID>`. Clicking on the tag will let you:
- Visit the Paper page
- Filter for other models on the Hub that cite the same paper.
Read more about paper pages here.
https://huggingface.co/docs/hub/paper-pages
|
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] |
jxie/esol
|
jxie
| 2023-08-04T22:25:16Z | 72 | 0 |
[
"region:us"
] | null | 2023-08-04T22:25:04Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: float64
splits:
- name: train_0
num_bytes: 31089
num_examples: 902
- name: val_0
num_bytes: 3828
num_examples: 113
- name: test_0
num_bytes: 4016
num_examples: 113
- name: train_1
num_bytes: 31354
num_examples: 902
- name: val_1
num_bytes: 3731
num_examples: 113
- name: test_1
num_bytes: 3848
num_examples: 113
- name: train_2
num_bytes: 31095
num_examples: 902
- name: val_2
num_bytes: 3869
num_examples: 113
- name: test_2
num_bytes: 3969
num_examples: 113
download_size: 75468
dataset_size: 116799
---
# Dataset Card for "esol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vlsp-2023-vllm/hhh_alignment
|
vlsp-2023-vllm
| 2023-10-30T03:32:46Z | 72 | 0 |
[
"region:us"
] | null | 2023-09-15T17:17:32Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int32
- name: metadata
struct:
- name: subset
dtype: string
splits:
- name: test
num_bytes: 285938
num_examples: 221
download_size: 66013
dataset_size: 285938
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# HHH-alignment
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `hellaswag_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks hhh_alignment_vi \
--batch_size auto \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks hhh_alignment_vi \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring.
|
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] |
hippocrates/CochranePLS
|
hippocrates
| 2023-09-20T16:38:53Z | 72 | 0 |
[
"region:us"
] | null | 2023-09-20T16:38:50Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6321
num_examples: 1
- name: test
num_bytes: 1279406
num_examples: 200
download_size: 669610
dataset_size: 1285727
---
# Dataset Card for "CochranePLS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
rmanluo/RoG-cwq
|
rmanluo
| 2023-10-01T23:47:36Z | 72 | 1 |
[
"region:us"
] | null | 2023-10-01T23:29:54Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
sequence: string
- name: q_entity
sequence: string
- name: a_entity
sequence: string
- name: graph
sequence:
sequence: string
- name: choices
sequence: 'null'
splits:
- name: train
num_bytes: 8890766478
num_examples: 27639
- name: validation
num_bytes: 1170336525
num_examples: 3519
- name: test
num_bytes: 1208452620
num_examples: 3531
download_size: 1993772283
dataset_size: 11269555623
---
# Dataset Card for "RoG-cwq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
FinGPT/fingpt-finred
|
FinGPT
| 2023-10-10T06:58:37Z | 72 | 2 |
[
"region:us"
] | null | 2023-10-10T06:56:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 25113554
num_examples: 27558
- name: test
num_bytes: 4477146
num_examples: 5112
download_size: 2114835
dataset_size: 29590700
---
# Dataset Card for "fingpt-finred"
This dataset consist of both Relation Extraction part and Classification part, and it used in Multi-task Instruction Tuning
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
phatjk/viquad
|
phatjk
| 2023-10-14T07:30:19Z | 72 | 0 |
[
"region:us"
] | null | 2023-10-14T07:30:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: ans_start
dtype: int64
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 24944836
num_examples: 19240
- name: test
num_bytes: 5091238
num_examples: 4065
download_size: 4943526
dataset_size: 30036074
---
# Dataset Card for "viquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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phosseini/multimodal_satire
|
phosseini
| 2023-10-19T23:22:11Z | 72 | 0 |
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"image-classification"
] | 2023-10-19T23:05:51Z |
---
dataset_info:
features:
- name: url
dtype: string
- name: headline
dtype: string
- name: image_link
dtype: string
- name: is_satire
dtype: int32
splits:
- name: train
num_bytes: 2841764
num_examples: 10000
download_size: 1268537
dataset_size: 2841764
task_categories:
- image-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset card for "multimodal_satire"
This is the dataset for the paper [A Multi-Modal Method for Satire Detection using Textual and Visual Cues](https://aclanthology.org/2020.nlp4if-1.4/). To obtain the full-text body of the articles, you need to scrape websites using the provided links in the dataset.
* GitHub repository: [https://github.com/lilyli2004/satire](https://github.com/lilyli2004/satire)
## Reference
If you use this dataset, please cite the following paper:
```
@inproceedings{li-etal-2020-multi-modal,
title = "A Multi-Modal Method for Satire Detection using Textual and Visual Cues",
author = "Li, Lily and
Levi, Or and
Hosseini, Pedram and
Broniatowski, David",
booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics (ICCL)",
url = "https://aclanthology.org/2020.nlp4if-1.4",
pages = "33--38",
abstract = "Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines.",
}
```
|
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satellite-image-deep-learning/SODA-A
|
satellite-image-deep-learning
| 2023-10-22T05:19:07Z | 72 | 0 |
[
"license:mit",
"remote-sensing",
"oriented-bounding-boxes",
"object-detection",
"region:us"
] | null | 2023-10-22T03:38:59Z |
---
license: mit
tags:
- remote-sensing
- oriented-bounding-boxes
- object-detection
---
SODA-A comprises 2513 high-resolution images of aerial scenes, which has 872069 instances annotated with oriented rectangle box annotations over 9 classes.
- [Website](https://shaunyuan22.github.io/SODA/)
|
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] |
jxm/nq_corpus_dpr
|
jxm
| 2023-11-07T22:15:32Z | 72 | 0 |
[
"region:us"
] | null | 2023-11-07T22:13:52Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3284289693
num_examples: 5332023
- name: dev
num_bytes: 520583613
num_examples: 849508
download_size: 2568992962
dataset_size: 3804873306
---
# Dataset Card for "nq_corpus_dpr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
joseluhf11/oct-object-detection-v2-merge
|
joseluhf11
| 2023-11-22T08:42:10Z | 72 | 0 |
[
"region:us"
] | null | 2023-11-15T15:13:12Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: string
splits:
- name: train
num_bytes: 153967507.25
num_examples: 1246
download_size: 71637288
dataset_size: 153967507.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oct-object-detection-v2-merge"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
Changes from from v1 are images are grouped into a single row for the same class detection object, and also join with merge method overlapping boxes. merge means, get the whole area covered by both boxes.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
|
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] |
medmabfc/Arabic_News_Texts_Corpus
|
medmabfc
| 2023-11-23T22:22:35Z | 72 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-22T20:38:26Z |
---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 655293
num_examples: 154
download_size: 309603
dataset_size: 655293
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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BubbleJoe/bootstrap_sms
|
BubbleJoe
| 2023-11-28T20:05:51Z | 72 | 0 |
[
"region:us"
] | null | 2023-11-28T20:05:50Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 473170
num_examples: 1325
download_size: 106042
dataset_size: 473170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bootstrap_sms"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jahb57/glue_subset_1_sentences
|
jahb57
| 2023-12-03T11:13:22Z | 72 | 0 |
[
"region:us"
] | null | 2023-12-03T11:13:09Z |
---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 111335869
num_examples: 1444677
download_size: 81465370
dataset_size: 111335869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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mozilla-foundation/common_voice_4_0
|
mozilla-foundation
| 2023-07-29T16:00:01Z | 71 | 1 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- n<1K
ar:
- 10K<n<100K
br:
- 10K<n<100K
ca:
- 100K<n<1M
cnh:
- 1K<n<10K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
en:
- 1M<n<10M
eo:
- 10K<n<100K
es:
- 100K<n<1M
et:
- 1K<n<10K
eu:
- 10K<n<100K
fa:
- 100K<n<1M
fr:
- 100K<n<1M
ga-IE:
- 1K<n<10K
ia:
- 1K<n<10K
id:
- 1K<n<10K
it:
- 10K<n<100K
ja:
- 1K<n<10K
kab:
- 100K<n<1M
ky:
- 10K<n<100K
lv:
- 1K<n<10K
mn:
- 1K<n<10K
nl:
- 10K<n<100K
pt:
- 10K<n<100K
rm-sursilv:
- n<1K
ru:
- 10K<n<100K
rw:
- 10K<n<100K
sah:
- 1K<n<10K
sl:
- 1K<n<10K
sv-SE:
- 1K<n<10K
ta:
- 1K<n<10K
tr:
- 10K<n<100K
tt:
- 10K<n<100K
vot:
- n<1K
zh-CN:
- 10K<n<100K
zh-HK:
- n<1K
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 4
language_bcp47:
- ab
- ar
- br
- ca
- cnh
- cv
- cy
- de
- dv
- en
- eo
- es
- et
- eu
- fa
- fr
- ga-IE
- ia
- id
- it
- ja
- kab
- ky
- lv
- mn
- nl
- pt
- rm-sursilv
- ru
- rw
- sah
- sl
- sv-SE
- ta
- tr
- tt
- vot
- zh-CN
- zh-HK
- zh-TW
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.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 4
## 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:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 4257 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 3401 validated hours in 40 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, Arabic, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakha Chin, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Mongolian, Persian, Portuguese, Romansh Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Votic, Welsh
```
## 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_4_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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] |
Moo/korean-parallel-corpora
|
Moo
| 2022-07-01T15:32:54Z | 71 | 6 |
[
"task_categories:translation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"translation"
] | 2022-05-16T07:35:42Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- ko
- en
license:
- cc-by-sa-3.0
multilinguality:
- multilingual
- translation
pretty_name: 'korean-parallel-corpora '
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
|
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lccc
| null | 2022-11-18T22:07:56Z | 71 | 14 |
[
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:mit",
"arxiv:2008.03946",
"region:us"
] |
[
"conversational"
] | 2022-06-14T18:05:32Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- zh
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: lccc
pretty_name: 'LCCC: Large-scale Cleaned Chinese Conversation corpus'
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
dataset_info:
- config_name: large
features:
- name: dialog
list: string
splits:
- name: train
num_bytes: 1530827965
num_examples: 12007759
download_size: 607605643
dataset_size: 1530827965
- config_name: base
features:
- name: dialog
list: string
splits:
- name: train
num_bytes: 932634902
num_examples: 6820506
- name: test
num_bytes: 1498216
num_examples: 10000
- name: validation
num_bytes: 2922731
num_examples: 20000
download_size: 371475095
dataset_size: 937055849
---
# Dataset Card for LCCC
## Table of Contents
- [Dataset Card for LCCC](#dataset-card-for-lccc)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/thu-coai/CDial-GPT
- **Paper:** https://arxiv.org/abs/2008.03946
### Dataset Summary
LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
LCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
LCCC is in Chinese
LCCC中的对话是中文的
## Dataset Structure
### Data Instances
```json
{
"dialog": ["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"]
}
```
### Data Fields
- `dialog` (list of strings): List of utterances consisting of a dialogue.
### Data Splits
We do not provide the offical split for LCCC-large.
But we provide a split for LCCC-base:
|train|valid|test|
|---:|---:|---:|
|6,820,506 | 20,000 | 10,000|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
MIT License
Copyright (c) 2020 lemon234071
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
### Citation Information
```bibtex
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
### Contributions
Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.
|
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] |
bigbio/distemist
|
bigbio
| 2023-04-01T16:51:57Z | 71 | 3 |
[
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-13T22:08:11Z |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: DisTEMIST
homepage: https://zenodo.org/record/6671292
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for DisTEMIST
## Dataset Description
- **Homepage:** https://zenodo.org/record/6671292
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER,NED
The DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.
All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022.
## Citation Information
```
@article{miranda2022overview,
title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases
from clinical texts: results, methods, evaluation and multilingual resources},
author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell,
Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras,
Georgios and Paliouras, Georgios and Krallinger, Martin},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum.
CEUR Workshop Proceedings},
year={2022}
}
```
|
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] |
pszemraj/booksum-short
|
pszemraj
| 2023-02-27T08:45:01Z | 71 | 1 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"source_datasets:kmfoda/booksum",
"language:en",
"license:bsd-3-clause",
"booksum",
"long-document",
"region:us"
] |
[
"summarization",
"text2text-generation"
] | 2022-11-23T16:40:45Z |
---
source_datasets: kmfoda/booksum
license:
- bsd-3-clause
train-eval-index:
- config: pszemraj--booksum_short
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
chapter: text
summary_text: target
task_categories:
- summarization
- text2text-generation
language:
- en
tags:
- booksum
- long-document
size_categories:
- 10K<n<100K
---
---
# booksum short
`BookSum` but all summaries with length greater than 512 `long-t5` tokens are filtered out.
The columns `chapter_length` and `summary_length` **in this dataset** have been updated to reflect the total of Long-T5 tokens in the respective source text.
## Token Length Distribution for inputs
|
[
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] |
mesmalif/amazon-shoe-reviews
|
mesmalif
| 2023-02-06T16:07:08Z | 71 | 0 |
[
"region:us"
] | null | 2023-02-06T16:06:43Z |
---
dataset_info:
features:
- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
- name: labels
dtype: int64
- name: helpful_votes
dtype: int64
- name: total_votes
dtype: int64
- name: vine
dtype: int64
- name: verified_purchase
dtype: int64
- name: review_headline
dtype: string
- name: text
dtype: string
- name: review_date
dtype: string
splits:
- name: train
num_bytes: 34784832.6
num_examples: 90000
- name: test
num_bytes: 3864981.4
num_examples: 10000
download_size: 21283157
dataset_size: 38649814.0
---
# Dataset Card for "amazon-shoe-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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GEM/xmediasum
|
GEM
| 2023-02-15T14:01:56Z | 71 | 4 |
[
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:zh",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"summarization"
] | 2023-02-15T14:01:13Z |
---
annotations_creators:
- expert-generated
language:
- en
- zh
- de
language_creators:
- crowdsourced
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: xmediasum
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- summarization
task_ids: []
---
# Dataset Card for XMediaSum
### Dataset Summary
We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
- Paper: [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://aclanthology.org/2022.emnlp-main.526/) (EMNLP 2022)
- GitHub: https://github.com/krystalan/ClidSum
### Supported Task
- Cross-Lingual Summarization
- Cross-Lingual Dialogue Summarization
### Languages
- source language: English
- target language: Chinese and German
## Dataset Structure
### Data Instances
One example is given below in JSON format:
```json
{
"dialogue": "MADELELEINE BRAND, host: OK, here's some good news on the jobs front for both men and women. A new survey out today from the employment firm Manpower finds that about a quarter of employers will add jobs this summer. That's for adults, but for teenagers this summer's job market is shaping up to be the weakest in more than 50 years.\r\nALEX COHEN, host: So, how do you get your teenage kids not to spend the entire summer glued to the couch? You're about to get some tips from Michelle Singletary. She's Day to Day's personal finance contributor. Hi, Michelle!\r\nMICHELLE SINGLETARY: Hi!\r\nALEX COHEN, host: So why is the summer job market so hard for teens this year?\r\nMICHELLE SINGLETARY: Lot of things going on right now. We've got a tough economy. We've got a lot of college graduates going into the market. We have people who are losing their jobs and taking jobs that would traditionally go to teens, like in restaurants and retailers. And we have a lot of older people holding on to their jobs and not retiring because they can't afford to retire. And that puts teens at the end of the line when it comes to these types of jobs.\r\nALEX COHEN, host: So you've got a teenager at home, a little bit young for the working world just yet, but what would you say to a teenager who's out there hunting around for a job?\r\nMICHELLE SINGLETARY: If you absolutely need a job, keep looking. You know, obviously the types of jobs that teens tend to go for in retail, fast food, you know, they still need people. And oftentimes you know, listen, you may not get the job at the beginning of the summer, but hold on because in late summer, when some of those college students are going back and perhaps some of those people who lost their jobs are finding permanent positions with more pay, you might be able to still get that job. So don't give up, you may spend a month or month and a half without it, but go back to those retailers and those restaurants and those fast food places to see if they still need someone.\r\nALEX COHEN, host: And now I know parents like having the break from providing allowance. But, you know, is - are there reasons maybe not to push your teen towards taking a job?\r\nMICHELLE SINGLETARY: I think it absolutely is. In fact I think too many teens are working and they don't need to work. They're some who absolutely need, they're contributing to their household or they're putting money into their own college fund. But more often than not, what parents do is say you've got to get a job, and then the teens get the job and they spend all the money on clothes and you know videos and iPods and paying their cell phone bills because they don't need a cell phone anyway.\r\nALEX COHEN, host: So it's not going towards the college tuition at all.\r\nMICHELLE SINGLETARY: It is not. It's just disposable income that they're disposing of. And parents are not setting any limits and you know and then the kids get used to the fact that they're using all of their paycheck. That's another bad habit. Because they don't have to pay bills and all, all their income goes through you know this stuff.\r\nMICHELLE SINGLETARY: And when it comes time to get a real job, they're surprised they don't have enough money. And so you know what? You can wait to work. Instead, maybe they can spend the summer volunteering at a charitable organization or you know going back to school and boosting up their math skills or their English skills. We push the teens out into the market too soon, I think for some families.\r\nALEX COHEN, host: But now let's say your kid is working. What tips can parents provide in terms of holding on to that summer money?\r\nMICHELLE SINGLETARY: You know, before they get their job, they need to sit down with them and do a budget. So before they actually work and get that first paycheck I mean, you know, have them draw up a budge where the money is going. And you ought to have some requirements for some of their money. That's right, be a parent.\r\nMICHELLE SINGLETARY: So make them put some of it towards their college fund, if in fact they're headed for college. You know what? Make them put some away, I call it the tax fund, even though they may not have to pay taxes, but to pay for long-term things that they may want. You know, books once they get to college, or maybe they want to get a car, and they can actually pay cash for it, with some of these funds. Don't let them just go out and spend it on movies and stuff. You ought to set some guidelines - this is where you should put the money. And look at their budget.\r\nALEX COHEN, host: Day to Day's personal finance contributor Michelle Singletary. Thank you, Michelle!\r\nMICHELLE SINGLETARY: You're welcome.\r\nALEX COHEN, host: Stay with us. NPR's Day to Day continues.",
"summary": "The tight job market could be bad news for teens seeking summer work. If your teen does find a job, will he or she know how to manage those paychecks? Our personal finance contributor talks with Alex Cohen about ways to help teens find a job.",
"summary_de": "Der angespannte Arbeitsmarkt könnte für Jugendliche, die Sommerarbeit suchen, eine schlechte Nachricht sein. Wenn Ihr Teenager einen Job findet, wird er oder sie wissen, wie er mit diesen Gehaltsschecks umgeht? Unser Mitarbeiter für persönliche Finanzen spricht mit Alex Cohen darüber, wie Teenager bei der Jobsuche unterstützt werden können.",
"summary_zh": "紧张的就业市场对寻找暑期工作的青少年来说可能是个坏消息。如果你的孩子找到了一份工作，他/她懂得怎么管理这些薪水吗？我们的个人理财撰稿人与亚历克斯·科恩谈论如何帮助青少年找到工作。"
},
```
### Data Fields
- 'dialogue': An English dialogue
- 'summary': the original English summary of the corresponding dialogue (provided by MediaSum)
- 'summary_de': the human-translated German summary
- 'summary_zh': the human-translated Chinese summary
### Data Splits
- training set: 20K samples
- validation set: 10K samples
- testing set: 10K samples
## Dataset Creation
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Considerations for Using the Data
Please refer to [our paper](https://aclanthology.org/2022.emnlp-main.526/) for more details.
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/krystalan/ClidSum)
### Licensing Information
License: CC BY-NC-SA 4.0
### Citation Information
```
@inproceedings{wang-etal-2022-clidsum,
title = "{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization",
author = "Wang, Jiaan and
Meng, Fandong and
Lu, Ziyao and
Zheng, Duo and
Li, Zhixu and
Qu, Jianfeng and
Zhou, Jie",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.526",
pages = "7716--7729",
abstract = "We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.",
}
```
### Contributions
Thanks to [@krystalan](https://github.com/krystalan) for adding this dataset.
|
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azhx/counterfact
|
azhx
| 2023-04-07T21:22:57Z | 71 | 0 |
[
"region:us"
] | null | 2023-04-07T21:18:02Z |
---
dataset_info:
features:
- name: case_id
dtype: int64
- name: pararel_idx
dtype: int64
- name: requested_rewrite
struct:
- name: prompt
dtype: string
- name: relation_id
dtype: string
- name: subject
dtype: string
- name: target_new
struct:
- name: id
dtype: string
- name: str
dtype: string
- name: target_true
struct:
- name: id
dtype: string
- name: str
dtype: string
- name: paraphrase_prompts
sequence: string
- name: neighborhood_prompts
sequence: string
- name: attribute_prompts
sequence: string
- name: generation_prompts
sequence: string
splits:
- name: train
num_bytes: 29388723
num_examples: 19728
- name: test
num_bytes: 3268668
num_examples: 2191
download_size: 12387190
dataset_size: 32657391
---
# Dataset Card for "counterfact"
Dataset from [ROME](https://rome.baulab.info/) by Meng et al.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ctu-aic/csfever_v2
|
ctu-aic
| 2023-07-27T08:52:58Z | 71 | 1 |
[
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:natural-language-inference",
"task_ids:document-retrieval",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:fever",
"language:cs",
"license:cc-by-sa-3.0",
"Fact-checking",
"arxiv:2201.11115",
"region:us"
] |
[
"text-classification",
"text-retrieval"
] | 2023-05-09T14:19:36Z |
---
license: cc-by-sa-3.0
task_categories:
- text-classification
- text-retrieval
task_ids:
- natural-language-inference
- document-retrieval
language:
- cs
tags:
- Fact-checking
pretty_name: CsFEVERv2
multilinguality: monolingual
source_datasets: fever
size_categories:
- 100K<n<1M
---
# Dataset Card for "CsFEVERv2"
## Dataset Description
CsFEVERv2 is a dataset for Czech fact-checking developed as part of a bachelor thesis at the Artificial Intelligence Center of the Faculty of Electrical Engineering of
the Czech technical university in Prague. The dataset consists of an **original** subset, which is only an iteration of CsFEVER with new data and better processing and
**f1**, **precision**, and **07** subsets filtered using an NLI model and optimized threshold values. The subset **wiki_pages** is a processed Wikipedia dump from
August 2022 with correct revids. This subset should be used to map evidence from datasets to Wikipedia texts. Additionaly preprocessed datasets **original_nli**, **f1_nli**, **precision_nli**, **07_nli**,
for training of NLI models are included.
The original subset can be used to generate other filtered datasets by filtering with other thresholds using predicted_label and predicted_score fields.
### Languages
Czech
## Dataset Usage Example
```python
from datasets import load_dataset
#load default (original) subset
dataset = load_dataset("/home/mlynatom/csfever_v2")
dataset = load_dataset("/home/mlynatom/csfever_v2", "original")
#load f1, f1_nli, precision, precision_nli, 07, and 07_nli subsets
dataset = load_dataset("/home/mlynatom/csfever_v2", "f1")
#load wiki_pages subset
dataset = load_dataset("/home/mlynatom/csfever_v2", "wiki_pages")
```
## Dataset Structure
### Data Instances
#### original
An example of 'train' looks as follows.
```json
{'id': 75397,
'label': 'SUPPORTS',
'predicted_label': 'SUPPORTS',
'predicted_score': 0.921731
'claim': 'Nikolaj Coster-Waldau pracoval pro Fox Broadcasting Company.',
'evidence': [ [ "Nikolaj Coster-Waldau", "Nikolaj Coster-Waldau" ], [ "Fox Broadcasting Company", "Fox Broadcasting Company" ] ]}
```
#### f1, precision, 07
An example of 'train' looks as follows.
```json
{'id': 75397,
'label': 'SUPPORTS',
'claim': 'Nikolaj Coster-Waldau pracoval pro Fox Broadcasting Company.',
'evidence': [ [ "Nikolaj Coster-Waldau", "Nikolaj Coster-Waldau" ], [ "Fox Broadcasting Company", "Fox Broadcasting Company" ] ]}
```
#### original_nli, f1_nli, precision_nli, 07_nli
An example of 'train' looks as follows.
```json
{'id': 155439,
'label': 2,
'claim': 'Newcastle United FC vyhrál pět ligových titulů.',
'evidence': "Ronnie Simpson. Ronnie Simpson (21. října 1930, Glasgow – 19. dubna 2004, Edinburgh) byl skotský fotbalový brankář..."}
```
#### wiki_pages
An example of 'wiki_pages' looks as follows.
```json
{'id': 80916,
'revid': 20561555,
'url': "https://cs.wikipedia.org/wiki?curid=80916",
'title': "Altruismus",
'text': "Altruismus (z lat. "alter", druhý, 3. pád "altrui", druhému) je moderní ..."}
```
### Data Fields
#### original
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `predicted_label`: a `string` feature. (label predicted by NLI model)
- `predicted_score`: a `int32` feature. (confidence of predicted_label predicted by NLI model)
- `claim`: a `string` feature.
- `evidence`: a `sequence` feature.
#### f1, precision, 07
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `claim`: a `string` feature.
- `evidence`: a `sequence` feature.
#### original_nli, f1_nli, precision_nli, 07_nli
- `id`: a `int32` feature.
- `label`: a `int32` feature.
- `claim`: a `string` feature.
- `evidence`: a `string` feature.
#### wiki_pages
- `id`: a `int32` feature.
- `revid`: a `int32` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
### Data Splits
#### original
| | train | dev | test |
|----------|-------:|-----:|------:|
| original | 118950 | 7458 | 7520 |
#### f1
| | train | dev | test |
|----|------:|-----:|-----:|
| f1 | 83438 | 5445 | 5328 |
#### precision
| | train | dev | test |
|-----------|-------:|-----:|------:|
| precision | 60828 | 4288 | 4236 |
#### 07
| | train | dev | test |
|----|-------:|-----:|------:|
| 07 | 108607 | 6685 | 6623 |
#### wiki_pages
| | wiki_pages |
|------------|-----------:|
| wiki_pages | 825078 |
# Citation
```bibtex
@article{Ullrich_2023,
doi = {10.1007/s10579-023-09654-3},
url = {https://doi.org/10.1007%2Fs10579-023-09654-3},
year = 2023,
month = {may},
publisher = {Springer Science and Business Media {LLC}},
author = {Herbert Ullrich and Jan Drchal and Martin Rýpar and Hana Vincourová and Václav Moravec},
title = {{CsFEVER} and {CTKFacts}: acquiring Czech data for fact verification},
journal = {Language Resources and Evaluation},
archivePrefix={arXiv},
eprint={2201.11115},
}
```
```bibtex
@thesis{Mlynar_2023,
author = {Mlynář, Tomáš},
type = {Bachelor's Thesis}
title = {Automated Fact Checking Based on Czech Wikipedia},
institution = {Czech Technical University in Prague, Faculty of Electrical Engineering},
date = {2023},
url = {http://hdl.handle.net/10467/109219}
}
```
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cdminix/libritts-aligned
|
cdminix
| 2023-10-11T19:46:28Z | 71 | 5 |
[
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"speech",
"audio",
"automatic-speech-recognition",
"text-to-speech",
"arxiv:1904.02882",
"arxiv:2211.16049",
"region:us"
] |
[
"automatic-speech-recognition",
"text-to-speech"
] | 2023-05-14T10:29:46Z |
---
pretty_name: LibriTTS Corpus with Forced Alignments
annotations_creators:
- crowdsourced
language: en
tags:
- speech
- audio
- automatic-speech-recognition
- text-to-speech
license:
- cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
extra_gated_prompt: "When using this dataset to download LibriTTS, you agree to the terms on https://www.openslr.org"
---
> There is also an identical dataset for the new libritts-r dataset at [cdminix/libritts-r-aligned](https://huggingface.co/datasets/cdminix/libritts-r-aligned)
# Dataset Card for LibriTTS with Forced Alignments (and Measures)
UPDATE: The preprocessed alignments are now in this repository, so montreal forced aligner does not have to run locally.
## Requirements
- ``pip install alignments phones`` **(required)**
- ``pip install speech-collator`` (optional)
## Example Item
```json
{
'id': '100_122655_000073_000002.wav',
'speaker': '100',
'text': 'the day after, diana and mary quitted it for distant b.',
'start': 0.0,
'end': 3.6500000953674316,
'phones': ['[SILENCE]', 'ð', 'ʌ', '[SILENCE]', 'd', 'eɪ', '[SILENCE]', 'æ', 'f', 't', 'ɜ˞', '[COMMA]', 'd', 'aɪ', 'æ', 'n', 'ʌ', '[SILENCE]', 'æ', 'n', 'd', '[SILENCE]', 'm', 'ɛ', 'ɹ', 'i', '[SILENCE]', 'k', 'w', 'ɪ', 't', 'ɪ', 'd', '[SILENCE]', 'ɪ', 't', '[SILENCE]', 'f', 'ɜ˞', '[SILENCE]', 'd', 'ɪ', 's', 't', 'ʌ', 'n', 't', '[SILENCE]', 'b', 'i', '[FULL STOP]'],
'phone_durations': [5, 2, 4, 0, 5, 13, 0, 16, 7, 5, 20, 2, 6, 9, 15, 4, 2, 0, 11, 3, 5, 0, 3, 8, 9, 8, 0, 13, 3, 5, 3, 6, 4, 0, 8, 5, 0, 9, 5, 0, 7, 5, 6, 7, 4, 5, 10, 0, 3, 35, 9],
'audio': '/dev/shm/metts/train-clean-360-alignments/100/100_122655_000073_000002.wav'
}
```
The phones are IPA phones, and the phone durations are in frames (assuming a hop length of 256, sample rate of 22050 and window length of 1024). These attributes can be changed using the ``hop_length``, ``sample_rate`` and ``window_length`` arguments to ``LibriTTSAlign``.
## Data Collator
This dataset comes with a data collator which can be used to create batches of data for training.
It can be installed using ``pip install speech-collator`` ([MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator)) and can be used as follows:
```python
import json
from datasets import load_dataset
from speech_collator import SpeechCollator
from torch.utils.data import DataLoader
dataset = load_dataset('cdminix/libritts-aligned', split="train")
speaker2ixd = json.load(open("speaker2idx.json"))
phone2ixd = json.load(open("phone2idx.json"))
collator = SpeechCollator(
speaker2ixd=speaker2idx,
phone2ixd=phone2idx ,
)
dataloader = DataLoader(dataset, collate_fn=collator.collate_fn, batch_size=8)
```
You can either download the ``speaker2idx.json`` and ``phone2idx.json`` files from [here](https://huggingface.co/datasets/cdminix/libritts-aligned/tree/main/data) or create them yourself using the following code:
```python
import json
from datasets import load_dataset
from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx
dataset = load_dataset("cdminix/libritts-aligned", split="train")
# Create speaker2idx and phone2idx
speaker2idx = create_speaker2idx(dataset, unk_idx=0)
phone2idx = create_phone2idx(dataset, unk_idx=0)
# save to json
with open("speaker2idx.json", "w") as f:
json.dump(speaker2idx, f)
with open("phone2idx.json", "w") as f:
json.dump(phone2idx, f)
```
### Measures
When using ``speech-collator`` you can also use the ``measures`` argument to specify which measures to use. The following example extracts Pitch and Energy on the fly.
```python
import json
from torch.utils.data import DataLoader
from datasets import load_dataset
from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx
from speech_collator.measures import PitchMeasure, EnergyMeasure
dataset = load_dataset("cdminix/libritts-aligned", split="train")
speaker2idx = json.load(open("data/speaker2idx.json"))
phone2idx = json.load(open("data/phone2idx.json"))
# Create SpeechCollator
speech_collator = SpeechCollator(
speaker2idx=speaker2idx,
phone2idx=phone2idx,
measures=[PitchMeasure(), EnergyMeasure()],
return_keys=["measures"]
)
# Create DataLoader
dataloader = DataLoader(
dataset,
batch_size=8,
collate_fn=speech_collator.collate_fn,
)
```
COMING SOON: Detailed documentation on how to use the measures at [MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator).
## Splits
This dataset has the following splits:
- ``train``: All the training data, except one sample per speaker which is used for validation.
- ``dev``: The validation data, one sample per speaker.
- ``train.clean.100``: Training set derived from the original materials of the train-clean-100 subset of LibriSpeech.
- ``train.clean.360``: Training set derived from the original materials of the train-clean-360 subset of LibriSpeech.
- ``train.other.500``: Training set derived from the original materials of the train-other-500 subset of LibriSpeech.
- ``dev.clean``: Validation set derived from the original materials of the dev-clean subset of LibriSpeech.
- ``dev.other``: Validation set derived from the original materials of the dev-other subset of LibriSpeech.
- ``test.clean``: Test set derived from the original materials of the test-clean subset of LibriSpeech.
- ``test.other``: Test set derived from the original materials of the test-other subset of LibriSpeech.
## Environment Variables
There are a few environment variable which can be set.
- ``LIBRITTS_VERBOSE``: If set, will print out more information about the dataset creation process.
- ``LIBRITTS_MAX_WORKERS``: The number of workers to use when creating the alignments. Defaults to ``cpu_count()``.
- ``LIBRITTS_PATH``: The path to download LibriTTS to. Defaults to the value of ``HF_DATASETS_CACHE``.
# Citation
When using LibriTTS please cite the following papers:
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://arxiv.org/abs/1904.02882)
- [Montreal Forced Aligner: Trainable text-speech alignment using Kaldi](https://www.researchgate.net/publication/319185277_Montreal_Forced_Aligner_Trainable_Text-Speech_Alignment_Using_Kaldi)
When using the Measures please cite the following paper (ours):
- [Evaluating and reducing the distance between synthetic and real speech distributions](https://arxiv.org/abs/2211.16049)
|
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] |
doushabao4766/weibo_ner_knowledge_V3_wc
|
doushabao4766
| 2023-05-20T02:21:51Z | 71 | 1 |
[
"region:us"
] | null | 2023-05-20T02:21:48Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-GPE.NAM
'1': B-GPE.NOM
'2': B-LOC.NAM
'3': B-LOC.NOM
'4': B-ORG.NAM
'5': B-ORG.NOM
'6': B-PER.NAM
'7': B-PER.NOM
'8': I-GPE.NAM
'9': I-GPE.NOM
'10': I-LOC.NAM
'11': I-LOC.NOM
'12': I-ORG.NAM
'13': I-ORG.NOM
'14': I-PER.NAM
'15': I-PER.NOM
'16': O
- name: knowledge
dtype: string
- name: token_words
sequence:
sequence: string
- name: knowledge_words
sequence:
sequence: string
splits:
- name: train
num_bytes: 7027512
num_examples: 1350
- name: validation
num_bytes: 1116528
num_examples: 270
- name: test
num_bytes: 1107689
num_examples: 270
download_size: 2405285
dataset_size: 9251729
---
# Dataset Card for "weibo_ner_knowledge_V3_wc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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HumanCompatibleAI/ppo-seals-CartPole-v0
|
HumanCompatibleAI
| 2023-05-29T09:52:49Z | 71 | 0 |
[
"region:us"
] | null | 2023-05-29T09:52:45Z |
---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 516313
num_examples: 24
download_size: 297546
dataset_size: 516313
---
# Dataset Card for "ppo-seals-CartPole-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shibing624/snli-zh
|
shibing624
| 2023-06-14T07:15:52Z | 71 | 2 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:shibing624",
"language_creators:liuhuanyong",
"multilinguality:monolingual",
"size_categories:100K<n<20M",
"source_datasets:https://github.com/liuhuanyong/ChineseTextualInference/",
"language:zh",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2023-06-14T04:33:26Z |
---
annotations_creators:
- shibing624
language_creators:
- liuhuanyong
language:
- zh
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<20M
source_datasets:
- https://github.com/liuhuanyong/ChineseTextualInference/
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: snli
pretty_name: Stanford Natural Language Inference
---
# Dataset Card for SNLI_zh
## Dataset Description
- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
- **Dataset:** [train data from ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/)
- **Size of downloaded dataset files:** 54 MB
- **Total amount of disk used:** 54 MB
### Dataset Summary
中文SNLI和MultiNLI数据集，翻译自英文[SNLI](https://huggingface.co/datasets/snli)和[MultiNLI](https://huggingface.co/datasets/multi_nli)
### Supported Tasks and Leaderboards
Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。
中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果：
**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec)
### Languages
数据集均是简体中文文本。
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
sentence1 sentence2 gold_label
是的，我想一个洞穴也会有这样的问题 我认为洞穴可能会有更严重的问题。 neutral
几周前我带他和一个朋友去看幼儿园警察 我还没看过幼儿园警察，但他看了。 contradiction
航空旅行的扩张开始了大众旅游的时代，希腊和爱琴海群岛成为北欧人逃离潮湿凉爽的夏天的令人兴奋的目的地。 航空旅行的扩大开始了许多旅游业的发展。 entailment
```
### Data Fields
The data fields are the same among all splits.
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including entailment(0), neutral(1), contradiction(2). 注意：此数据集0表示相似，2表示不相似。
-
### Data Splits
after remove None and len(text) < 1 data:
```shell
$ wc -l ChineseTextualInference-train.txt
419402 total
```
### Data Length
## Dataset Creation
### Curation Rationale
作为中文SNLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。
@inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title = {A large annotated corpus for learning natural language inference},
Year = {2015}
}
### Annotations
#### Annotation process
#### Who are the annotators?
原作者。
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context.
Systems that are successful at such a task may be more successful in modeling semantic representations.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
- [liuhuanyong](https://github.com/liuhuanyong/ChineseTextualInference/)翻译成中文
- [shibing624](https://github.com/shibing624) 上传到huggingface的datasets
### Licensing Information
用于学术研究。
### Contributions
[shibing624](https://github.com/shibing624) add this dataset.
|
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] |
youssef101/artelingo-dummy
|
youssef101
| 2023-07-23T16:21:23Z | 71 | 1 |
[
"task_categories:image-to-text",
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"language:ar",
"language:zh",
"license:mit",
"Affective Captioning",
"Emotions",
"Prediction",
"Art",
"ArtELingo",
"region:us"
] |
[
"image-to-text",
"text-classification",
"image-classification",
"text-to-image",
"text-generation"
] | 2023-07-23T14:41:17Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: art_style
dtype: string
- name: painting
dtype: string
- name: emotion
dtype: string
- name: language
dtype: string
- name: text
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 18587167692.616
num_examples: 62989
- name: validation
num_bytes: 965978050.797
num_examples: 3191
- name: test
num_bytes: 2330046601.416
num_examples: 6402
download_size: 4565327615
dataset_size: 21883192344.829002
task_categories:
- image-to-text
- text-classification
- image-classification
- text-to-image
- text-generation
language:
- en
- ar
- zh
tags:
- Affective Captioning
- Emotions
- Prediction
- Art
- ArtELingo
pretty_name: ArtELingo
size_categories:
- 100K<n<1M
---
ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems.
The dataset in ArtELingo contains many artworks with multiple annotations in three languages, providing a diverse set of data that enables the study of similarities and differences across languages and cultures. The researchers investigate captioning tasks and find that diversity in annotations improves the performance of baseline models.
The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI. By including annotations in multiple languages and considering cultural differences, the dataset aims to build more human-compatible AI that is sensitive to emotional nuances across various cultural contexts. The researchers believe that studying emotions in this way is crucial to understanding a significant aspect of human intelligence.
In summary, ArtELingo is a dataset that extends ArtEmis by providing annotations in multiple languages and cultures, facilitating research on diversity in AI systems and improving their performance in emotion-related tasks like label prediction and affective caption generation. The dataset is publicly available, and the researchers hope that it will facilitate future studies in multilingual and culturally-aware artificial intelligence.
|
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] |
germank/hh-rlhf_with_features_flan_t5_large
|
germank
| 2023-07-24T14:19:59Z | 71 | 0 |
[
"region:us"
] | null | 2023-07-24T14:07:42Z |
---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: helpfulness_chosen
dtype: int64
- name: helpfulness_rejected
dtype: int64
- name: specificity_chosen
dtype: int64
- name: specificity_rejected
dtype: int64
- name: intent_chosen
dtype: int64
- name: intent_rejected
dtype: int64
- name: factuality_chosen
dtype: int64
- name: factuality_rejected
dtype: int64
- name: easy-to-understand_chosen
dtype: int64
- name: easy-to-understand_rejected
dtype: int64
- name: relevance_chosen
dtype: int64
- name: relevance_rejected
dtype: int64
- name: readability_chosen
dtype: int64
- name: readability_rejected
dtype: int64
- name: enough-detail_chosen
dtype: int64
- name: enough-detail_rejected
dtype: int64
- name: biased:_chosen
dtype: int64
- name: biased:_rejected
dtype: int64
- name: fail-to-consider-individual-preferences_chosen
dtype: int64
- name: fail-to-consider-individual-preferences_rejected
dtype: int64
- name: repetetive_chosen
dtype: int64
- name: repetetive_rejected
dtype: int64
- name: fail-to-consider-context_chosen
dtype: int64
- name: fail-to-consider-context_rejected
dtype: int64
- name: too-long_chosen
dtype: int64
- name: too-long_rejected
dtype: int64
- name: human
dtype: string
- name: assistant_chosen
dtype: string
- name: assistant_rejected
dtype: string
- name: log_score_chosen
dtype: float64
- name: log_score_rejected
dtype: float64
- name: labels
dtype: string
splits:
- name: train
num_bytes: 14434424
num_examples: 9574
- name: test
num_bytes: 14378349
num_examples: 9574
download_size: 15748504
dataset_size: 28812773
---
# Dataset Card for "hh-rlhf_with_features_flan_t5_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vencortex/DeOSAgentDocuments
|
vencortex
| 2023-07-25T14:20:30Z | 71 | 0 |
[
"region:us"
] | null | 2023-07-25T14:20:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: company_id
dtype: string
- name: context_id
dtype: string
- name: source
dtype: string
- name: date
dtype: string
- name: text
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 33884007
num_examples: 10000
download_size: 29585235
dataset_size: 33884007
---
# Dataset Card for "DeOSAgentDocuments"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nathanReitinger/mlcb
|
nathanReitinger
| 2023-11-04T02:30:20Z | 71 | 0 |
[
"region:us"
] | null | 2023-10-25T01:54:40Z |
---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 8132250961
num_examples: 76369
- name: test
num_bytes: 897865830
num_examples: 8486
download_size: 2715307703
dataset_size: 9030116791
---
# Dataset Card for "mlcb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CJWeiss/ukabs_id_rename
|
CJWeiss
| 2023-11-06T15:28:47Z | 71 | 0 |
[
"region:us"
] | null | 2023-11-06T15:28:40Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 53147657
num_examples: 594
- name: test
num_bytes: 10152794
num_examples: 120
- name: valid
num_bytes: 8112656
num_examples: 79
download_size: 33052341
dataset_size: 71413107
---
# Dataset Card for "ukabs_id_rename"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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bgspaditya/byt-malicious-url-treatment
|
bgspaditya
| 2023-11-12T12:04:00Z | 71 | 0 |
[
"region:us"
] | null | 2023-11-12T11:45:48Z |
---
dataset_info:
features:
- name: url
dtype: string
- name: type
dtype: string
- name: type_code
dtype: int64
splits:
- name: train
num_bytes: 42342364.57124805
num_examples: 512794
- name: val
num_bytes: 5292774.928436036
num_examples: 64099
- name: test
num_bytes: 5292857.500315916
num_examples: 64100
download_size: 31993322
dataset_size: 52927997.0
---
# Dataset Card for "byt-malicious-url-treatment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jlbaker361/subtraction_whole
|
jlbaker361
| 2023-11-15T13:00:00Z | 71 | 0 |
[
"region:us"
] | null | 2023-11-14T23:39:09Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1192290.3
num_examples: 29376
- name: test
num_bytes: 132476.7
num_examples: 3264
download_size: 684606
dataset_size: 1324767.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "subtraction_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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atmallen/qm_alice_easy_2_grader_first_1.0e
|
atmallen
| 2023-11-16T18:27:13Z | 71 | 0 |
[
"region:us"
] | null | 2023-11-16T03:19:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 10359818.0
num_examples: 117117
- name: validation
num_bytes: 1000602.0
num_examples: 11279
- name: test
num_bytes: 993048.0
num_examples: 11186
download_size: 2659401
dataset_size: 12353468.0
---
# Dataset Card for "qm_alice_easy_2_grader_first_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ChengAoShen/emoji_dataset
|
ChengAoShen
| 2023-11-21T11:59:50Z | 71 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T10:29:27Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 477727160.512
num_examples: 80672
download_size: 400526151
dataset_size: 477727160.512
license: mit
---
# Emoji_dataset
This dataset including various emojis to enable training diffusion and other generative model.
|
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aops02/MetaMath-Vi
|
aops02
| 2023-11-27T19:22:02Z | 71 | 2 |
[
"region:us"
] | null | 2023-11-23T14:37:49Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 72172855
num_examples: 32972
download_size: 15771804
dataset_size: 72172855
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "MetaMath-Vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ccdv/WCEP-10
|
ccdv
| 2022-10-25T10:55:52Z | 70 | 3 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"conditional-text-generation",
"arxiv:2005.10070",
"arxiv:2110.08499",
"region:us"
] |
[
"summarization",
"text2text-generation"
] | 2022-05-09T14:13:26Z |
---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# WCEP10 dataset for summarization
Summarization dataset copied from [PRIMERA](https://github.com/allenai/PRIMER)
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/WCEP-10": ("document", "summary")
```
# Configs
4 possibles configs:
- `roberta` will concatenate documents with "\</s\>" (default)
- `newline` will concatenate documents with "\n"
- `bert` will concatenate documents with "[SEP]"
- `list` will return the list of documents instead of a string
### Data Fields
- `id`: paper id
- `document`: a string/list containing the body of a set of documents
- `summary`: a string containing the abstract of the set
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
| Dataset Split | Number of Instances |
| ------------- | --------------------|
| Train | 8158 |
| Validation | 1020 |
| Test | 1022 |
# Cite original article
```
@article{DBLP:journals/corr/abs-2005-10070,
author = {Demian Gholipour Ghalandari and
Chris Hokamp and
Nghia The Pham and
John Glover and
Georgiana Ifrim},
title = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
Current Events Portal},
journal = {CoRR},
volume = {abs/2005.10070},
year = {2020},
url = {https://arxiv.org/abs/2005.10070},
eprinttype = {arXiv},
eprint = {2005.10070},
timestamp = {Fri, 22 May 2020 16:21:28 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-2110-08499,
author = {Wen Xiao and
Iz Beltagy and
Giuseppe Carenini and
Arman Cohan},
title = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document
Summarization},
journal = {CoRR},
volume = {abs/2110.08499},
year = {2021},
url = {https://arxiv.org/abs/2110.08499},
eprinttype = {arXiv},
eprint = {2110.08499},
timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
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] |
victor/autotrain-data-donut-vs-croissant
|
victor
| 2022-09-09T20:32:23Z | 70 | 0 |
[
"task_categories:image-classification",
"region:us"
] |
[
"image-classification"
] | 2022-09-09T20:29:58Z |
---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: donut-vs-croissant
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project donut-vs-croissant.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<512x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(num_classes=2, names=['croissant', 'donut'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 133 |
| valid | 362 |
|
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copenlu/spiced
|
copenlu
| 2022-10-24T12:31:04Z | 70 | 2 |
[
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|s2orc",
"language:en",
"license:mit",
"scientific text",
"scholarly text",
"semantic text similarity",
"fact checking",
"misinformation",
"region:us"
] |
[
"text-classification"
] | 2022-10-20T15:18:50Z |
---
annotations_creators:
- crowdsourced
- machine-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: SPICED
size_categories:
- 1K<n<10K
source_datasets:
- extended|s2orc
tags:
- scientific text
- scholarly text
- semantic text similarity
- fact checking
- misinformation
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
---
# Dataset Card for SPICED
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.copenlu.com/publication/2022_emnlp_wright/
- **Repository:** https://github.com/copenlu/scientific-information-change
- **Paper:**
### Dataset Summary
The Scientific Paraphrase and Information ChangE Dataset (SPICED) is a dataset of paired scientific findings from scientific papers, news media, and Twitter. The types of pairs are between <paper, news> and <paper, tweet>. Each pair is labeled for the degree of information similarity in the _findings_ described by each sentence, on a scale from 1-5. This is called the _Information Matching Score (IMS)_. The data was curated from S2ORC and matched news articles and Tweets using Altmetric. Instances are annotated by experts using the Prolific platform and Potato. Please use the following citation when using this dataset:
```
@article{modeling-information-change,
title={{Modeling Information Change in Science Communication with Semantically Matched Paraphrases}},
author={Wright, Dustin and Pei, Jiaxin and Jurgens, David and Augenstein, Isabelle},
year={2022},
booktitle = {Proceedings of EMNLP},
publisher = {Association for Computational Linguistics},
year = 2022
}
```
### Supported Tasks and Leaderboards
The task is to predict the IMS between two scientific sentences, which is a scalar between 1 and 5. Preferred metrics are mean-squared error and Pearson correlation.
### Languages
English
## Dataset Structure
### Data Fields
- DOI: The DOI of the original scientific article
- instance\_id: Unique instance ID for the sample. The ID contains the field, whether or not it is a tweet, and whether or not the sample was manually labeled or automatically using SBERT (marked as "easy")
- News Finding: Text of the news or tweet finding
- Paper Finding: Text of the paper finding
- News Context: For news instances, the surrounding two sentences for the news finding. For tweets, a copy of the tweet
- Paper Context: The surrounding two sentences for the paper finding
- scores: Annotator scores after removing low competence annotators
- field: The academic field of the paper ('Computer\_Science', 'Medicine', 'Biology', or 'Psychology')
- split: The dataset split ('train', 'val', or 'test')
- final\_score: The IMS of the instance
- source: Either "news" or "tweet"
- News Url: A URL to the source article if a news instance or the tweet ID of a tweet
### Data Splits
- train: 4721 instances
- validation: 664 instances
- test: 640 instances
## Dataset Creation
For the full details of how the dataset was created, please refer to our [EMNLP 2022 paper]().
### Curation Rationale
Science communication is a complex process of translation from highly technical scientific language to common language that lay people can understand. At the same time, the general public relies on good science communication in order to inform critical decisions about their health and behavior. SPICED was curated in order to provide a training dataset and benchmark for machine learning models to measure changes in scientific information at different stages of the science communication pipeline.
### Source Data
#### Initial Data Collection and Normalization
Scientific text: S2ORC
News articles and Tweets are collected through Altmetric.
#### Who are the source language producers?
Scientists, journalists, and Twitter users.
### 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
Models trained on SPICED can be used to perform large scale analyses of science communication. They can be used to match the same finding discussed in different media, and reveal trends in differences in reporting at different stages of the science communication pipeline. It is hoped that this can help to build tools which will improve science communication.
### Discussion of Biases
The dataset is restricted to computer science, medicine, biology, and psychology, which may introduce some bias in the topics which models will perform well on.
### Other Known Limitations
While some context is available, we do not release the full text of news articles and scientific papers, which may contain further context to help with learning the task. We do however provide the paper DOIs and links to the original news articles in case full text is desired.
## Additional Information
### Dataset Curators
Dustin Wright, Jiaxin Pei, David Jurgens, and Isabelle Augenstein
### Licensing Information
MIT
### Contributions
Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset.
|
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] |
bigbio/tmvar_v2
|
bigbio
| 2022-12-22T15:47:06Z | 70 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:12:31Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: tmVar v2
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for tmVar v2
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
This dataset contains 158 PubMed articles manually annotated with mutation mentions of various kinds and dbsnp normalizations for each of them.
It can be used for NER tasks and NED tasks, This dataset has a single split
## Citation Information
```
@article{wei2018tmvar,
title={tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine},
author={Wei, Chih-Hsuan and Phan, Lon and Feltz, Juliana and Maiti, Rama and Hefferon, Tim and Lu, Zhiyong},
journal={Bioinformatics},
volume={34},
number={1},
pages={80--87},
year={2018},
publisher={Oxford University Press}
}
```
|
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ghmfx/natural-questions-short
|
ghmfx
| 2022-12-17T21:29:07Z | 70 | 0 |
[
"license:wtfpl",
"region:us"
] | null | 2022-12-17T21:28:43Z |
---
license: wtfpl
---
|
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laion/laion2B-en-md5
|
laion
| 2023-01-07T00:35:33Z | 70 | 2 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-01-06T23:37:05Z |
---
license: cc-by-4.0
---
|
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ChristophSchuhmann/essays-with-instructions
|
ChristophSchuhmann
| 2023-01-26T21:59:21Z | 70 | 11 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-01-26T21:57:19Z |
---
license: apache-2.0
---
|
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] |
TurkuNLP/squad_v2_fi
|
TurkuNLP
| 2023-10-10T19:55:56Z | 70 | 0 |
[
"task_categories:question-answering",
"language:fi",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"question-answering"
] | 2023-01-30T09:03:27Z |
---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- fi
---
### Dataset Summary
This is a Finnish SQuAD question answering dataset. It is a DeepL -based machine translation of the English SQuAD2.0 dataset which combines the 100,000 questions in
SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported
by the paragraph and abstain from answering.
### Data Fields
The data fields are the same among all splits.
#### Example Data
```
{
"title": "Victoria_(Australia)",
"paragraphs": [
{
"qas": [
{
"question": "Millainen talous Victoriassa on?",
"id": "570d2417fed7b91900d45c3d",
"answers": [
{
"text": "monipuolinen",
"answer_start": 26,
"texts": [
"monipuolinen"
],
"starts": [
26
]
},
{
"text": "hyvin monipuolinen",
"answer_start": 20,
"texts": [
"hyvin ",
"monipuolinen"
],
"starts": [
20,
26
]
},
{
"text": "hyvin monipuolinen",
"answer_start": 20,
"texts": [
"hyvin ",
"monipuolinen"
],
"starts": [
20,
26
]
}
],
"is_impossible": false
}
],
"context": "Victorian talous on hyvin monipuolinen: palvelualat, kuten rahoitus- ja kiinteistöpalvelut, terveydenhuolto, koulutus, tukkukauppa, vähittäiskauppa, majoitus- ja ravitsemistoiminta ja teollisuus muodostavat suurimman osan työllisyydestä. Victorian osavaltion bruttokansantuote on Australian toiseksi suurin, vaikka Victoria on asukaskohtaisen bruttokansantuotteen osalta neljäntenä, koska sen kaivostoiminta on vähäistä. Kulttuurin alalla Melbournessa on useita museoita, taidegallerioita ja teattereita, ja sitä kutsutaan myös \"Australian urheilupääkaupungiksi\". Melbournen krikettikenttä (Melbourne Cricket Ground) on Australian suurin stadion, ja siellä järjestettiin vuoden 1956 kesäolympialaiset ja vuoden 2006 Kansainyhteisön kisat. Kenttää pidetään myös australialaisen kriketin ja australialaisen jalkapallon \"henkisenä kotina\", ja se isännöi vuosittain Australian jalkapalloliigan (AFL) suurta loppuottelua, johon osallistuu yleensä yli 95 000 ihmistä. Victoriaan kuuluu kahdeksan julkista yliopistoa, joista vanhin, Melbournen yliopisto, on perustettu vuonna 1853."
}
]
}
```
#### squad_v2
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `texts`: a `string` feature.
- `starts`: a `int32` feature.
### Data Splits
| name | train | validation |
| -------- | -----: | ---------: |
| squad_v2 | 130319 | 11873 |
### Evaluation Results
Results from fine-tuning [TurkuNLP/bert-base-finnish-cased-v1](ttps://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) for extractive question answering.
| dataset | F1 |
| -------------------- | ----: |
| TurkuNLP/squad_v2_fi | 73.66 |
| ilmariky/SQuAD_v2_fi | 61.87 |
### Considerations for Using the Data
Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation
system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations
except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
### Licensing Information
Contents of this repository are distributed under the
[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Copyright of the dataset contents belongs to the original copyright holders.
|
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d0rj/dialogsum-ru
|
d0rj
| 2023-05-13T06:27:30Z | 70 | 3 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:knkarthick/dialogsum",
"language:ru",
"license:mit",
"conversations-summarization",
"dialogue-summarization",
"region:us"
] |
[
"summarization",
"text2text-generation",
"text-generation"
] | 2023-05-08T14:17:46Z |
---
annotations_creators:
- expert-generated
language_creators:
- translated
language:
- ru
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- knkarthick/dialogsum
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids: []
pretty_name: DIALOGSum Corpus (ru)
tags:
- conversations-summarization
- dialogue-summarization
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 19115158
num_examples: 12460
- name: validation
num_bytes: 746312
num_examples: 500
- name: test
num_bytes: 2282379
num_examples: 1500
download_size: 10144708
dataset_size: 22143849
train-eval-index:
- config: samsum
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
dialogue: text
summary: target
---
# Dataset Card for DIALOGSum Corpus
## Dataset Description
### Links
- **Homepage:** https://aclanthology.org/2021.findings-acl.449
- **Repository:** https://github.com/cylnlp/dialogsum
- **Paper:** https://aclanthology.org/2021.findings-acl.449
### Dataset Summary
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.
### Languages
Russian (translated from English by Google Translate).
## Dataset Structure
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- topic: human written topic/one liner of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 12460
- val: 500
- test: 1500
- holdout: 100 [Only 3 features: id, dialogue, topic]
## Dataset Creation
### Curation Rationale
In paper:
We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers.
Compared with previous datasets, dialogues from DialogSum have distinct characteristics:
Under rich real-life scenarios, including more diverse task-oriented scenarios;
Have clear communication patterns and intents, which is valuable to serve as summarization sources;
Have a reasonable length, which comforts the purpose of automatic summarization.
We ask annotators to summarize each dialogue based on the following criteria:
Convey the most salient information;
Be brief;
Preserve important named entities within the conversation;
Be written from an observer perspective;
Be written in formal language.
### Who are the source language producers?
linguists
### Who are the annotators?
language experts
## Licensing Information
MIT License
## Citation Information
```
@inproceedings{chen-etal-2021-dialogsum,
title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset",
author = "Chen, Yulong and
Liu, Yang and
Chen, Liang and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.449",
doi = "10.18653/v1/2021.findings-acl.449",
pages = "5062--5074",
```
## Contributions
Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.
|
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almanach/hc3_french_ood
|
almanach
| 2023-06-05T10:19:19Z | 70 | 1 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"license:cc-by-sa-4.0",
"ChatGPT",
"Bing",
"LM Detection",
"Detection",
"OOD",
"arxiv:2301.07597",
"region:us"
] |
[
"text-classification",
"question-answering",
"sentence-similarity",
"zero-shot-classification"
] | 2023-05-30T14:16:14Z |
---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- fr
size_categories:
- 10K<n<100K
tags:
- ChatGPT
- Bing
- LM Detection
- Detection
- OOD
license: cc-by-sa-4.0
---
Dataset card for the dataset used in :
## Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?
Paper: https://gitlab.inria.fr/wantoun/robust-chatgpt-detection/-/raw/main/towards_chatgpt_detection.pdf
Source Code: https://gitlab.inria.fr/wantoun/robust-chatgpt-detection
## Dataset Summary
#### overview:
This dataset is made of two parts:
- First, an extension of the [Human ChatGPT Comparison Corpus (HC3) dataset](https://huggingface.co/datasets/Hello-SimpleAI/HC3) with French data automatically translated from the English source.
- Second, out-of-domain and adversarial French data set have been gathereed (Human adversarial, BingGPT, Native French ChatGPT responses).
#### Details:
- We first format the data into three subsets: `sentence`, `question` and `full` following the original paper.
- We then extend the data by translating the English questions and answers to French.
- We provide native French ChatGPT responses to a sample of the translated questions.
- We added a subset with QA pairs from BingGPT
- We included an adversarial subset with human-written answers in the style of conversational LLMs like Bing/ChatGPT.
## Available Subsets
### Out-of-domain:
- `hc3_fr_qa_chatgpt`: Translated French questions and native French ChatGPT answers pairs from HC3. This is the `ChatGPT-Native` subset from the paper.
- Features: `id`, `question`, `answer`, `chatgpt_answer`, `label`, `source`
- Size:
- test: `113` examples, `25592` words
- `qa_fr_binggpt`: French questions and BingGPT answers pairs. This is the `BingGPT` subset from the paper.
- Features: `id`, `question`, `answer`, `label`, `deleted_clues`, `deleted_sources`, `remarks`
- Size:
- test: `106` examples, `26291` words
- `qa_fr_binglikehuman`: French questions and human written BingGPT-like answers pairs. This is the `Adversarial` subset from the paper.
- Features: `id`, `question`, `answer`, `label`, `source`
- Size:
- test: `61` examples, `17328` words
- `faq_fr_gouv`: French FAQ questions and answers pairs from domain ending with `.gouv` from the MQA dataset (subset 'fr-faq-page'). https://huggingface.co/datasets/clips/mqa. This is the `FAQ-Gouv` subset from the paper.
- Features: `id`, `page_id`, `question_id`, `answer_id`, `bucket`, `domain`, `question`, `answer`, `label`
- Size:
- test: `235` examples, `22336` words
- `faq_fr_random`: French FAQ questions and answers pairs from random domain from the MQA dataset (subset 'fr-faq-page'). https://huggingface.co/datasets/clips/mqa. This is the `FAQ-Rand` subset from the paper.
- Features: `id`, `page_id`, `question_id`, `answer_id`, `bucket`, `domain`, `question`, `answer`, `label`
- Size:
- test: `4454` examples, `271823` words
### In-domain:
- `hc3_en_qa`: English questions and answers pairs from HC3.
- Features: `id`, `question`, `answer`, `label`, `source`
- Size:
- train: `68335` examples, `12306363` words
- validation: `17114` examples, `3089634` words
- test: `710` examples, `117001` words
- `hc3_en_sentence`: English answers split into sentences from HC3.
- Features: `id`, `text`, `label`, `source`
- Size:
- train: `455320` examples, `9983784` words
- validation: `113830` examples, `2510290` words
- test: `4366` examples, `99965` words
- `hc3_en_full`: English questions and answers pairs concatenated from HC3.
- Features: `id`, `text`, `label`, `source`
- Size:
- train: `68335` examples, `9982863` words
- validation: `17114` examples, `2510058` words
- test: `710` examples, `99926` words
- `hc3_fr_qa`: Translated French questions and answers pairs from HC3.
- Features: `id`, `question`, `answer`, `label`, `source`
- Size:
- train: `68283` examples, `12660717` words
- validation: `17107` examples, `3179128` words
- test: `710` examples, `127193` words
- `hc3_fr_sentence`: Translated French answers split into sentences from HC3.
- Features: `id`, `text`, `label`, `source`
- Size:
- train: `464885` examples, `10189606` words
- validation: `116524` examples, `2563258` words
- test: `4366` examples, `108374` words
- `hc3_fr_full`: Translated French questions and answers pairs concatenated from HC3.
- Features: `id`, `text`, `label`, `source`
- Size:
- train: `68283` examples, `10188669` words
- validation: `17107` examples, `2563037` words
- test: `710` examples, `108352` words
## How to load
```python
from datasets import load_dataset
dataset = load_dataset("almanach/hc3_multi", "hc3_fr_qa")
```
## Dataset Copyright
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same.
If not, they follow CC-BY-SA license.
| English Split | Source | Source License | Note |
|----------|-------------|--------|-------------|
| reddit_eli5 | [ELI5](https://github.com/facebookresearch/ELI5) | BSD License | |
| open_qa | [WikiQA](https://www.microsoft.com/en-us/download/details.aspx?id=52419) | [PWC Custom](https://paperswithcode.com/datasets/license) | |
| wiki_csai | Wikipedia | CC-BY-SA | | [Wiki FAQ](https://en.wikipedia.org/wiki/Wikipedia:FAQ/Copyright) |
| medicine | [Medical Dialog](https://github.com/UCSD-AI4H/Medical-Dialogue-System) | Unknown| [Asking](https://github.com/UCSD-AI4H/Medical-Dialogue-System/issues/10)|
| finance | [FiQA](https://paperswithcode.com/dataset/fiqa-1) | Unknown | Asking by 📧 |
| FAQ | [MQA]( https://huggingface.co/datasets/clips/mqa) | CC0 1.0| |
| ChatGPT/BingGPT | | Unknown | This is ChatGPT/BingGPT generated data. |
| Human | | CC-BY-SA | |
## Citation
```bibtex
@proceedings{towards-a-robust-2023-antoun,
title = "Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?",
editor = "Antoun, Wissam and
Mouilleron, Virginie and
Sagot, Benoit and
Seddah, Djam{\'e}",
month = "6",
year = "2023",
address = "Paris, France",
publisher = "ATALA",
url = "https://gitlab.inria.fr/wantoun/robust-chatgpt-detection/-/raw/main/towards_chatgpt_detection.pdf",
}
```
```bibtex
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
url ="https://arxiv.org/abs/2301.07597"
}
```
|
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] |
rombodawg/LimitlessCodeTraining_Guanaco_Format
|
rombodawg
| 2023-09-29T04:23:00Z | 70 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-09-10T06:05:33Z |
---
license: mit
---
This is the LimitlessCodeTraining dataset converted to guanaco format. Enjoy
Original model card:
_________________
----- BREAK THROUGH YOUR LIMITS -----
_________________
LimitlessCodeTraining is the direct sequal to Megacodetraining that is now called Legacy_MegaCodeTraining200k.
This dataset is just over 646k lines of pure refined coding data.
It is the pinacle of open source code training. It is the combination of the filtered Megacode training dataset filtered by shahules786 (shoutout to him) and the bigcode commitpackft dataset I converted to alpaca format.
The dataset that were used to create this dataset are linked bellow:
- https://huggingface.co/datasets/rombodawg/Rombodawgs_commitpackft_Evolinstruct_Converted
- https://huggingface.co/datasets/shahules786/megacode-best
|
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open-llm-leaderboard/details_wei123602__Llama-2-13b-FINETUNE4_TEST
|
open-llm-leaderboard
| 2023-10-25T04:35:49Z | 70 | 0 |
[
"region:us"
] | null | 2023-09-21T23:18:19Z |
---
pretty_name: Evaluation run of wei123602/Llama-2-13b-FINETUNE4_TEST
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [wei123602/Llama-2-13b-FINETUNE4_TEST](https://huggingface.co/wei123602/Llama-2-13b-FINETUNE4_TEST)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__Llama-2-13b-FINETUNE4_TEST\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T04:35:36.269188](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__Llama-2-13b-FINETUNE4_TEST/blob/main/results_2023-10-25T04-35-36.269188.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2558724832214765,\n\
\ \"em_stderr\": 0.004468637497676013,\n \"f1\": 0.29727348993288566,\n\
\ \"f1_stderr\": 0.0043971826108447475,\n \"acc\": 0.4511208594202994,\n\
\ \"acc_stderr\": 0.010571455427847876\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2558724832214765,\n \"em_stderr\": 0.004468637497676013,\n\
\ \"f1\": 0.29727348993288566,\n \"f1_stderr\": 0.0043971826108447475\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13191811978771797,\n \
\ \"acc_stderr\": 0.009321265253857515\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838234\n\
\ }\n}\n```"
repo_url: https://huggingface.co/wei123602/Llama-2-13b-FINETUNE4_TEST
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|arc:challenge|25_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T04_35_36.269188
path:
- '**/details_harness|drop|3_2023-10-25T04-35-36.269188.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T04-35-36.269188.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T04_35_36.269188
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-35-36.269188.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-35-36.269188.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hellaswag|10_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-17-56.003321.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T23-17-56.003321.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T23-17-56.003321.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T04_35_36.269188
path:
- '**/details_harness|winogrande|5_2023-10-25T04-35-36.269188.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T04-35-36.269188.parquet'
- config_name: results
data_files:
- split: 2023_09_21T23_17_56.003321
path:
- results_2023-09-21T23-17-56.003321.parquet
- split: 2023_10_25T04_35_36.269188
path:
- results_2023-10-25T04-35-36.269188.parquet
- split: latest
path:
- results_2023-10-25T04-35-36.269188.parquet
---
# Dataset Card for Evaluation run of wei123602/Llama-2-13b-FINETUNE4_TEST
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/wei123602/Llama-2-13b-FINETUNE4_TEST
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [wei123602/Llama-2-13b-FINETUNE4_TEST](https://huggingface.co/wei123602/Llama-2-13b-FINETUNE4_TEST) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_wei123602__Llama-2-13b-FINETUNE4_TEST",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T04:35:36.269188](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__Llama-2-13b-FINETUNE4_TEST/blob/main/results_2023-10-25T04-35-36.269188.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2558724832214765,
"em_stderr": 0.004468637497676013,
"f1": 0.29727348993288566,
"f1_stderr": 0.0043971826108447475,
"acc": 0.4511208594202994,
"acc_stderr": 0.010571455427847876
},
"harness|drop|3": {
"em": 0.2558724832214765,
"em_stderr": 0.004468637497676013,
"f1": 0.29727348993288566,
"f1_stderr": 0.0043971826108447475
},
"harness|gsm8k|5": {
"acc": 0.13191811978771797,
"acc_stderr": 0.009321265253857515
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838234
}
}
```
### 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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NobodyExistsOnTheInternet/SillyJSON
|
NobodyExistsOnTheInternet
| 2023-10-17T11:45:58Z | 70 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-17T10:51:09Z |
---
license: mit
---
|
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] |
sade-adrien/context_extension-mistral-7k
|
sade-adrien
| 2023-11-08T20:21:36Z | 70 | 0 |
[
"region:us"
] | null | 2023-11-08T20:20:45Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: label
sequence: int64
splits:
- name: train
num_bytes: 1627254691
num_examples: 8774
- name: val
num_bytes: 176513698
num_examples: 975
download_size: 782669168
dataset_size: 1803768389
---
# Dataset Card for "context_extension-mistral-7k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
justinphan3110/repe_emotions_function_llama2_chat
|
justinphan3110
| 2023-11-09T21:33:54Z | 70 | 0 |
[
"region:us"
] | null | 2023-11-09T21:33:49Z |
---
dataset_info:
features:
- name: sentence
sequence: string
- name: label
sequence: bool
splits:
- name: happiness
num_bytes: 82983
num_examples: 582
- name: sadness
num_bytes: 83172
num_examples: 582
- name: anger
num_bytes: 82272
num_examples: 582
- name: fear
num_bytes: 82870
num_examples: 582
- name: disgust
num_bytes: 83999
num_examples: 582
- name: surprise
num_bytes: 84882
num_examples: 582
download_size: 96046
dataset_size: 500178
---
# Dataset Card for "repe_emotions_function_llama2_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mjphayes/machine_learning_questions
|
mjphayes
| 2023-11-12T11:52:33Z | 70 | 1 |
[
"region:us"
] | null | 2023-11-12T11:42:42Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 120983.07547169812
num_examples: 508
- name: test
num_bytes: 30483.924528301886
num_examples: 128
download_size: 85722
dataset_size: 151467.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "machine_learning_questions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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mlabonne/mini-platypus
|
mlabonne
| 2023-11-17T16:39:56Z | 70 | 0 |
[
"region:us"
] | null | 2023-11-16T19:26:36Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4201526
num_examples: 1000
download_size: 2247083
dataset_size: 4201526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
thanaphatt1/LongAlpaca-12k-th
|
thanaphatt1
| 2023-11-22T10:03:09Z | 70 | 2 |
[
"region:us"
] | null | 2023-11-17T12:19:28Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1171826005
num_examples: 11908
download_size: 434360238
dataset_size: 1171826005
---
# Dataset Card for "LongAlpaca-12k-th"
Thai-translated version of https://huggingface.co/datasets/Yukang/LongAlpaca-12k
Translated by Google translate
|
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] |
florentgbelidji/oa_german
|
florentgbelidji
| 2023-11-22T17:50:06Z | 70 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-22T17:49:19Z |
---
license: apache-2.0
dataset_info:
features:
- name: conversation_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: lang_original
dtype: string
- name: could_be_code
dtype: bool
splits:
- name: train_english
num_bytes: 29675151
num_examples: 18192
- name: train_german
num_bytes: 28931906
num_examples: 18192
download_size: 21854409
dataset_size: 58607057
configs:
- config_name: default
data_files:
- split: train_english
path: data/train_english-*
- split: train_german
path: data/train_german-*
---
|
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mvarma/medwiki
|
mvarma
| 2022-10-25T09:51:06Z | 69 | 4 |
[
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"license:cc-by-4.0",
"arxiv:2110.08228",
"region:us"
] |
[
"text-retrieval"
] | 2022-03-02T23:29:22Z |
---
YAML tags:
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en-US
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: medwiki
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
---
# Dataset Card for MedWiki
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github](https://github.com/HazyResearch/medical-ned-integration)
- **Paper:** [Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text](https://arxiv.org/abs/2110.08228)
- **Point of Contact:** [Maya Varma](mailto:[email protected])
### Dataset Summary
MedWiki is a large sentence dataset collected from a medically-relevant subset of Wikipedia and annotated with biomedical entities in the Unified Medical Language System (UMLS) knowledge base. For each entity, we include a rich set of types sourced from both UMLS and WikiData. Consisting of over 13 million sentences and 17 million entity annotations, MedWiki can be utilized as a pretraining resource for language models and can improve performance of medical named entity recognition and disambiguation systems, especially on rare entities.
Here, we include two configurations of MedWiki (further details in [Dataset Creation](#dataset-creation)):
- `MedWiki-Full` is a large sentence dataset with UMLS medical entity annotations generated through the following two steps: (1) a weak labeling proecedure to annotate WikiData entities in sentences and (2) a data integration approach that maps WikiData entities to their counterparts in UMLS.
- `MedWiki-HQ` is a subset of MedWiki-Full with higher quality labels designed to limit noise that arises from the annotation procedure listed above.
### Languages
The text in the dataset is in English and was obtained from English Wikipedia.
## Dataset Structure
### Data Instances
A typical data point includes a sentence collected from Wikipedia annotated with UMLS medical entities and associated titles and types.
An example from the MedWiki test set looks as follows:
```
{'sent_idx_unq': 57000409,
'sentence': "The hair , teeth , and skeletal side effects of TDO are lifelong , and treatment is used to manage those effects .",
'mentions': ['tdo'],
'entities': ['C2931236'],
'entity_titles': ['Tricho-dento-osseous syndrome 1'],
'types': [['Disease or Syndrome', 'disease', 'rare disease', 'developmental defect during embryogenesis', 'malformation syndrome with odontal and/or periodontal component', 'primary bone dysplasia with increased bone density', 'syndromic hair shaft abnormality']],
'spans': [[10, 11]]}
```
### Data Fields
- `sent_idx_unq`: a unique integer identifier for the data instance
- `sentence`: a string sentence collected from English Wikipedia. Punctuation is separated from words, and the sentence can be tokenized into word-pieces with the .split() method.
- `mentions`: list of medical mentions in the sentence.
- `entities`: list of UMLS medical entity identifiers corresponding to mentions. There is exactly one entity for each mention, and the length of the `entities` list is equal to the length of the `mentions` list.
- `entity_titles`: List of English titles collected from UMLS that describe each entity. The length of the `entity_titles` list is equal to the length of the `entities` list.
- `types`: List of category types associated with each entity, including types collected from UMLS and WikiData.
- `spans`: List of integer pairs representing the word span of each mention in the sentence.
### Data Splits
MedWiki includes two configurations: MedWiki-Full and MedWiki-HQ (described further in [Dataset Creation](#dataset-creation)). For each configuration, data is split into training, development, and test sets. The split sizes are as follow:
| | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| MedWiki-Full Sentences |11,784,235 | 649,132 | 648,608 |
| MedWiki-Full Mentions |15,981,347 | 876,586 | 877,090 |
| MedWiki-Full Unique Entities | 230,871 | 55,002 | 54,772 |
| MedWiki-HQ Sentences | 2,962,089 | 165,941 | 164,193 |
| MedWiki-HQ Mentions | 3,366,108 | 188,957 | 186,622 |
| MedWiki-HQ Unique Entities | 118,572 | 19,725 | 19,437 |
## Dataset Creation
### Curation Rationale
Existing medical text datasets are generally limited in scope, often obtaining low coverage over the entities and structural resources in the UMLS medical knowledge base. When language models are trained across such datasets, the lack of adequate examples may prevent models from learning the complex reasoning patterns that are necessary for performing effective entity linking or disambiguation, especially for rare entities as shown in prior work by [Orr et al.](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf). Wikipedia, which is often utilized as a rich knowledge source in general text settings, contains references to medical terms and can help address this issue. Here, we curate the MedWiki dataset, which is a large-scale, weakly-labeled dataset that consists of sentences from Wikipedia annotated with medical entities in the UMLS knowledge base. MedWiki can serve as a pretraining dataset for language models and holds potential for improving performance on medical named entity recognition tasks, especially on rare entities.
### Source Data
#### Initial Data Collection and Normalization
MedWiki consists of sentences obtained from the November 2019 dump of English Wikipedia. We split pages into an 80/10/10 train/dev/test split and then segment each page at the sentence-level. This ensures that all sentences associated with a single Wikipedia page are placed in the same split.
#### Who are the source language producers?
The source language producers are editors on English Wikipedia.
### Annotations
#### Annotation process
We create two configurations of our dataset: MedWiki-Full and MedWiki-HQ. We label MedWiki-Full by first annotating all English Wikipedia articles with textual mentions and corresponding WikiData entities; we do so by obtaining gold entity labels from internal page links as well as generating weak labels based on pronouns and alternative entity names (see [Orr et al. 2020](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf) for additional information). Then, we use the off-the-shelf entity linker [Bootleg](https://github.com/HazyResearch/bootleg) to map entities in WikiData to their counterparts in the 2017AA release of the Unified Medical Language System (UMLS), a standard knowledge base for biomedical entities (additional implementation details in forthcoming publication). Any sentence containing at least one UMLS entity is included in MedWiki-Full. We also include types associated with each entity, which are collected from both WikiData and UMLS using the generated UMLS-Wikidata mapping. It is important to note that types obtained from WikiData are filtered according to methods described in [Orr et al. 2020](http://cidrdb.org/cidr2021/papers/cidr2021_paper13.pdf).
Since our labeling procedure introduces some noise into annotations, we also release the MedWiki-HQ dataset configuration with higher-quality labels. To generate MedWiki-HQ, we filtered the UMLS-Wikidata mappings to only include pairs of UMLS medical entities and WikiData items that share a high textual overlap between titles. MedWiki-HQ is a subset of MedWiki-Full.
To evaluate the quality of our UMLS-Wikidata mappings, we find that WikiData includes a small set of "true" labeled mappings between UMLS entities and WikiData items. (Note that we only include WikiData items associated with linked Wikipedia pages.) This set comprises approximately 9.3k UMLS entities in the original UMLS-Wikidata mapping (used for MedWiki-Full) and 5.6k entities in the filtered UMLS-Wikidata mapping (used for MedWiki-HQ). Using these labeled sets, we find that our mapping accuracy is 80.2% for the original UMLS-Wikidata mapping and 94.5% for the filtered UMLS-Wikidata mapping. We also evaluate integration performance on this segment as the proportion of mapped WikiData entities that share a WikiData type with the true entity, suggesting the predicted mapping adds relevant structural resources. Integration performance is 85.4% for the original UMLS-Wikidata mapping and 95.9% for the filtered UMLS-Wikidata mapping. The remainder of items in UMLS have no “true” mappings to WikiData.
#### Who are the annotators?
The dataset was labeled using weak-labeling techniques as described above.
### Personal and Sensitive Information
No personal or sensitive information is included in MedWiki.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to enable the creation of better named entity recognition systems for biomedical text. MedWiki encompasses a large set of entities in the UMLS knowledge base and includes a rich set of types associated with each entity, which can enable the creation of models that achieve high performance on named entity recognition tasks, especially on rare or unpopular entities. Such systems hold potential for improving automated parsing and information retrieval from large quantities of biomedical text.
### Discussion of Biases
The data included in MedWiki comes from English Wikipedia. Generally, Wikipedia articles are neutral in point of view and aim to avoid bias. However, some [prior work](https://www.hbs.edu/ris/Publication%20Files/15-023_e044cf50-f621-4759-a827-e9a3bf8920c0.pdf) has shown that ideological biases may exist within some Wikipedia articles, especially those that are focused on political issues or those that are written by fewer authors. We anticipate that such biases are rare for medical articles, which are typically comprised of scientific facts. However, it is important to note that bias encoded in Wikipedia is likely to be reflected by MedWiki.
### Other Known Limitations
Since MedWiki was annotated using weak labeling techniques, there is likely some noise in entity annotations. (Note that to address this, we include the MedWiki-HQ configuration, which is a subset of MedWiki-Full with higher quality labels. Additional details in [Dataset Creation](#dataset-creation)).
## Additional Information
### Dataset Curators
MedWiki was curated by Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, and Chris Ré.
### Licensing Information
Dataset licensed under CC BY 4.0.
### Citation Information
```
@inproceedings{varma-etal-2021-cross-domain,
title = "Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text",
author = "Varma, Maya and
Orr, Laurel and
Wu, Sen and
Leszczynski, Megan and
Ling, Xiao and
R{\'e}, Christopher",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.388",
pages = "4566--4575",
}
```
### Contributions
Thanks to [@maya124](https://github.com/maya124) for adding this dataset.
|
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HuggingFaceM4/charades
|
HuggingFaceM4
| 2022-10-20T21:35:42Z | 69 | 2 |
[
"task_categories:other",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1604.01753",
"region:us"
] |
[
"other"
] | 2022-05-11T07:07:47Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: charades
pretty_name: Charades
tags: []
---
# Dataset Card for Charades
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://prior.allenai.org/projects/charades
- **Repository:** https://github.com/gsig/charades-algorithms
- **Paper:** https://arxiv.org/abs/1604.01753
- **Leaderboard:** https://paperswithcode.com/sota/action-classification-on-charades
- **Point of Contact:** mailto: [email protected]
### Dataset Summary
Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos
### Supported Tasks and Leaderboards
- `multilabel-action-classification`: The goal of this task is to classify actions happening in a video. This is a multilabel classification. The leaderboard is available [here](https://paperswithcode.com/sota/action-classification-on-charades)
### Languages
The annotations in the dataset are in English.
## Dataset Structure
### Data Instances
```
{
"video_id": "46GP8",
"video": "/home/amanpreet_huggingface_co/.cache/huggingface/datasets/downloads/extracted/3f022da5305aaa189f09476dbf7d5e02f6fe12766b927c076707360d00deb44d/46GP8.mp4",
"subject": "HR43",
"scene": "Kitchen",
"quality": 6,
"relevance": 7,
"verified": "Yes",
"script": "A person cooking on a stove while watching something out a window.",
"objects": ["food", "stove", "window"],
"descriptions": [
"A person cooks food on a stove before looking out of a window."
],
"labels": [92, 147],
"action_timings": [
[11.899999618530273, 21.200000762939453],
[0.0, 12.600000381469727]
],
"length": 24.829999923706055
}
```
### Data Fields
- `video_id`: `str` Unique identifier for each video.
- `video`: `str` Path to the video file
- `subject`: `str` Unique identifier for each subject in the dataset
- `scene`: `str` One of 15 indoor scenes in the dataset, such as Kitchen
- `quality`: `int` The quality of the video judged by an annotator (7-point scale, 7=high quality), -100 if missing
- `relevance`: `int` The relevance of the video to the script judged by an annotated (7-point scale, 7=very relevant), -100 if missing
- `verified`: `str` 'Yes' if an annotator successfully verified that the video matches the script, else 'No'
- `script`: `str` The human-generated script used to generate the video
- `descriptions`: `List[str]` List of descriptions by annotators watching the video
- `labels`: `List[int]` Multi-label actions found in the video. Indices from 0 to 156.
- `action_timings`: `List[Tuple[int, int]]` Timing where each of the above actions happened.
- `length`: `float` The length of the video in seconds
<details>
<summary>
Click here to see the full list of Charades class labels mapping:
</summary>
|id|Class|
|--|-----|
|c000 | Holding some clothes |
|c001 | Putting clothes somewhere |
|c002 | Taking some clothes from somewhere |
|c003 | Throwing clothes somewhere |
|c004 | Tidying some clothes |
|c005 | Washing some clothes |
|c006 | Closing a door |
|c007 | Fixing a door |
|c008 | Opening a door |
|c009 | Putting something on a table |
|c010 | Sitting on a table |
|c011 | Sitting at a table |
|c012 | Tidying up a table |
|c013 | Washing a table |
|c014 | Working at a table |
|c015 | Holding a phone/camera |
|c016 | Playing with a phone/camera |
|c017 | Putting a phone/camera somewhere |
|c018 | Taking a phone/camera from somewhere |
|c019 | Talking on a phone/camera |
|c020 | Holding a bag |
|c021 | Opening a bag |
|c022 | Putting a bag somewhere |
|c023 | Taking a bag from somewhere |
|c024 | Throwing a bag somewhere |
|c025 | Closing a book |
|c026 | Holding a book |
|c027 | Opening a book |
|c028 | Putting a book somewhere |
|c029 | Smiling at a book |
|c030 | Taking a book from somewhere |
|c031 | Throwing a book somewhere |
|c032 | Watching/Reading/Looking at a book |
|c033 | Holding a towel/s |
|c034 | Putting a towel/s somewhere |
|c035 | Taking a towel/s from somewhere |
|c036 | Throwing a towel/s somewhere |
|c037 | Tidying up a towel/s |
|c038 | Washing something with a towel |
|c039 | Closing a box |
|c040 | Holding a box |
|c041 | Opening a box |
|c042 | Putting a box somewhere |
|c043 | Taking a box from somewhere |
|c044 | Taking something from a box |
|c045 | Throwing a box somewhere |
|c046 | Closing a laptop |
|c047 | Holding a laptop |
|c048 | Opening a laptop |
|c049 | Putting a laptop somewhere |
|c050 | Taking a laptop from somewhere |
|c051 | Watching a laptop or something on a laptop |
|c052 | Working/Playing on a laptop |
|c053 | Holding a shoe/shoes |
|c054 | Putting shoes somewhere |
|c055 | Putting on shoe/shoes |
|c056 | Taking shoes from somewhere |
|c057 | Taking off some shoes |
|c058 | Throwing shoes somewhere |
|c059 | Sitting in a chair |
|c060 | Standing on a chair |
|c061 | Holding some food |
|c062 | Putting some food somewhere |
|c063 | Taking food from somewhere |
|c064 | Throwing food somewhere |
|c065 | Eating a sandwich |
|c066 | Making a sandwich |
|c067 | Holding a sandwich |
|c068 | Putting a sandwich somewhere |
|c069 | Taking a sandwich from somewhere |
|c070 | Holding a blanket |
|c071 | Putting a blanket somewhere |
|c072 | Snuggling with a blanket |
|c073 | Taking a blanket from somewhere |
|c074 | Throwing a blanket somewhere |
|c075 | Tidying up a blanket/s |
|c076 | Holding a pillow |
|c077 | Putting a pillow somewhere |
|c078 | Snuggling with a pillow |
|c079 | Taking a pillow from somewhere |
|c080 | Throwing a pillow somewhere |
|c081 | Putting something on a shelf |
|c082 | Tidying a shelf or something on a shelf |
|c083 | Reaching for and grabbing a picture |
|c084 | Holding a picture |
|c085 | Laughing at a picture |
|c086 | Putting a picture somewhere |
|c087 | Taking a picture of something |
|c088 | Watching/looking at a picture |
|c089 | Closing a window |
|c090 | Opening a window |
|c091 | Washing a window |
|c092 | Watching/Looking outside of a window |
|c093 | Holding a mirror |
|c094 | Smiling in a mirror |
|c095 | Washing a mirror |
|c096 | Watching something/someone/themselves in a mirror |
|c097 | Walking through a doorway |
|c098 | Holding a broom |
|c099 | Putting a broom somewhere |
|c100 | Taking a broom from somewhere |
|c101 | Throwing a broom somewhere |
|c102 | Tidying up with a broom |
|c103 | Fixing a light |
|c104 | Turning on a light |
|c105 | Turning off a light |
|c106 | Drinking from a cup/glass/bottle |
|c107 | Holding a cup/glass/bottle of something |
|c108 | Pouring something into a cup/glass/bottle |
|c109 | Putting a cup/glass/bottle somewhere |
|c110 | Taking a cup/glass/bottle from somewhere |
|c111 | Washing a cup/glass/bottle |
|c112 | Closing a closet/cabinet |
|c113 | Opening a closet/cabinet |
|c114 | Tidying up a closet/cabinet |
|c115 | Someone is holding a paper/notebook |
|c116 | Putting their paper/notebook somewhere |
|c117 | Taking paper/notebook from somewhere |
|c118 | Holding a dish |
|c119 | Putting a dish/es somewhere |
|c120 | Taking a dish/es from somewhere |
|c121 | Wash a dish/dishes |
|c122 | Lying on a sofa/couch |
|c123 | Sitting on sofa/couch |
|c124 | Lying on the floor |
|c125 | Sitting on the floor |
|c126 | Throwing something on the floor |
|c127 | Tidying something on the floor |
|c128 | Holding some medicine |
|c129 | Taking/consuming some medicine |
|c130 | Putting groceries somewhere |
|c131 | Laughing at television |
|c132 | Watching television |
|c133 | Someone is awakening in bed |
|c134 | Lying on a bed |
|c135 | Sitting in a bed |
|c136 | Fixing a vacuum |
|c137 | Holding a vacuum |
|c138 | Taking a vacuum from somewhere |
|c139 | Washing their hands |
|c140 | Fixing a doorknob |
|c141 | Grasping onto a doorknob |
|c142 | Closing a refrigerator |
|c143 | Opening a refrigerator |
|c144 | Fixing their hair |
|c145 | Working on paper/notebook |
|c146 | Someone is awakening somewhere |
|c147 | Someone is cooking something |
|c148 | Someone is dressing |
|c149 | Someone is laughing |
|c150 | Someone is running somewhere |
|c151 | Someone is going from standing to sitting |
|c152 | Someone is smiling |
|c153 | Someone is sneezing |
|c154 | Someone is standing up from somewhere |
|c155 | Someone is undressing |
|c156 | Someone is eating something |
</details>
### Data Splits
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000 |100000 |
## Dataset Creation
### Curation Rationale
> Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are not particularly exciting, they typically do not appear on YouTube, in movies or TV broadcasts. So how do we collect sufficiently many diverse but boring samples representing our lives? We propose a novel Hollywood in Homes approach to collect such data. Instead of shooting videos in the lab, we ensure diversity by distributing and crowdsourcing the whole process of video creation from script writing to video recording and annotation.
### Source Data
#### Initial Data Collection and Normalization
> Similar to filming, we have a three-step process for generating a video. The first step is generating the script of the indoor video. The key here is to allow workers to generate diverse scripts yet ensure that we have enough data for each category. The second step in the process is to use the script and ask workers to record a video of that sentence being acted out. In the final step, we ask the workers to verify if the recorded video corresponds to script, followed by an annotation procedure.
#### Who are the source language producers?
Amazon Mechnical Turk annotators
### Annotations
#### Annotation process
> Similar to filming, we have a three-step process for generating a video. The first step is generating the script of the indoor video. The key here is to allow workers to generate diverse scripts yet ensure that we have enough data for each category. The second step in the process is to use the script and ask workers to record a video of that sentence being acted out. In the final step, we ask the workers to verify if the recorded video corresponds to script, followed by an annotation procedure.
#### Who are the annotators?
Amazon Mechnical Turk annotators
### Personal and Sensitive Information
Nothing specifically mentioned in the paper.
## 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
AMT annotators
### Licensing Information
License for Non-Commercial Use
If this software is redistributed, this license must be included. The term software includes any source files, documentation, executables, models, and data.
This software and data is available for general use by academic or non-profit, or government-sponsored researchers. It may also be used for evaluation purposes elsewhere. This license does not grant the right to use this software or any derivation of it in a for-profit enterprise. For commercial use, please contact The Allen Institute for Artificial Intelligence.
This license does not grant the right to modify and publicly release the data in any form.
This license does not grant the right to distribute the data to a third party in any form.
The subjects in this data should be treated with respect and dignity. This license only grants the right to publish short segments or still images in an academic publication where necessary to present examples, experimental results, or observations.
This software comes with no warranty or guarantee of any kind. By using this software, the user accepts full liability.
The Allen Institute for Artificial Intelligence (C) 2016.
### Citation Information
```bibtex
@article{sigurdsson2016hollywood,
author = {Gunnar A. Sigurdsson and G{\"u}l Varol and Xiaolong Wang and Ivan Laptev and Ali Farhadi and Abhinav Gupta},
title = {Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding},
journal = {ArXiv e-prints},
eprint = {1604.01753},
year = {2016},
url = {http://arxiv.org/abs/1604.01753},
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.
|
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breakend/nllb-multi-domain
|
breakend
| 2022-08-09T20:44:23Z | 69 | 1 |
[
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|flores",
"language:en",
"language:ru",
"language:ayr",
"language:bho",
"language:dyu",
"language:fur",
"language:wol",
"license:cc-by-sa-4.0",
"arxiv:2207.04672",
"region:us"
] |
[
"conditional-text-generation"
] | 2022-07-18T23:01:53Z |
---
language:
- en
- ru
- ayr
- bho
- dyu
- fur
- wol
annotations_creators:
- found
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
- translation
pretty_name: nllb-multi-domain
size_categories:
- unknown
source_datasets:
- extended|flores
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
paperswithcode_id: flores
---
# Dataset Card for NLLB Multi-Domain
## Table of Contents
- [Dataset Card for NLLB Multi-Domain](#dataset-card-for-nllb-multi-domain)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [Flores](https://github.com/facebookresearch/flores/tree/main/nllb_md)
- **Repository:** [Github](https://github.com/facebookresearch/flores/tree/main/nllb_md)
### Dataset Summary
NLLB Multi Domain is a set of professionally-translated sentences in News, Unscripted informal speech, and Health domains. It is designed to enable assessment of out-of-domain performance and to study domain adaptation for machine translation. Each domain has approximately 3000 sentences.
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this.
### Languages
Language | FLORES-200 code
---|---
Central Aymara | ayr_Latn
Bhojpuri | bho_Deva
Dyula | dyu_Latn
Friulian | fur_Latn
Russian | rus_Cyrl
Wolof | wol_Latn
Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-rus_Cyrl" will provide sentences in the format below).
## Dataset Structure
### Data Instances
See Dataset Viewer.
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language (may have _lang for pairings)
- `domain`: The domain of the sentence.
### Dataset Creation
Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation.
## Additional Information
### Dataset Curators
See paper for details.
### Licensing Information
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@article{nllb2022,
author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},
title = {No Language Left Behind: Scaling Human-Centered Machine Translation},
year = {2022}
}
```
Please also cite prior work that this dataset builds on:
```bibtex
@inproceedings{,
title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
year={2021}
}
```
```bibtex
@inproceedings{,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Guzm\'{a}n, Francisco and Chen, Peng-Jen and Ott, Myle and Pino, Juan and Lample, Guillaume and Koehn, Philipp and Chaudhary, Vishrav and Ranzato, Marc'Aurelio},
journal={arXiv preprint arXiv:1902.01382},
year={2019}
}
```
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Multimodal-Fatima/FGVC_Aircraft_train
|
Multimodal-Fatima
| 2023-05-04T05:30:31Z | 69 | 0 |
[
"region:us"
] | null | 2022-11-13T05:05:42Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: family
dtype:
class_label:
names:
'0': A300
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class_label:
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- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
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sequence: string
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dtype: string
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sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_fgvc
sequence: string
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
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dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
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dtype: string
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dtype: string
splits:
- name: train
num_bytes: 931613762.0
num_examples: 3334
download_size: 925638163
dataset_size: 931613762.0
---
# Dataset Card for "FGVC_Aircraft_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bigbio/osiris
|
bigbio
| 2022-12-22T15:46:10Z | 69 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | 2022-11-13T22:11:10Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_3p0
pretty_name: OSIRIS
homepage: https://sites.google.com/site/laurafurlongweb/databases-and-tools/corpora/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for OSIRIS
## Dataset Description
- **Homepage:** https://sites.google.com/site/laurafurlongweb/databases-and-tools/corpora/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
The OSIRIS corpus is a set of MEDLINE abstracts manually annotated
with human variation mentions. The corpus is distributed under the terms
of the Creative Commons Attribution License
Creative Commons Attribution 3.0 Unported License,
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited (Furlong et al, BMC Bioinformatics 2008, 9:84).
## Citation Information
```
@ARTICLE{Furlong2008,
author = {Laura I Furlong and Holger Dach and Martin Hofmann-Apitius and Ferran Sanz},
title = {OSIRISv1.2: a named entity recognition system for sequence variants
of genes in biomedical literature.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {84},
doi = {10.1186/1471-2105-9-84},
pii = {1471-2105-9-84},
pmid = {18251998},
timestamp = {2013.01.15},
url = {http://dx.doi.org/10.1186/1471-2105-9-84}
}
```
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HuggingFaceH4/helpful-instructions
|
HuggingFaceH4
| 2023-02-20T08:58:24Z | 69 | 5 |
[
"license:apache-2.0",
"human-feedback",
"region:us"
] | null | 2023-02-16T09:12:16Z |
---
license: apache-2.0
tags:
- human-feedback
pretty_name: Helpful Instructions
---
# Dataset Card for Helpful Instructions
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Lewis Tunstall**
### Dataset Summary
Helpful Instructions is a dataset of `(instruction, demonstration)` pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
```python
from datasets import load_dataset
# Load all subsets
helpful_instructions = load_dataset("HuggingFaceH4/helpful_instructions")
# Load a single subset
helpful_instructions_subset = load_dataset("HuggingFaceH4/helpful_instructions", data_dir="data/helpful-anthropic-raw")
```
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Languages
English
## 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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] |
argilla/databricks-dolly-15k-es-deepl
|
argilla
| 2023-04-13T10:30:19Z | 69 | 0 |
[
"region:us"
] | null | 2023-04-13T10:30:14Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: instruction_en
dtype: string
- name: context_en
dtype: string
- name: response_en
dtype: string
splits:
- name: train
num_bytes: 25838910
num_examples: 15015
download_size: 16464221
dataset_size: 25838910
---
# Dataset Card for "databricks-dolly-15k-es-deepl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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roupenminassian/twitter-misinformation
|
roupenminassian
| 2023-04-20T06:17:32Z | 69 | 0 |
[
"task_categories:text-classification",
"region:us"
] |
[
"text-classification"
] | 2023-04-17T07:29:33Z |
---
task_categories:
- text-classification
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
Frorozcol/recetas-cocina
|
Frorozcol
| 2023-09-18T16:40:48Z | 69 | 1 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:es",
"license:mit",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-04-23T17:28:56Z |
---
license: mit
task_categories:
- text-generation
- conversational
language:
- es
pretty_name: recetas de cocina
size_categories:
- 1K<n<10K
---
## Resumen del dataset
Se trata de un dataset de recetas de comidas en español. Se hizo un scrapy de diferentes páginas de internet sobre recetas de comidas que estuvieran en español, se logró extraer alrededor de 30 k valores, que se dividen en train, test y valid.
## Tareas admitidas y tablas de clasificación
task-generation: Dado los ingredientes, generar la receta.
Idioma
Es un dataset que cuenta con un español de diferentes partes del mundo, especial de latino america
## Estructura de los datos
### Instancias
A continuación se muestra una instancia de ejemplo del dataset:
```json
{
'title': 'Smoothie bicolor de leche KLIM® y MILO®',
'url': "https://www.recetasnestle.com.co/recetas/smoothie-chocolate-leche-bicolor"
'ingredients ': "2 cucharadas de MILO® (25 g) 1 taza de hielo 3 cucharadas de Leche en polvo KLIM® Clásica (24 g)",
'steps': ' 1. Licúa las cucharadas de MILO® con media taza de hielo hasta que lo veas frapeado y pon la mezcla en un vaso, lleva al congelador mientras preparas la leche. 2. Aparte, en el mismo vaso de la licuadora añade la media taza de hielo restante y las cucharadas de leche en polvo KLIM® Clásica, licúa por 5 segundos hasta que lo veas frapeado. 3. Retira el vaso del congelador y sirve encima el licuado de la leche, así tendrás los dos colores, decora con fruta de tu preferencia.',
'uuid': 'ca4fa322-a38d-4f6a-8c06-79f68fe729f4.'
}
```
## Campos de los datos
+ title: Titulo de la receta
+ url: Url de donde se hizo el scrapy
+ ingredients: Los ingredientes para hacer la receta
+ steps: Los pasos para hacer la receta
+ uuid: Código del dataset.
|
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alpayariyak/LLaVA_calculus_handwriting
|
alpayariyak
| 2023-05-24T20:29:57Z | 69 | 3 |
[
"region:us"
] | null | 2023-05-24T18:47:22Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: string
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 9607911271.0
num_examples: 100000
download_size: 9289147010
dataset_size: 9607911271.0
---
# Dataset Card for "LLaVA_calculus_handwriting"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
KaiLv/UDR_ComE
|
KaiLv
| 2023-06-21T12:35:45Z | 69 | 0 |
[
"region:us"
] | null | 2023-06-21T12:35:33Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: string
- name: question
dtype: string
- name: choices
dtype: string
- name: len_question
dtype: int64
- name: max_len_choices
dtype: int64
splits:
- name: train
num_bytes: 4855852
num_examples: 9996
- name: test
num_bytes: 468814
num_examples: 1000
- name: debug
num_bytes: 2432484
num_examples: 5000
download_size: 3748196
dataset_size: 7757150
---
# Dataset Card for "UDR_ComE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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allenai/MADLAD-400
|
allenai
| 2023-10-31T20:47:56Z | 69 | 75 |
[
"task_categories:text-generation",
"size_categories:n>1T",
"license:odc-by",
"arxiv:2309.04662",
"arxiv:2010.14571",
"arxiv:2103.12028",
"region:us"
] |
[
"text-generation"
] | 2023-09-01T00:06:27Z |
---
license: odc-by
task_categories:
- text-generation
size_categories:
- n>1T
---
# MADLAD-400
## Dataset and Introduction
[MADLAD-400 (*Multilingual Audited Dataset: Low-resource And Document-level*)](https://arxiv.org/abs/2309.04662) is
a document-level multilingual dataset based on Common Crawl, covering 419
languages in total. This uses all snapshots of CommonCrawl available as of August
1, 2022. The primary advantage of this dataset over similar datasets is that it
is more multilingual (419 languages), it is audited and more highly filtered,
and it is document-level. The main disadvantage is also its strength -- being
more filtered, it may lack the recall needed for some applications.
There are two versions released: the **noisy** dataset, which has no filtering
except document-level LangID, and the **clean** dataset, which has a variety of
filters applied, though it naturally has a fair amount of noise itself. Each
dataset is released in a document-level form that has been deduplicated.
## Loading
You can load both the clean and noisy versions of any language by specifing its LangID:
~~~
madlad_abt = load_dataset("allenai/madlad-400", "abt")
~~~
A list of langagues can also be supplied with a keyword argument:
~~~
madlad_multilang = load_dataset("allenai/madlad-400", languages=["abt", "ace"])
~~~
Additionally, you can load the noisy and clean subsets seperately with the split keyword argument:
~~~
madlad_multilang_clean = load_dataset("allenai/madlad-400", languages=["abt", "ace"], split="clean")
~~~
## LangID model and Crawl
Following [Language Id In the Wild](https://arxiv.org/pdf/2010.14571.pdf), we
trained a Semi-Supervised LangId model (SSLID) on 500 languages. The training
data is as described in that paper, with the differences that 1) training data
is sampled to a temperature of `T=3` to reduce over-triggering on low-resource
languages; and 2) the data is supplemented with web-crawled data from the same
paper (that has already been through the various filters described therein) in
the hopes that it will increase robustness to web-domain text.
## Filtering
Before separating the raw CommonCrawl corpus by LangID, these
filtering steps are done, similar to Raffel et al (2020):
- Discarded any page with fewer than 5 sentences and only retained lines that
contained at least 3 words.
- Removed any line with the word Javascript.
- Removed any page where the phrase “lorem ipsum” appeared.
- Removed any pages containing the phrases "terms of use", "privacy policy",
"cookie policy", "uses cookies", "use of cookies", "use cookies"
- Removed any pages that contained a curly bracket.
- To deduplicate the data set, discarded all but one of any three-sentence span occurring more than once in the data set.
The `noisy` subset of the data was filtered only by document-level LangID, which
was taken to be the majority sentence-level LangID prediction. The `clean`
subset removed all documents with a `percent_questionable` score greater than
20%. It furthermore removed any document with under 5 sentences.
The `pct_questionable` score is simple the percentage of sentences in the input
document that were "questionable". A sentence was considered questionable if any
of the following were true:
* **LangID Consistency:** the sentence-level LangID does not match the
document-level LangID
* **List Case:** The sentence has at least 12 tokens, and over 50% percent of
the tokens began in a capital letter.
* **Length:** The sentence has under 20 characters or over 500 characters
(note: this is a bad heuristic for ideographic languages)
* **Danger Chars:** Over 20% of the characters in the sentence match
`[0-9{}+/()>]`
* **Cursedness:** The sentence matches a cursed regex (see below)
### Cursed Substrings
Based on the initial round of data audits, the authors created a heuristic list of
substrings and regexes accounting for a large amount of questionable content.
Keep in mind that these all are fed into the `pct_questionable` score -- a
sentence is only excluded from the `clean` dataset if over 20% of the sentences
in that document are flagged as questionable.
notes about cursed substrings:
* low quality sentences ending in the pipe character were very common. Before
you ask, this was not Devanagari-script text using a Danda.
* The last few regexes are meant to match `A N T S P E A K`, `List Case`, and
weirdly regular text (for instance, lists of shipping labels or country
codes)
```
# this implementation is for demonstration and is pretty inefficient;
# to speed it up, use string inclusion (`in`) instead of regex for all but the
# last four, and for those use a compiled regex.
def is_cursed(s):
return any(re.findall(curse, s) in s for curse in CURSED_SUBSTRINGS)
CURSED_SUBSTRINGS = [" №", "���", "\\|\\s*$", " nr\\.$", "aute irure dolor ", " sunt in culpa qui ", "orem ipsum ", " quis nostrud ", " adipisicing ", " dolore eu ", " cupidatat ", "autem vel eum", "wisi enim ad", " sex ", " porn ", "黄色电影", "mp3", "ownload", "Vol\\.", " Ep\\.", "Episode", " г\\.\\s*$", " кг\\.\\s*$", " шт\\.", "Develop", "Facebook", " crusher ", " xxx ", " ... ... ... ... ... ... ... ... ...", " .... .... .... .... .... .... .... .... ....", " [^ ] [^ ] [^ ] [^ ] [^ ] [^ ] [^ ] [^ ] [^ ]", ", ..,,? ..,,? ..,,? ..,,?"]
```
### Virama Correction
Many languages using Brahmic Abugida (South and Southeast Asian scripts like
Devanagari, Khmer, etc.) use some variant on the virama character. For whatever
reason, it was found that this character was often messed up in the common crawl
snapshots used. Therefore, for the languages `bn my pa gu or ta te kn ml
si th tl mn lo bo km hi mr ne gom as jv dv bho dz hne ks_Deva mag mni shn yue zh
ja kjg mnw ksw rki mtr mwr xnr`, a special correction step was done.
For these languages, the authors took the list of all virama characters and removed all
unnecessary spaces between each instance of a virama character and the next
character with a regex.
```
'%s' % regex.sub(r' ([%s]) ' % _VIRAMA_CHARS, '\\1', x)
```
### Myanmar Font Compatibility
Prior to 2019, the most popular font for Burmese websites was the Zawgyi font.
The authors used [Myanmar Tools](https://github.com/google/myanmar-tools) to convert text.
Several scripts, like the Chinese script, Tibetan script, and Thai, do not use
whitespace to separate characters. The languages with this property in this
dataset are `yue zh ja th lo kjg mnw my shn ksw rki km bo dz`.
Alas, the **Length** aspect of the `pct_questionable` score was calculated using
simplistic whitespace tokenization, and therefore rendered the whole
`pct_questionable` score invalid for those languages. Therefore, for these
languages, the "clean" data is identical to the "noisy" data (barring Chinese;
see below.)
### Special filters
Chinese had a particular issue with pornographic content. After manual inspection
a list of strings likely to be present in pornographic content was developed. All
pages containing at least one of these strings were removed. Resulted in 17%
reduction in number of documents and 56% reduction in file size.
```
pornsignals = "caoporn caoprom caopron caoporen caoponrn caoponav caopom caoorn 99re dy888 caopro hezyo re99 4438x zooskool xfplay 7tav xxoo xoxo 52av freexx 91chinese anquye cao97 538porm 87fuli 91pron 91porn 26uuu 4438x 182tv kk4444 777me ae86 91av 720lu yy6080 6080yy qqchub paa97 aiai777 yy4480 videossexo 91free 一级特黄大片 偷拍久久国产视频 日本毛片免费视频观看 久久免费热在线精品 高清毛片在线看 日本毛片高清免费视频 一级黄色录像影片 亚洲男人天堂 久久精品视频在线看 自拍区偷拍亚洲视频 亚洲人成视频在线播放 色姑娘综合站 丁香五月啪啪 在线视频成人社区 亚洲人成视频在线播放 久久国产自偷拍 一本道 大香蕉无码 香港经典三级 亚洲成在人线免费视频 天天色综合网 大香蕉伊人久草 欧美一级高清片 天天鲁夜夜啪视频在线 免费黄片视频在线观看 加比勒久久综合 久草热久草在线视频 韩国三级片大全在线观看 青青草在线视频 美国一级毛片 久草在线福利资源 啪啪啪视频在线观看免费 成人福利视频在线观看 婷婷我去也 老司机在线国产 久久成人视频 手机看片福利永久国产 高清国产偷拍在线 大香蕉在线影院 日本高清免费一本视频 男人的天堂东京热 影音先锋男人资源 五月婷婷开心中文字幕 亚洲香蕉视频在线播放 天天啪久久爱视频精品 超碰久久人人摸人人搞".split()
```
A few more random notes, comparing to common alternative codes for these
languages:
* `fil` for Filipino/Tagalog, not `tl`
* `ak` for Twi/Akan, rather than `tw`. This includes Fante.
* Unfortunately use the macro code `chm` for Meadow Mari (instead of the
correct `mhr`), and `mrj` for Hill Mari
* `no` for Norwegian Bokmål, whereas some resources use
`nb`
* `ps` for Pashto instead of `pbt` (Southern Pashto)
* `ms` for Standard Malay, not `zlm`
* `sq` for Albanian, and don't distinguish dialects like
Gheg (`aln`) and Tosk (`als`)
* `ber` as the code for Tamazight, after consultation with Tamazight
speakers opining that the dialect distinctions are not significant. Other
resources use the individual codes like `tzm` and `kab`.
* Macrocode `qu` for Quechua. In practice, this seems usually to be
a mix of the Ayacucho and Cusco dialects. Other resources, like NLLB, may
use the dialect code, e.g. `quy` for Ayacucho Chanka. The same is true for a
few other macro codes, like `ff` (Macro code for Fulfulde, whereas other
sources may use e.g. `fuv`.)
* Really, there are notes that can be made about almost any code, from the
well-accepted conventions like `zh` for Mandarin, to many dialectical notes,
like which variant of Hmong really is the `hmn` data? But the above ones are
made specifically for ones where the authors are aware of other datasources floating
out there that use different conventions.
## Audit
Following [Quality at a Glance](https://arxiv.org/abs/2103.12028), the authors performed
an "audit" of every corpus in this dataset. Although the authors did not speak most
languages, they were able to give high-level comments on the general quality. They
looked at a sample of 20 documents of each language.
After an initial round of auditing, they devised a new set of filters and applied
them. They then re-did all audits.
### Overall notes from the audit
The decision was to **include languages that looked noisy, but omit any language
that was clearly majority noise, or only had 20 or fewer docs.** This is a low
bar -- twenty documents can be very little indeed, and some of the corpora released are quite noisy, but all of them should have at least the potential to
be used in some useful way. The motivation for not releasing nonsense or tiny
datasets is to not give a false sense of how multilingual this dataset actually
is ("Representation washing"), as recommended by **Quality at a Glance**.
A few overarching points:
* Many low-resource languages only had Bible text, or in some cases jw.org
data. These are marked in the rows below. Generally `ok bible` means that
100% of the audited sentences were Biblical, whereas if `bible` is simply
mentioned in the note, it was not the only source of data.
* Indian languages in the Latin script had a high concentration of
pornographic content.
### Renames and Merges as a result of the Audit
In several cases, it was clear from the audit that the corpora were not in the
languages that the LangID model claimed they were. This led to the following
renames:
* dty renamed to `zxx-xx-dtynoise`, aka a "language" of noise. This is mainly
mis-rendered PDFs and may have some practical applications for decoding
said.
* `fan` renamed to `bum`
* `ss-SZ` renamed to `ss` -- this was just a result of us having inconsistent
data labels.
* `cjk` merged into the `gil` dataset
* `bjj` merged into the `awa` dataset
## Canaries
Canaries are provided in separate `canaries` folder. Canaries are organized into three directions: `monolingual` hosts canaries designed for the MADLAD-400 monody data, `multiway` for the multiway data, and `generic` the generic canaries generated only from the model's vocabulary.
* Monolingual: Canaries here are organized by the language the canary was generated from. This corresponds exactly to the `translate_copy` setting in the paper, where the source and target language match.
* Multiway: Canaries here are organized in one of two fashions. `to_XX` indicates canaries organized by the target language (and where the source language could be any language). `XX-XX` indicates the canaries (interleaved_both and interleaved_mislabeled_both) designed for a specific pair of languages.
Within each subdirectory above, canaries are into separate files named by the canary type. There is always only a single file for each canary type. The `generic` folder contains within it the four canary types.
Canaries can be mixed in with normal training data to then be analyzed post-hoc to training
## References
Raffel, Colin, et al. "Exploring the limits of transfer learning with a unified
text-to-text transformer." J. Mach. Learn. Res. 21.140 (2020): 1-67.
## Contact
Please reach out to {snehakudugunta, icaswell}꩜google.com. For questions about the canaries, reach out to [email protected]
## License
This data is released with the `CC-BY-4.0` license.
## Detailed notes from the audit
Here are the notes on all languages, along with the number of documents
found, and the final decision made with respect to including the language in
this dataset.
| Lang. | note | N | decision |
| --------------- | ------------------------ | ---------- | --------------- |
| en | ok | 1838712272 | keep |
| ru | ok | 402458746 | keep |
| es | good | 250906994 | keep |
| de | ok | 225111495 | keep |
| fr | ok | 218863911 | keep |
| it | ok | 126406256 | keep |
| pt | ok | 124207090 | keep |
| pl | ok | 90908786 | keep |
| nl | ok | 86594116 | keep |
| tr | ok | 56417359 | keep |
| vi | ok | 54988654 | keep |
| cs | ok | 38254671 | keep |
| id | ok | 37979244 | keep |
| ro | ok | 35397563 | keep |
| sv | ok. Also the last | 35153050 | keep |
: : language (suz) is "ok : : :
: : bible" : : :
| hu | ok | 29677075 | keep |
| uk | ok | 24968305 | keep |
| fa | idk ask a farsi speaker; | 23138888 | keep |
: : ALI\: OK : : :
| ja | ok a little en mixed in | 21818123 | keep |
| el | ok | 20932239 | keep |
| fi | ok | 20433664 | keep |
| da | ok | 17865888 | keep |
| th | ok | 17439979 | keep |
| no | ok | 14864710 | keep |
| bg | ok | 12755329 | keep |
| ko | ok | 12653878 | keep |
| ar | good | 12411641 | keep |
| sk | ok | 11857945 | keep |
| ca | ok | 9477390 | keep |
| lt | ok | 8748025 | keep |
| iw | ok | 7194574 | keep |
| sl | ok | 6310419 | keep |
| et | ok | 5542933 | keep |
| lv | ok | 5007982 | keep |
| hi | ok some porn | 4512205 | keep |
| sq | good | 3622957 | keep |
| az | good | 3256331 | keep |
| hr | ok | 2841400 | keep |
| ta | ok | 2594191 | keep |
| ms | ok | 2337672 | keep |
| ml | ok | 2072605 | keep |
| sr | ok | 2010607 | keep |
| kk | ok | 1810963 | keep |
| te | ok a lot of weirdly low | 1682441 | keep |
: : quality looking content : : :
: : like commerce : : :
| mr | ok fix virama | 1673848 | keep |
| is | ok | 1560913 | keep |
| bs | good | 1362582 | keep |
| mk | ok | 1358293 | keep |
| gl | ok | 1253170 | keep |
| eu | ok | 1155671 | keep |
| bn | ok | 1138848 | keep |
| be | ok | 1092785 | keep |
| ka | ok | 936497 | keep |
| fil | ok more bible than | 901507 | keep |
: : expected for such a : : :
: : major language : : :
| mn | ok mongolian cyrillic | 879878 | keep |
| af | good | 868671 | keep |
| uz | ok some cyrllic noise | 669909 | keep |
| gu | ok | 659727 | keep |
| kn | ok | 657846 | keep |
| kaa | ok cyrllic | 586361 | keep |
| sw | ok | 537847 | keep |
| ur | ok | 467236 | keep |
| ne | ok | 453349 | keep |
| cy | ok; was terrible before | 430719 | keep |
: : filtering short docs : : :
| hy | ok | 397523 | keep |
| ky | ok | 367577 | keep |
| si | good | 349220 | keep |
| tt | good plus some | 346927 | keep |
: : nonunicode misrendered : : :
: : PDF : : :
| tg | good | 328194 | keep |
| la | ok some broken chars | 319178 | keep |
| so | good | 293218 | keep |
| ga | ok some en noise | 285999 | keep |
| km | ook | 285740 | keep |
| mt | ok | 265388 | keep |
| eo | ok; likely a lot of Mt | 259971 | keep |
| ps | ok | 252888 | keep |
| rw | ok | 226466 | keep |
| ku | ok | 218850 | keep |
| lo | ok many entities in | 215982 | keep |
: : latin script : : :
| fy | ok plausible but i bet | 210025 | keep |
: : there is a lot of nl in : : :
: : there : : :
| ha | ok | 173485 | keep |
| my | filter noise and en fix | 172401 | keep |
: : virama : : :
| dv | good | 167179 | keep |
| pa | ok | 150588 | keep |
| ckb | ok | 148870 | keep |
| lb | ok | 145988 | keep |
| mg | ok some bible jw | 115387 | keep |
| ht | ok | 110443 | keep |
| ug | ok | 106549 | keep |
| am | good | 106301 | keep |
| or | ok | 100530 | keep |
| fo | good | 97754 | keep |
| gd | ok | 94275 | keep |
| ba | ok | 90318 | keep |
| tk | ok; a few weird docs | 82495 | keep |
| mi | ok | 79509 | keep |
| hmn | ok | 75213 | keep |
| grc | ok some bible | 70730 | keep |
| jv | ok | 69473 | keep |
| ceb | ok | 66164 | keep |
| sd | good | 65858 | keep |
| yi | ok | 64949 | keep |
| kaa-Latn | ok urls are .ru or .kz | 61169 | keep |
| sn | ok | 60196 | keep |
| co | ok;l i suspect lots of | 55387 | keep |
: : MT : : :
| su | good | 54968 | keep |
| pap | ok | 54498 | keep |
| ig | ok | 54410 | keep |
| zu | good | 53809 | keep |
| xh | ok | 53672 | keep |
| sm | ok | 52614 | keep |
| ny | ok | 52244 | keep |
| yo | ok | 52067 | keep |
| cv | good | 47318 | keep |
| el-Latn | good; a lot of old | 46428 | keep |
: : content! : : :
| kl | ok | 46027 | keep |
| haw | ok scam tv products | 45670 | keep |
| gsw | wtf is happening here; | 42712 | keep |
: : keep with disclaimer; : : :
: : STILL BOILERPLATE : : :
| tet | good ; actually a lot of | 40367 | keep |
: : fun data! : : :
| st | ok | 40360 | keep |
| lus | ok | 36437 | keep |
| oc | ok | 36379 | keep |
| as | good | 33825 | keep |
| rm | ok | 33805 | keep |
| br | ok after shortfilter | 33219 | keep |
| sah | ok | 29169 | keep |
| hi-Latn | filter porn this is half | 26723 | keep |
: : porn : : :
| se | good | 23872 | keep |
| cnh | good, some local news! | 21556 | keep |
: : not sure if WL : : :
| om | ok | 18895 | keep |
| ce | ok | 14968 | keep |
| udm | ok | 13376 | keep |
| lg | ok lot of | 13030 | keep |
: : www.bukedde.co.ug in : : :
: : this : : :
| os | ok | 12623 | keep |
| nv | ok | 12578 | keep |
| kha | ok | 12070 | keep |
| ilo | ok some bible | 11754 | keep |
| ctd-Latn | ok; from some local | 11629 | keep |
: : news? : : :
| vec | very noisy has wiki from | 11108 | keep |
: : other langs and .it : : :
: : websites so not sure if : : :
: : vec : : :
| hil | ok some en boilerplate | 10564 | keep |
| tyv | ok fun stuff plus some | 9083 | keep |
: : russian noise i think : : :
| iba | ok jw data | 7638 | keep |
| ru-Latn | ok | 7523 | keep |
| kbd | ok many .ru | 7486 | keep |
| ti | ok; poor tigray | 7288 | keep |
| sa | ok | 7117 | keep |
| av | good | 6331 | keep |
| bo | needs some serious | 6226 | keep |
: : script filtering. but : : :
: : there is some ok data in : : :
: : there. : : :
| zza | good | 6019 | keep |
| ber-Latn | ok | 5612 | keep |
| otq | ok | 5554 | keep |
| te-Latn | great good text....but | 5305 | keep |
: : mostly pornographic : : :
| bua | ok | 5264 | keep |
| ts | good | 5198 | keep |
| cfm | ok mostly from | 4858 | keep |
: : chinland.co : : :
| tn | good | 4821 | keep |
| krc | ok | 4815 | keep |
| ak | good; much but not all | 4768 | keep |
: : bible : : :
| meo | ok mostly blogs | 4655 | keep |
| chm | ok; fyi watch out for | 4653 | keep |
: : yandex translationese : : :
| to | good ; news bible | 4612 | keep |
: : government : : :
| ee | good; mostly religious | 4536 | keep |
| nso | ok | 4422 | keep |
| ady | good | 4206 | keep |
| rom | bible | 4187 | keep |
| bho | mostly from anjoria.com. | 4121 | keep |
: : Looks like valid : : :
: : Bhojpuri. : : :
| ltg | ok mostly www.lakuga.lv | 4120 | keep |
| fj | ok | 3976 | keep |
| yua | ok | 3965 | keep |
| gn | ok some broken | 3858 | keep |
: : characters some bible : : :
| az-RU | good; a lot of JW | 3781 | keep |
| ln | ok bible jw | 3325 | keep |
| ada | good; bible; likely | 3095 | keep |
: : mixed with gaa : : :
| myv | maybe has .ru urls | 3095 | keep |
| bik | ok. keep in mind the bik | 3092 | keep |
: : vs bcl issue. : : :
| tlh | ok, but why tf are there | 3054 | keep |
: : websites inklingon? all : : :
: : MT ? : : :
| kbp | not sure if right script | 3036 | keep |
: : wiki says latin : : :
| war | ok but v sus. Pls filter | 2928 | keep |
: : out wikipedia : : :
| wa | ok lots of wiki stuff | 2772 | keep |
| bew | mostly blogs. idk if | 2677 | keep |
: : standard Indonesian or : : :
: : not : : :
| rcf | ok | 2630 | keep |
| ta-Latn | good text .... but | 2580 | keep |
: : pornographic : : :
| kac | ok | 2567 | keep |
| iu | filter script some is en | 2537 | keep |
: : rest is iu script : : :
| ay | good; mix of bible and | 2505 | keep |
: : other news sources : : :
| kum | ok | 2495 | keep |
| qu | ok | 2449 | keep |
| bgp | almost all ur-Latn. | 2427 | keep |
: : consider removing or : : :
: : renaming : : :
| hif | ok some en noise and | 2358 | keep |
: : religious : : :
| kw | ok short boilerplate | 2324 | keep |
: : bible wiki; ok some porn : : :
| nan-Latn-TW | ok | 2285 | keep |
| srn | ok bible + jw | 2281 | keep |
| tly-IR | deeply sus | 2239 | keep |
| sg | ok jw | 2106 | keep |
| gom | ok | 2102 | keep |
| ml-Latn | ok some short docs | 2071 | keep |
| kj | ok | 2062 | keep |
| ksd | ok bible | 2000 | keep |
| dz | ok; hidden parallel | 1899 | keep |
: : text; maybe actually bo; : : :
: : mainly buddhist : : :
| kv | ok a lil boilerplate | 1878 | keep |
: : vibes : : :
| msi | ok | 1870 | keep |
| ve | ok mostly bible jw | 1866 | keep |
| zap | ok JW. | 1803 | keep |
| zxx-xx-dtynoise | BEAUTIFUL NOISE rename | 1765 | keep |
: : but keep as beautiful : : :
: : xample. (was called : : :
: : "dty") : : :
| meu | ok bible | 1728 | keep |
| iso | ok jw | 1721 | keep |
| ium | filter out zh | 1721 | keep |
| nhe | ok | 1714 | keep |
| tyz | ok bible bu again i | 1707 | keep |
: : think some mixeed : : :
: : dialects : : :
| hui | ok some bible | 1680 | keep |
| new | ok | 1634 | keep |
| mdf | ok some short docs | 1609 | keep |
| pag | bible | 1588 | keep |
| gv | filter short repetitive | 1586 | keep |
: : sentences; still same : : :
: : but keep : : :
| gag | has 1-2 cyrillic | 1572 | keep |
: : examples with small amts : : :
: : of arabic script noise : : :
| ngu | ok | 1534 | keep |
| quc | bible | 1526 | keep |
| mam | ok bible jw | 1513 | keep |
| min | ok mostly wiki and bible | 1474 | keep |
| ho | ok | 1466 | keep |
| pon | bible | 1462 | keep |
| mrj | ok | 1447 | keep |
| lu | ok jw | 1444 | keep |
| gom-Latn | ok very noisy ; some ok | 1432 | keep |
: : stuff ; release with : : :
: : disclaimer : : :
| alt | ok | 1422 | keep |
| nzi | ok | 1371 | keep |
| tzo | ok bible + jw | 1357 | keep |
| bci | ok bible | 1329 | keep |
| dtp | ok; mostly from | 1309 | keep |
: : www.newsabahtimes.com.my : : :
| abt | fine; bible | 1305 | keep |
| bbc | ok | 1274 | keep |
| pck | ok | 1255 | keep |
| mai | ok mild amounts of en | 1240 | keep |
: : noise : : :
| mps | ok bible | 1239 | keep |
| emp | ok bible | 1238 | keep |
| mgh | ok bible jw | 1222 | keep |
| tab | idk plausibly ok | 1202 | keep |
| crh | ok | 1184 | keep |
| tbz | good mostly bible but | 1126 | keep |
: : not all : : :
| ss | good mix of data ; | 1089 | keep |
: : renamed from "ss" : : :
| chk | ok bible | 1082 | keep |
| bru | ok; bible | 1072 | keep |
| nnb | ok | 1071 | keep |
| fon | ok mostly jw but not all | 1065 | keep |
| ppk | bible | 1063 | keep |
| tiv | ok jw | 1063 | keep |
| btx | ok probably | 1009 | keep |
| bg-Latn | ok | 991 | keep |
| mbt | ok bible | 969 | keep |
| ace | good; bible | 966 | keep |
| tvl | ok jw | 933 | keep |
| dov | ok bible + jw | 923 | keep |
| ach | good; bible | 915 | keep |
| xal | ok has .ru sites though | 913 | keep |
| cuk | ok bible | 899 | keep |
| kos | ok lds bible | 881 | keep |
| crs | ok | 873 | keep |
| wo | ok; mostly bible. | 871 | keep |
| bts | ok; mostly bible | 869 | keep |
| ubu | ok bible | 846 | keep |
| gym | ok biblle | 820 | keep |
| ibb | ok bible and repeated @ | 818 | keep |
| ape | good; bible | 814 | keep |
| stq | ok i think ? | 809 | keep |
| ang | much noise but some good | 803 | keep |
: : Old English in there! : : :
| enq | ok bible | 793 | keep |
| tsg | much noise but somegood | 789 | keep |
: : data too! : : :
| shn | mostly English | 788 | keep |
: : boilerplate. filter by : : :
: : latin text before : : :
: : releasing : : :
| kri | ok boilerplate noise | 786 | keep |
: : bible jw : : :
| kek | ok jw bible | 782 | keep |
| rmc | ok | 738 | keep |
| acf | good; bible | 730 | keep |
| syr | good; practictitioners | 716 | keep |
: : should keep dialect in : : :
: : mind. : : :
| qub | bible | 705 | keep |
| bm | good | 702 | keep |
| tzh | ok jw | 702 | keep |
| jiv | ok bible | 696 | keep |
| kn-Latn | filter en noise of | 688 | keep |
: : karnatake govt websites : : :
| kjh | ok .ru domain | 672 | keep |
| yap | ok | 638 | keep |
| ban | ok bible | 637 | keep |
| tuc | ok bible | 635 | keep |
| tcy | good; mostly wikipedia; | 632 | keep |
: : likely some konkani : : :
: : mixed in : : :
| cab | ok jw | 629 | keep |
| cak | ok bible | 617 | keep |
| din | ok after SD filter | 611 | keep |
| arn | good; bible | 593 | keep |
| lrc | ok | 587 | keep |
| gil | empty; but merged in | 586 | keep |
: : data in "cjk" : : :
| gil | this is all in gil | 586 | keep |
: : (Kiribati). merged into : : :
: : "gil" : : :
| rwo | bible | 572 | keep |
| hus | ok bible | 569 | keep |
| bum | ok bible; but wrong | 559 | keep |
: : language. Data is in : : :
: : Bulu, not Fang : : :
| mak | ok bible | 555 | keep |
| frp | fair amount from | 550 | keep |
: : wikipedia. : : :
| seh | ok jw | 545 | keep |
| twu | ok bible, but also i | 539 | keep |
: : think it's lots of mixed : : :
: : similar dialects : : :
| kmb | ok bible jw | 538 | keep |
| ksw | ok bible | 536 | keep |
| sja | ok bibe | 527 | keep |
| amu | good; bible; crazy | 511 | keep |
: : diacritics : : :
| mad | remove mostly short text | 509 | keep |
| quh | bible | 501 | keep |
| dyu | ok bible | 483 | keep |
| toj | ok jw | 452 | keep |
| ch | ok; not sure about WL | 449 | keep |
| sus | hella sus jk ok bible | 437 | keep |
| nog | ok | 419 | keep |
| jam | ok bible | 416 | keep |
| gui | ok bible | 409 | keep |
| nia | ok | 408 | keep |
| mas | ok some amount of bible | 405 | keep |
| bzj | ok bible | 404 | keep |
| mkn | ok bible | 402 | keep |
| lhu | ok bible | 377 | keep |
| ctu | ok bible | 366 | keep |
| kg | ok bible jw | 365 | keep |
| inb | ok bible | 343 | keep |
| guh | ok bible | 331 | keep |
| rn | bible | 323 | keep |
| bus | ok; bible; about 50bzc | 322 | keep |
| mfe | ok mostly bible maybe | 320 | keep |
: : some french creole short : : :
: : doc noise : : :
| sda | ok bible | 317 | keep |
| bi | good! fun! | 311 | keep |
| cr-Latn | noise and lorem ipsom. | 303 | keep |
: : But some ok Cree text. : : :
| gor | ok bible | 303 | keep |
| jac | ok bible | 303 | keep |
| chr | ok bible | 301 | keep |
| mh | ok jw lds | 296 | keep |
| mni | ok | 290 | keep |
| wal | ok bible + jw | 286 | keep |
| teo | ok bible | 274 | keep |
| gub | ok bible | 271 | keep |
| qvi | bible | 266 | keep |
| tdx | ok jw | 262 | keep |
| rki | ok | 251 | keep |
| djk | ok; bible+jw | 246 | keep |
| nr | ok | 246 | keep |
| zne | ok jw | 239 | keep |
| izz | ok bible | 237 | keep |
| noa | ok | 234 | keep |
| bqc | ok; bible | 228 | keep |
| srm | ok; bible + jw | 227 | keep |
| niq | ok | 226 | keep |
| bas | ok; has some fun blog | 216 | keep |
: : stuff! : : :
| dwr | ok; bible; mixed script | 215 | keep |
| guc | ok bible | 214 | keep |
| jvn | ok bible | 213 | keep |
| hvn | ok religioous text | 200 | keep |
| sxn | ok bible ; also wild | 197 | keep |
: : diacritics : : :
| koi | ok | 196 | keep |
| alz | good; bible | 195 | keep |
| nyu | ok | 195 | keep |
| bn-Latn | ok | 191 | keep |
| suz | | 186 | keep |
| pau | ok | 185 | keep |
| nij | ok | 183 | keep |
| sat-Latn | good! al from local news | 183 | keep |
: : sources : : :
| gu-Latn | filter short en | 179 | keep |
: : boilerplate and : : :
: : repetitive sentences : : :
| msm | ok bible | 177 | keep |
| maz | ok bible jw | 170 | keep |
| qxr | bible | 153 | keep |
| shp | ok bible | 150 | keep |
| hne | ok | 146 | keep |
| ktu | ok bible jw | 144 | keep |
| laj | ok bible | 144 | keep |
| pis | bible | 139 | keep |
| mag | ok fix virama issue | 138 | keep |
| gbm | ok | 137 | keep |
| tzj | ok bible | 136 | keep |
| oj | ok | 135 | keep |
| ndc-ZW | ok | 132 | keep |
| tks | ok bible bu again i | 127 | keep |
: : think some mixeed : : :
: : dialects : : :
| gvl | filter short boilerplate | 126 | keep |
: : mostly bible : : :
| knj | ok bible | 126 | keep |
| awa | all bible in awadhi | 126 | keep |
: : (awa). Renamed from bjj : : :
| spp | ok bible | 123 | keep |
| mqy | bible remove short docs | 119 | keep |
| tca | ok bible + jw | 117 | keep |
| cce | ok jw | 116 | keep |
| skr | ok; some pnb mixed in | 107 | keep |
| kmz-Latn | ok soome ar script noise | 106 | keep |
| dje | ok; mostly but not all | 100 | keep |
: : bible : : :
| gof | ok some bible | 97 | keep |
| agr | good; bible | 93 | keep |
| qvz | bible | 88 | keep |
| adh | good; bible | 87 | keep |
| quf | bible | 86 | keep |
| kjg | ok bible | 84 | keep |
| tsc | ok | 82 | keep |
| ber | ok great! | 79 | keep |
| ify | ok bible | 79 | keep |
| cbk | ok bible | 78 | keep |
| quy | bible | 78 | keep |
| ahk | good; bible; crazy | 77 | keep |
: : diacritics : : :
| cac | ok bible | 77 | keep |
| akb | good; bible | 71 | keep |
| nut | ok | 67 | keep |
| ffm | ok bible; mixed fulfulde | 65 | keep |
: : dialects; consider : : :
: : merging with ff : : :
| taj | ok bible | 65 | keep |
| ms-Arab | ok mostly utusanmelayu | 63 | keep |
: : website : : :
| brx | quite good! | 62 | keep |
| ann | good; all from wikimedia | 56 | keep |
: : incubator : : :
| qup | bible | 53 | keep |
| ms-Arab-BN | ok not sure if same as | 46 | keep |
: : ms-Arab : : :
| miq | ok | 45 | keep |
| msb | ok bible | 41 | keep |
| bim | good; bible | 40 | keep |
| raj | ok | 40 | keep |
| kwi | ok bible | 37 | keep |
| tll | ok jw | 37 | keep |
| trp | good ; lots of random | 36 | keep |
: : stuff : : :
| smt | ok bible but lots of | 34 | keep |
: : different bibles! : : :
| mrw | ok | 29 | keep |
| dln | ok bible | 28 | keep |
| qvc | bible | 27 | keep |
| doi | ok actually nice! | 26 | keep |
| ff | ok after shortfilter | 26 | keep |
| zh | very noisy | 19850947 | keep (filtered) |
| zh-Latn | poor quality | 602 | remove |
| rhg-Latn | remove | 10302 | remove |
| ja-Latn | remove maybe low quality | 7516 | remove |
: : short and repeated : : :
| pam | remove | 2773 | remove |
| za | revisit after | 1700 | remove |
: : shortfilter : : :
| ar-Latn | terrible, 0% orrect, | 1520 | remove |
: : remove : : :
| mnw | remove en noise and | 1100 | remove |
: : boilerplate : : :
| fip | ok jw ; but wrong | 729 | remove |
: : language. mostly : : :
: : Mambwe-Lungu and Bemba, : : :
: : as well as Fipu (mgr+bem : : :
: : vs. fip) : : :
| el-CY | bad; not Cypriote | 537 | remove |
| luz | terrible; remove | 354 | remove |
| cni | ok; bible; lots of mixed | 261 | remove |
: : in content in : : :
: : not,cob,cpc,arl : : :
| apd-SD | terribly questionable; | 227 | remove |
: : probably remove : : :
| mey | mostly short and noisy | 127 | remove |
: : borderline : : :
| awa | OK; should be used with | 126 | remove |
: : caution and suspicion : : :
| mtq | remove short doc | 111 | remove |
: : repetitive : : :
| mel | remove noisy en | 103 | remove |
| mr-Latn | remove mostly porn and | 91 | remove |
: : short docs : : :
| srr | remove ; english | 91 | remove |
: : boilerplate : : :
| en-Cyrl | ok ... some fr-Cyrl too | 90 | remove |
: : and maybe others : : :
| en-Arab | remove | 79 | remove |
| syl | idk maybe ok ? | 61 | remove |
| jax | filter mostly | 58 | remove |
: : text.medjugorje.ws : : :
: : boilerplate : : :
| xmm | very noisy lots of dj | 58 | remove |
: : tiktok and peppa pig : : :
: : repeated : : :
| shu | quite questionable. prob | 53 | remove |
: : remove : : :
| ks | ok shorter docs | 51 | remove |
| gyn | remove boilerplate and | 45 | remove |
: : porn : : :
| aa | some pretty bad data but | 32 | remove |
: : also some good data. : : :
: : filter on "Woo" (case : : :
: : sensitive) : : :
| sjp | terible; probably | 31 | remove |
: : remove; check again : : :
: : after short filter : : :
| abs | all short nonsense | 24 | remove |
: : remove : : :
| mui | remove short docs | 23 | remove |
| mdh | filter porn short text | 22 | remove |
: : and repetitive : : :
: : boilerplate : : :
| noe | ok | 22 | remove |
| sxu | rvisit after shortfilter | 22 | remove |
| bhb-Gujr | bad. remove. all junk | 20 | remove |
: : gu. : : :
| yaq | remove | 20 | remove |
| prk | ok | 18 | remove |
| cgg | rather noisy but | 17 | remove |
: : potentialy ok. not sure : : :
: : if WL or not : : :
| bto | bad; remove unless short | 16 | remove |
: : filter keeps enough : : :
| ayl | terrible | 13 | remove |
| pa-Arab | ok | 13 | remove |
| bmm | terrible. filter on | 11 | remove |
: : short and reevaluate : : :
| mfb | remove short boilerplate | 11 | remove |
| mtr | ok fix virama remove en | 11 | remove |
: : noise : : :
| pmy | remove | 11 | remove |
| skg | terrible; remove | 11 | remove |
| ymm | remove | 11 | remove |
| xnr | ok maybe fix virama | 9 | remove |
: : though it seems fine : : :
| kjb | ok bible | 8 | remove |
| azg | short noise; bible | 7 | remove |
| bgz | idk maybe ok but | 7 | remove |
: : probably bad : : :
| ctg | probably terrible | 7 | remove |
: : probably remove : : :
| nyo | ok | 7 | remove |
| mdy | ok bible | 6 | remove |
| syl-Latn | revist or remove after | 6 | remove |
: : shortfilter : : :
| xog | ok bible and stories | 6 | remove |
| cyo | terrifying noise; remove | 4 | remove |
| kfy | filter virama issue | 4 | remove |
| nd | ok | 4 | remove |
| rwr | remove | 4 | remove |
| tuf | ok bible | 4 | remove |
| clu | ok bible | 3 | remove |
| ng | ok | 3 | remove |
| zyj | deeply bad data .. | 3 | remove |
: : revisit after : : :
: : shortfilter : : :
| rkt | ok | 2 | remove |
| bgc | super sketch. Remove | 1 | remove |
: : unless short doc filter : : :
: : leaves some. remove : : :
| dcc | remove | 1 | remove |
| ff-Adlm | good | 1 | remove |
| gju | remove short boilerplate | 1 | remove |
| max | remove short some ru | 1 | remove |
| mwr | filter short docs fix | 1 | remove |
: : virama : : :
| trw | sus; remove | 1 | remove |
| vkt | 1 doc remove | 1 | remove |
| gjk | empty remove | 0 | remove |
| bfy | very bad. remove unless | 0 | remove |
: : it looks better after : : :
: : filtering short docs; : : :
: : remove : : :
| nyn | ok | 0 | remove |
| sgj | remove | 0 | remove |
A few comments too long to fit in the table above:
* `alt`: WAIT THIS IS AMAZING IT IS ACTUALLY ALTAI! e.g. from urls like
https://altaicholmon.ru/2020/02/28/jarashty-la-jajaltany-jarkyndu-lekeri/
* `tly-IR`: They all look like boilerplate content, e.g., list of
keywords/search queries used to bump page ranking in search results. Not any
useful material for translation. Remove.
* `zap`: pls note that at least some Zapotec speakers tend to view it as one
language, not as a million dialects like ISO does. However, some are
certainly mutually unintelligible, complicating the matter.
* `zh-Latn`: The biggest problem is that several examples are not in Latin
Chinese (i.e., romanization in my understanding) but in English or mixed
English and Chinese. For those data in Latin Chinese, their quality seems to
be good.
* `zh`: Many examples are porn-related, particularly those very long
documents. Also, there are some examples of traditional Chinese.
## Final Dataset information
The number of documents, sentences, tokens, characters, and bytes for the noisy
and clean splits of the data. Note that the "toks" field below uses whitespace
for tokenization, so is not appropriate for non-whitespace-separating languages
like Chinese (see section above). Note that the english subset in this version
is missing 18% of documents that were included in the published analysis of the dataset.
These documents will be incoporated in an update coming soon.
BCP-47 | docs (noisy) | docs (clean) | sents (noisy) | sents (clean) | toks (noisy) | toks (clean) | chars (noisy) | chars (clean) | clean | noisy |
----------------|:---------------|:---------------|:----------------|:----------------|:---------------|:---------------|:----------------|:----------------|:---------|:---------|
total* | 7.2B | 3.7B | 133.1B | 97.5B | 4.6T | 2.6T | 30.6T | 16.0T | 11.4 T | 6.3 T
en* | 3.0B | 1.5B | 71.1B | 45.4B | 2.0T | 1.3T | 12.3T | 7.6T | 2.6 T | 4.3 T |
ru | 823M | 402.5M | 823M | 12.4B | 416.5B | 240.9B | 3.1T | 1.8T | 832.9 G | 1.4 T |
es | 476.4M | 250.9M | 8.3B | 4.5B | 325.7B | 170.4B | 2.1T | 1.1T | 380.9 G | 747.5 G |
de | 478.6M | 225.1M | 11.5B | 6B | 299.5B | 139.6B | 2.2T | 1T | 370.6 G | 815.5 G |
fr | 384.2M | 218.9M | 7.9B | 5B | 307.1B | 165.2B | 2T | 1T | 370.4 G | 699.1 G |
it | 238.9M | 126.4M | 4.5B | 2.5B | 180.1B | 83.6B | 1.2T | 553.1B | 198.4 G | 429.6 G |
pt | 209.2M | 124.2M | 4B | 2.4B | 123.2B | 79.2B | 791.5B | 499.8B | 183.1 G | 289.6 G |
pl | 145.1M | 90.9M | 3.3B | 2.4B | 68.9B | 49.2B | 505B | 356.4B | 140.7 G | 202.5 G |
nl | 134.5M | 86.6M | 134.5M | 2.3B | 104.4B | 51.6B | 698.5B | 334.5B | 118.2 G | 247.5 G |
tr | 107M | 56.4M | 107M | 1.2B | 41.9B | 25B | 328.8B | 198.9B | 73.7 G | 123.9 G |
vi | 92.8M | 55M | 1.6B | 1B | 71.5B | 48.7B | 342B | 228.8B | 88.8 G | 133.9 G |
cs | 72.1M | 38.3M | 1.7B | 1B | 40.8B | 22.1B | 272.2B | 147.9B | 62.1 G | 112.7 G |
id | 120.9M | 38M | 2.2B | 747.5M | 60.4B | 20.2B | 443B | 148.3B | 48.5 G | 148.7 G |
ro | 60.8M | 35.4M | 60.8M | 746.4M | 37.1B | 22.9B | 244.1B | 148.2B | 55.5 G | 90.3 G |
sv | 65.2M | 35.2M | 65.2M | 1B | 62.1B | 23.9B | 422.6B | 153.7B | 57.0 G | 149.9 G |
hu | 47.6M | 29.7M | 1.3B | 806.3M | 29.8B | 17.8B | 223.6B | 134.9B | 53.5 G | 86.8 G |
uk | 46.6M | 25M | 1B | 599.9M | 21.6B | 12.8B | 164.2B | 95.2B | 45.1 G | 75.8 G |
fa | 58.1M | 23.1M | 920.6M | 493.5M | 40.6B | 18.4B | 220.4B | 96.7B | 43.4 G | 97.4 G |
ja | 23.3M | 21.8M | 326M | 321.6M | 10.9B | 10.9B | 133.3B | 132.2B | 98.7 G | 99.7 G |
el | 52.4M | 20.9M | 808M | 445.4M | 25B | 12B | 173.2B | 80.9B | 37.9 G | 80.8 G |
fi | 35.8M | 20.4M | 1B | 650.3M | 23.8B | 11.5B | 202.2B | 101.1B | 37.6 G | 74.1 G |
zh | 29.3M | 19.9M | 492.3M | 298.8M | 19.2B | 10B | 333B | 142.3B | 109.9 G | 191.8 G |
da | 38.5M | 17.9M | 1.1B | 508M | 37.7B | 13B | 252B | 83.1B | 29.4 G | 89.5 G |
th | 19M | 17.4M | 19M | 385.8M | 8.9B | 8.9B | 118.6B | 117.6B | 57.6 G | 58.2 G |
no | 34.7M | 14.9M | 34.7M | 498.7M | 46.6B | 11.8B | 305.6B | 74.8B | 27.3 G | 109.8 G |
bg | 27.2M | 12.8M | 599.4M | 360.3M | 14.4B | 8.8B | 95.6B | 57.8B | 26.0 G | 42.8 G |
ko | 19.7M | 12.7M | 628.6M | 471.8M | 13.3B | 9.3B | 65.9B | 43.8B | 34.2 G | 49.1 G |
ar | 67.6M | 12.4M | 876.6M | 182.6M | 39B | 7.1B | 243B | 43.2B | 20.9 G | 115.9 G |
sk | 23.2M | 11.9M | 487.9M | 300.6M | 11.3B | 6.7B | 77.8B | 45.7B | 18.8 G | 31.9 G |
ca | 17.9M | 9.5M | 258.6M | 153M | 8.9B | 5.6B | 56.5B | 34.6B | 12.6 G | 20.8 G |
lt | 15.3M | 8.7M | 374M | 256.9M | 7.5B | 5.3B | 58.6B | 41.3B | 15.7 G | 22.3 G |
he | 14.1M | 7.2M | 302.2M | 196.8M | 9.2B | 5.2B | 54.9B | 30.5B | 14.8 G | 26.3 G |
sl | 12M | 6.3M | 316M | 180M | 6.9B | 4.5B | 47.8B | 30.5B | 11.5 G | 18.0 G |
et | 8.8M | 5.5M | 223.8M | 176.3M | 5B | 3.6B | 40.1B | 28.7B | 10.7 G | 15.0 G |
lv | 8.4M | 5M | 186.1M | 138.5M | 4.8B | 3.2B | 36.7B | 23.9B | 9.1 G | 13.8 G |
hi | 9.9M | 4.5M | 254.4M | 152M | 7.4B | 3.8B | 39.9B | 20.1B | 9.9 G | 19.7 G |
sq | 5.5M | 3.6M | 5.5M | 56.1M | 2.7B | 2.1B | 17B | 12.7B | 4.8 G | 6.6 G |
az | 5.2M | 3.3M | 90.3M | 70.9M | 2.1B | 1.5B | 16.3B | 11.9B | 4.5 G | 6.3 G |
hr | 23M | 2.8M | 476.6M | 53M | 12.6B | 1.4B | 85.1B | 9.6B | 3.7 G | 33.5 G |
ta | 5.6M | 2.6M | 122.5M | 81.9M | 2.1B | 1.1B | 19.2B | 10.6B | 4.9 G | 8.8 G |
ms | 14.1M | 2.3M | 14.1M | 55.2M | 8B | 1.7B | 58.8B | 12.5B | 4.0 G | 20.4 G |
ml | 3.7M | 2.1M | 75M | 52M | 1B | 603.3M | 10.5B | 6.3B | 3.0 G | 5.1 G |
sr | 4.7M | 2M | 4.7M | 64M | 2.7B | 1.6B | 18.6B | 11B | 5.1 G | 8.7 G |
kk | 3.1M | 1.8M | 87.4M | 59.1M | 1.6B | 1B | 13.4B | 8.6B | 3.8 G | 5.8 G |
te | 2.5M | 1.7M | 59M | 46.4M | 900.2M | 618.5M | 7.4B | 5.1B | 2.6 G | 3.8 G |
mr | 2.9M | 1.7M | 2.9M | 50M | 1.2B | 776.9M | 8.7B | 5.5B | 2.8 G | 4.4 G |
is | 2.9M | 1.6M | 73.7M | 39.3M | 2.1B | 979.2M | 14.9B | 6.4B | 2.5 G | 5.9 G |
bs | 12.9M | 1.4M | 163.6M | 9M | 5.9B | 490.9M | 39.5B | 3.3B | 1.3 G | 15.6 G |
mk | 2.9M | 1.4M | 41.3M | 22.6M | 1.3B | 685.9M | 9.1B | 4.5B | 2.0 G | 4.0 G |
gl | 4.2M | 1.3M | 45.3M | 18.8M | 2.3B | 748.4M | 15.6B | 4.8B | 1.7 G | 5.5 G |
eu | 2.1M | 1.2M | 41.7M | 24.8M | 827.5M | 525.3M | 6.9B | 4.3B | 1.5 G | 2.4 G |
bn | 4.3M | 1.1M | 151.2M | 38.6M | 2.5B | 645.7M | 16.8B | 4.3B | 2.2 G | 8.7 G |
be | 2M | 1.1M | 48.8M | 31.3M | 981M | 632.9M | 7.2B | 4.6B | 2.2 G | 3.5 G |
ka | 3.1M | 936.5K | 53.7M | 26.6M | 1.2B | 460.8M | 10.3B | 3.8B | 1.9 G | 5.0 G |
fil | 4.2M | 901.5K | 67.4M | 19.2M | 2.2B | 741.7M | 14.6B | 4.7B | 1.5 G | 5.0 G |
mn | 2.2M | 879.9K | 43.3M | 24M | 1.1B | 487.5M | 7.9B | 3.5B | 1.6 G | 3.5 G |
af | 2.9M | 868.7K | 51.9M | 30M | 1.7B | 795M | 11.8B | 4.8B | 1.8 G | 4.2 G |
uz | 1.4M | 669.9K | 25.7M | 17.5M | 605.9M | 388.3M | 5.2B | 3.3B | 1.1 G | 1.9 G |
gu | 1.3M | 659.7K | 28.9M | 18.1M | 634.4M | 345.9M | 3.9B | 2.1B | 1.1 G | 2.0 G |
kn | 1.6M | 657.8K | 32.9M | 19.2M | 546.4M | 258.6M | 4.6B | 2.2B | 1.1 G | 2.3 G |
kaa | 1.1M | 586.4K | 19.8M | 13.3M | 455.9M | 269M | 3.8B | 2.2B | 990.2 M | 1.6 G |
sw | 1.3M | 537.8K | 1.3M | 9.5M | 660.7M | 345.8M | 4.6B | 2.4B | 826.1 M | 1.6 G |
ur | 967.2K | 467.2K | 29M | 18.4M | 1B | 562.5M | 5.2B | 2.7B | 1.2 G | 2.4 G |
ne | 876.4K | 453.3K | 876.4K | 20.4M | 585M | 345.3M | 3.9B | 2.2B | 1.1 G | 1.9 G |
cy | 4.9M | 430.7K | 68.3M | 7.4M | 3.6B | 275.6M | 26.4B | 1.7B | 609.5 M | 10.0 G |
hy | 2M | 397.5K | 31.1M | 9.9M | 1B | 190.9M | 8.1B | 1.5B | 678.9 M | 3.6 G |
ky | 751.1K | 367.6K | 14.3M | 9.6M | 303.4M | 181.6M | 2.5B | 1.4B | 665.1 M | 1.1 G |
si | 788K | 349.2K | 22.1M | 16M | 507.3M | 293.3M | 3.4B | 1.9B | 1023.6 M | 1.8 G |
tt | 2.1M | 346.9K | 60.2M | 8.6M | 1B | 135M | 12.1B | 1B | 494.1 M | 4.6 G |
tg | 789.2K | 328.2K | 789.2K | 7.4M | 363.8M | 208.8M | 2.6B | 1.4B | 635.7 M | 1.1 G |
la | 2.9M | 319.2K | 85.7M | 13.8M | 1.1B | 218.4M | 8.2B | 1.5B | 550.6 M | 2.9 G |
so | 729.2K | 293.2K | 729.2K | 3.1M | 294.8M | 146.3M | 2.1B | 992.4M | 350.8 M | 746.2 M |
ga | 5.3M | 286K | 31.7M | 6.9M | 4.2B | 229.3M | 30.6B | 1.4B | 500.7 M | 9.8 G |
km | 297.8K | 285.7K | 5M | 5M | 53M | 52.6M | 1.1B | 1.1B | 566.2 M | 570.0 M |
mt | 1.2M | 265.4K | 1.2M | 5.6M | 390.4M | 171.5M | 3.2B | 1.3B | 467.4 M | 1.1 G |
eo | 1.4M | 260K | 33.9M | 9.3M | 745.1M | 253.1M | 5.5B | 1.7B | 627.6 M | 1.9 G |
ps | 429.9K | 252.9K | 5.1M | 3.6M | 293.9M | 177.5M | 1.4B | 848.9M | 403.5 M | 682.9 M |
rw | 681.8K | 226.5K | 681.8K | 1.9M | 225M | 99.8M | 1.7B | 749.1M | 264.8 M | 702.4 M |
ku | 671.9K | 218.9K | 10.7M | 4.9M | 305.3M | 143.8M | 2.1B | 849.9M | 335.3 M | 791.9 M |
lo | 229.1K | 216K | 2.9M | 2.8M | 41.7M | 41.1M | 706.9M | 697.6M | 365.3 M | 370.8 M |
fy | 1.7M | 210K | 12.1M | 3.7M | 506.9M | 94M | 3.7B | 592.3M | 223.0 M | 1.2 G |
ha | 443.9K | 173.5K | 4.5M | 2.4M | 206.5M | 109.3M | 1.3B | 630.2M | 219.0 M | 478.1 M |
my | 176.5K | 172.4K | 176.5K | 10.1M | 96.6M | 96.3M | 1.3B | 1.3B | 648.8 M | 650.4 M |
dv | 264.4K | 167.2K | 4.3M | 3.5M | 92.8M | 64M | 877.3M | 603.1M | 238.3 M | 343.2 M |
pa | 368.2K | 150.6K | 368.2K | 6M | 306M | 152.8M | 1.6B | 797.1M | 414.1 M | 857.6 M |
ckb | 622.7K | 148.9K | 5.6M | 2.5M | 312.7M | 83.3M | 2.2B | 572.7M | 265.0 M | 1011.1 M |
lb | 7.6M | 146K | 47.1M | 3.4M | 7.5B | 85M | 58.4B | 575.5M | 218.4 M | 22.2 G |
mg | 295.2K | 115.4K | 4.5M | 2.6M | 189.4M | 75.5M | 1.3B | 548.5M | 179.0 M | 429.3 M |
ht | 425.6K | 110.4K | 6.7M | 2.6M | 163M | 84.3M | 994.5M | 461.5M | 168.2 M | 361.5 M |
ug | 227.1K | 106.5K | 4.5M | 3.1M | 122.9M | 62.7M | 998.5M | 504.6M | 233.1 M | 449.9 M |
am | 245.2K | 106.3K | 7.1M | 5.3M | 157M | 95.2M | 869.9M | 509M | 345.5 M | 539.4 M |
or | 139.6K | 100.5K | 139.6K | 3.1M | 66M | 47.3M | 437.2M | 309.5M | 160.3 M | 228.1 M |
fo | 382.9K | 97.8K | 3.9M | 1.8M | 136.5M | 48.9M | 923.3M | 314.9M | 122.0 M | 328.8 M |
gd | 206K | 94.3K | 3.7M | 2.4M | 127.6M | 84.5M | 812M | 526M | 173.4 M | 276.6 M |
ba | 372.4K | 90.3K | 9.3M | 2.6M | 101M | 42.1M | 766.5M | 320.7M | 154.8 M | 352.4 M |
tk | 180.2K | 82.5K | 180.2K | 1.8M | 65.4M | 43.3M | 575.2M | 369M | 131.3 M | 221.6 M |
mi | 711.9K | 79.5K | 5.9M | 1.9M | 262.5M | 73.5M | 1.6B | 371.9M | 120.2 M | 539.1 M |
hmn | 241.3K | 75.2K | 3.5M | 1.9M | 192.1M | 80.2M | 1.2B | 408.8M | 124.3 M | 366.0 M |
grc | 364.8K | 70.7K | 13.7M | 2.8M | 298.6M | 65.3M | 2B | 417.8M | 217.7 M | 1.0 G |
jv | 999.5K | 69.5K | 13M | 2M | 302.3M | 52.1M | 2.3B | 376.1M | 130.9 M | 797.8 M |
ceb | 617.5K | 66.2K | 6.7M | 1.6M | 225M | 58.2M | 1.5B | 357.7M | 116.2 M | 451.4 M |
sd | 115.6K | 65.9K | 115.6K | 2.4M | 112.6M | 77.8M | 561M | 380.4M | 182.3 M | 267.1 M |
yi | 160.6K | 64.9K | 3.3M | 1.9M | 129.1M | 53.9M | 838.4M | 352.6M | 146.0 M | 350.8 M |
kaa_Latn | 375.2K | 61.2K | 3.6M | 1.3M | 375.2K | 61.2K | 1.5M | 209.5K | 86.2 M | 264.6 M |
sn | 3.1M | 60.2K | 3.1M | 1.2M | 1.3B | 31.6M | 10.6B | 266M | 92.5 M | 3.2 G |
co | 546.7K | 55.4K | 6.1M | 1.3M | 172.6M | 43.6M | 1.1B | 265.5M | 98.8 M | 386.8 M |
su | 336.6K | 55K | 336.6K | 1.6M | 154M | 39.5M | 967.2M | 286.7M | 100.7 M | 308.5 M |
pap | 259.1K | 54.5K | 259.1K | 1.4M | 183.9M | 41.1M | 1.4B | 229.9M | 83.5 M | 451.4 M |
ig | 130.4K | 54.4K | 2.1M | 1.4M | 129.2M | 45.7M | 846.1M | 251.4M | 93.0 M | 178.9 M |
zu | 372.3K | 53.8K | 3.8M | 1.2M | 148.4M | 27.2M | 1.2B | 257.4M | 89.6 M | 374.7 M |
xh | 310.9K | 53.7K | 2.9M | 1.4M | 81.6M | 31.2M | 749.5M | 287.3M | 100.0 M | 319.1 M |
sm | 137.8K | 52.6K | 1.9M | 1.3M | 100.9M | 53.7M | 607.9M | 276.3M | 88.6 M | 184.5 M |
ny | 181.6K | 52.2K | 181.6K | 1.5M | 80.6M | 34.8M | 611.2M | 277.5M | 91.8 M | 209.8 M |
yo | 115K | 52.1K | 2M | 1.2M | 76.6M | 46.3M | 415.6M | 239M | 89.2 M | 157.8 M |
cv | 599.4K | 47.3K | 12M | 1.6M | 169.6M | 22.2M | 1B | 168.9M | 82.1 M | 413.6 M |
el_Latn | 497.3K | 46.4K | 11.3M | 1.7M | 497.3K | 46.4K | 2.3M | 162.8K | 196.8 M | 571.1 M |
kl | 85.9K | 46K | 2.1M | 1.5M | 32.3M | 22.3M | 403.9M | 279.1M | 84.2 M | 126.1 M |
haw | 310.4K | 45.7K | 7.1M | 1M | 141M | 43.3M | 892M | 214.2M | 69.9 M | 271.2 M |
gsw | 7.6M | 42.7K | 64.5M | 1M | 5B | 22.3M | 42.3B | 149.2M | 53.8 M | 13.5 G |
tet | 291K | 40.4K | 1.9M | 475.7K | 240.6M | 22.8M | 1.6B | 152.3M | 51.2 M | 455.4 M |
st | 96.8K | 40.4K | 96.8K | 1.1M | 65M | 39.8M | 381.5M | 226.9M | 74.0 M | 127.0 M |
lus | 91.5K | 36.4K | 1.4M | 863.5K | 53M | 31.3M | 298.3M | 167.3M | 60.1 M | 107.0 M |
oc | 2.4M | 36.4K | 2.4M | 1.6M | 887.6M | 26.7M | 6.7B | 177.6M | 58.7 M | 1.9 G |
as | 53.9K | 33.8K | 2.4M | 1.7M | 41.4M | 27.9M | 275.8M | 182.1M | 95.8 M | 146.1 M |
rm | 238.1K | 33.8K | 238.1K | 603.4K | 59.2M | 15.8M | 391M | 100.2M | 34.6 M | 133.1 M |
br | 705.4K | 33.2K | 7.8M | 731.7K | 646.8M | 21M | 3.7B | 125.4M | 46.2 M | 1.2 G |
sah | 1.3M | 29.2K | 1.3M | 1.2M | 283.7M | 17.6M | 2.2B | 148.2M | 68.3 M | 852.3 M |
hi_Latn | 1.2M | 26.7K | 22.6M | 1.2M | 1.2M | 26.7K | 5.3M | 98.9K | 53.5 M | 1.7 G |
se | 54.3K | 23.9K | 879.5K | 493.3K | 17.7M | 10M | 148.4M | 84.6M | 31.1 M | 56.6 M |
cnh | 44.4K | 21.6K | 688.6K | 406.9K | 21.6M | 12.5M | 110.8M | 63M | 22.1 M | 39.6 M |
om | 846.1K | 18.9K | 846.1K | 469.8K | 238M | 11.2M | 1.9B | 88.5M | 30.4 M | 881.5 M |
ce | 59.3K | 15K | 991.1K | 460.1K | 17.8M | 9.6M | 130.6M | 67.8M | 31.1 M | 60.2 M |
udm | 67.1K | 13.4K | 942.7K | 510.3K | 14M | 7.4M | 106M | 55.5M | 26.3 M | 49.2 M |
lg | 61.1K | 13K | 510.9K | 166.1K | 21.4M | 6.1M | 160.7M | 48M | 17.3 M | 56.7 M |
os | 172.1K | 12.6K | 172.1K | 359.3K | 27.1M | 6.9M | 233.5M | 50.1M | 23.1 M | 87.7 M |
nv | 17.1K | 12.6K | 17.1K | 86.5K | 3.1M | 1.1M | 24.8M | 9.1M | 2.0 M | 7.9 M |
kha | 37.8K | 12.1K | 235.5K | 75.2K | 15.8M | 6M | 88.6M | 30.2M | 9.8 M | 27.3 M |
ilo | 69.8K | 11.8K | 889.2K | 365.1K | 26.7M | 9M | 187.9M | 59.4M | 20.6 M | 64.0 M |
ctd_Latn | 23.3K | 11.6K | 575.6K | 382.2K | 23.3K | 11.6K | 90.7K | 41K | 21.5 M | 35.1 M |
vec | 1.1M | 11.1K | 10M | 209.7K | 284.7M | 7.8M | 1.8B | 43.8M | 17.7 M | 625.0 M |
hil | 126.8K | 10.6K | 1.1M | 379.7K | 43.9M | 9.2M | 293.5M | 57.2M | 18.5 M | 95.2 M |
tyv | 61.6K | 9.1K | 596.6K | 268.3K | 9.9M | 4.7M | 80.2M | 38.5M | 16.7 M | 36.6 M |
iba | 34K | 7.6K | 326.9K | 126.1K | 37.8M | 4.8M | 251.4M | 30.5M | 10.0 M | 61.3 M |
ru_Latn | 346.3K | 7.5K | 346.3K | 239.1K | 346.3K | 7.5K | 1.5M | 27.7K | 14.9 M | 452.3 M |
kbd | 154.7K | 7.5K | 1.4M | 257.2K | 31.9M | 4.4M | 321.4M | 36.8M | 16.8 M | 209.6 M |
ti | 20.8K | 7.3K | 20.8K | 481.3K | 18.2M | 8.8M | 95.4M | 44.6M | 30.9 M | 63.6 M |
sa | 154.3K | 7.1K | 154.3K | 1.1M | 70M | 9.9M | 512.5M | 88.8M | 44.9 M | 236.6 M |
av | 107.6K | 6.3K | 806.1K | 190.1K | 15.5M | 3.4M | 129M | 30.2M | 12.8 M | 56.0 M |
bo | 6.2K | 6.2K | 1.1M | 1.1M | 3.4M | 3.4M | 88.7M | 88.7M | 40.7 M | 40.7 M |
zza | 370.1K | 6K | 3.3M | 229.2K | 87.7M | 3.9M | 617.3M | 26.3M | 10.0 M | 234.1 M |
ber_Latn | 480.5K | 5.6K | 10.5M | 169.4K | 480.5K | 5.6K | 2.1M | 18.9K | 11.0 M | 945.3 M |
otq | 17.6K | 5.6K | 17.6K | 114.8K | 10.2M | 3.8M | 65M | 23.4M | 7.7 M | 22.8 M |
te_Latn | 236.6K | 5.3K | 4.4M | 269.1K | 236.6K | 5.3K | 1M | 19.3K | 11.4 M | 254.3 M |
bua | 9.8K | 5.3K | 252K | 144.6K | 4.7M | 2.7M | 38M | 21.7M | 10.0 M | 17.9 M |
ts | 34.7K | 5.2K | 34.7K | 248.6K | 39.6M | 6.5M | 377.2M | 38.8M | 12.2 M | 99.5 M |
cfm | 9.1K | 4.9K | 199.6K | 128.6K | 6.2M | 4M | 32.9M | 21.5M | 7.4 M | 11.6 M |
tn | 138.2K | 4.8K | 138.2K | 174.4K | 46M | 5.5M | 302.3M | 29.2M | 9.4 M | 99.0 M |
krc | 359.5K | 4.8K | 2.3M | 153.9K | 50.2M | 2.6M | 369.5M | 20.7M | 9.1 M | 139.9 M |
ak | 19.5K | 4.8K | 341.7K | 210.2K | 12.3M | 4.7M | 74.5M | 24.8M | 9.1 M | 24.7 M |
meo | 790.7K | 4.7K | 16.5M | 39K | 478M | 1.2M | 3B | 7.5M | 3.1 M | 1.2 G |
chm | 81.5K | 4.7K | 929.1K | 179.7K | 17.2M | 2.9M | 132.2M | 21.3M | 9.8 M | 53.5 M |
to | 14.3K | 4.6K | 14.3K | 149K | 10.3M | 5.7M | 58.2M | 29.9M | 9.6 M | 19.0 M |
ee | 14.1K | 4.5K | 353.6K | 246.7K | 9.7M | 6.2M | 67.9M | 32.8M | 11.8 M | 23.3 M |
nso | 376.2K | 4.4K | 376.2K | 188.4K | 419.2M | 5.3M | 2B | 28.2M | 9.1 M | 502.7 M |
ady | 74.9K | 4.2K | 446.8K | 96.9K | 8M | 1.6M | 67.9M | 14.8M | 6.4 M | 30.6 M |
rom | 22.9K | 4.2K | 22.9K | 76.1K | 8.9M | 2.6M | 59M | 15.9M | 5.8 M | 21.0 M |
bho | 13.6K | 4.1K | 306.2K | 118.5K | 7.1M | 2.7M | 37.6M | 13.4M | 7.4 M | 20.6 M |
ltg | 13.1K | 4.1K | 213.7K | 87.3K | 4M | 1.9M | 29.2M | 13.9M | 5.6 M | 11.7 M |
fj | 17K | 4K | 410K | 164.1K | 11.6M | 5.2M | 67.7M | 28M | 8.6 M | 22.5 M |
yua | 10.4K | 4K | 141.6K | 77.6K | 5.2M | 2.5M | 36.8M | 17.2M | 5.7 M | 12.4 M |
gn | 87.1K | 3.9K | 770.9K | 162.6K | 19.2M | 2.7M | 140.7M | 20.8M | 7.8 M | 52.1 M |
az_RU | 6.5K | 3.8K | 231.8K | 177.3K | 6.5K | 3.8K | 24K | 12.9K | 10.3 M | 15.1 M |
ln | 94.7K | 3.3K | 718.7K | 139K | 42.4M | 3.4M | 291.8M | 21.5M | 6.8 M | 85.3 M |
ada | 6.5K | 3.1K | 291.5K | 199.2K | 7.5M | 4.9M | 38.9M | 24.2M | 8.6 M | 13.9 M |
myv | 164.8K | 3.1K | 164.8K | 130K | 16M | 1.7M | 120.3M | 13.8M | 6.2 M | 49.5 M |
bik | 44.8K | 3.1K | 376.7K | 77K | 14.8M | 2.5M | 102.3M | 15.7M | 5.3 M | 34.0 M |
tlh | 516.9K | 3.1K | 516.9K | 46.9K | 221.3M | 1.1M | 1.4B | 7.8M | 2.7 M | 554.2 M |
kbp | 5.9K | 3K | 247.9K | 128.3K | 5.6M | 2.6M | 30.8M | 14.6M | 5.7 M | 12.4 M |
war | 1M | 2.9K | 114M | 96.2K | 612.1M | 2.4M | 3.5B | 16.1M | 3.7 M | 1.2 G |
wa | 70.6K | 2.8K | 1.5M | 127.2K | 35.2M | 3.6M | 198.8M | 20.4M | 7.2 M | 67.8 M |
bew | 311.1K | 2.7K | 10.4M | 58.4K | 212.4M | 1.3M | 1.4B | 8.5M | 3.1 M | 547.1 M |
rcf | 21.6K | 2.6K | 21.6K | 50.5K | 4.9M | 1.2M | 30.2M | 5.7M | 2.1 M | 11.4 M |
ta_Latn | 260.7K | 2.6K | 3.4M | 142.7K | 260.7K | 2.6K | 1.2M | 9.1K | 5.0 M | 215.4 M |
kac | 5.9K | 2.6K | 109.2K | 77.4K | 5M | 2.8M | 26.6M | 13.6M | 4.3 M | 8.0 M |
iu | 5.4K | 2.5K | 92.6K | 53.1K | 1.9M | 907.4K | 17.5M | 8.3M | 4.8 M | 9.9 M |
ay | 8.1K | 2.5K | 196.7K | 83.8K | 3.9M | 1.4M | 34.5M | 13.1M | 4.5 M | 12.7 M |
kum | 4.2K | 2.5K | 132.2K | 89.7K | 2.3M | 1.6M | 18.2M | 12.4M | 5.3 M | 8.0 M |
qu | 149.7K | 2.4K | 1M | 87K | 26.7M | 1.3M | 200.6M | 12.2M | 4.0 M | 68.3 M |
bgp | 355.7K | 2.4K | 5.6M | 43.3K | 186.1M | 1.8M | 1.1B | 9.8M | 3.1 M | 377.5 M |
hif | 702K | 2.4K | 7.9M | 124.7K | 1.2B | 3.2M | 9.1B | 19.1M | 5.9 M | 3.5 G |
kw | 176.9K | 2.3K | 1M | 51.6K | 53.1M | 1.3M | 327.8M | 7.7M | 2.8 M | 89.2 M |
nan_Latn_TW | 7.4K | 2.3K | 7.4K | 72.7K | 7.4K | 2.3K | 28.3K | 7.7K | 4.8 M | 15.4 M |
srn | 16.7K | 2.3K | 16.7K | 139.5K | 8M | 3.4M | 49.1M | 17M | 5.1 M | 15.6 M |
tly_IR | 406.3K | 2.2K | 406.3K | 18.2K | 406.3K | 2.2K | 1.6M | 8.6K | 580.4 K | 283.0 M |
sg | 4.2K | 2.1K | 154K | 117.9K | 4.6M | 3.3M | 22.6M | 15.5M | 4.6 M | 6.8 M |
gom | 4.6K | 2.1K | 178.3K | 108K | 2.7M | 1.4M | 19.8M | 10M | 5.0 M | 10.5 M |
ml_Latn | 260.8K | 2.1K | 3.5M | 77.3K | 260.8K | 2.1K | 1.1M | 7.2K | 3.5 M | 277.7 M |
kj | 112.2K | 2.1K | 881.8K | 22.6K | 46.9M | 877.3K | 339.6M | 6M | 2.1 M | 104.9 M |
ksd | 14.9K | 2K | 533K | 78.6K | 11.5M | 2.1M | 62.4M | 10M | 2.9 M | 20.0 M |
dz | 1.9K | 1.9K | 191.7K | 191.7K | 1.1M | 1.1M | 22.7M | 22.7M | 10.0 M | 10.0 M |
kv | 59.1K | 1.9K | 584.3K | 88.8K | 9.5M | 1.2M | 91.4M | 9M | 4.4 M | 41.0 M |
msi | 686.7K | 1.9K | 686.7K | 22.6K | 414.8M | 440.4K | 2.6B | 2.7M | 1.1 M | 1.0 G |
ve | 3.8K | 1.9K | 97.8K | 79.4K | 3.2M | 2.1M | 19M | 11.7M | 3.8 M | 6.2 M |
zap | 5.5K | 1.8K | 202.3K | 93.5K | 4.2M | 1.8M | 26.4M | 11.4M | 4.0 M | 9.6 M |
zxx_xx_dtynoise | 118.8K | 1.8K | 3.8M | 49.3K | 118.8K | 1.8K | 501K | 6.6K | 3.9 M | 367.0 M |
meu | 5.9K | 1.7K | 232.1K | 72.6K | 4.2M | 1.4M | 27.2M | 8.6M | 2.6 M | 9.1 M |
iso | 3.7K | 1.7K | 155.8K | 111.5K | 4.4M | 2.7M | 23M | 13.7M | 4.9 M | 8.1 M |
ium | 100.3K | 1.7K | 6.2M | 54.9K | 48.4M | 1.7M | 314M | 7.4M | 2.6 M | 124.0 M |
nhe | 3K | 1.7K | 3K | 57.7K | 1.9M | 1.2M | 15.6M | 9.8M | 2.7 M | 4.8 M |
tyz | 8K | 1.7K | 454.8K | 104.6K | 7.5M | 1.9M | 46.3M | 11.3M | 3.8 M | 16.0 M |
hui | 2K | 1.7K | 80.1K | 74.7K | 1.8M | 1.7M | 11.8M | 10.9M | 3.0 M | 3.3 M |
new | 6.6K | 1.6K | 6.6K | 85K | 3.2M | 1.4M | 21.2M | 8.8M | 4.4 M | 10.6 M |
mdf | 71K | 1.6K | 394.7K | 45.1K | 8.3M | 670.1K | 65.8M | 5.5M | 2.5 M | 26.7 M |
pag | 49.6K | 1.6K | 49.6K | 88.8K | 13.8M | 1.9M | 92.9M | 12M | 3.9 M | 29.2 M |
gv | 501.9K | 1.6K | 18.8M | 26.9K | 137.7M | 996.2K | 933.1M | 6.2M | 2.0 M | 318.6 M |
gag | 33.9K | 1.6K | 491K | 37K | 10.2M | 661K | 84.9M | 5.2M | 2.1 M | 32.6 M |
ngu | 3.8K | 1.5K | 3.8K | 87.1K | 2.7M | 1.5M | 21.4M | 11.8M | 3.6 M | 6.7 M |
quc | 4.4K | 1.5K | 89.2K | 41.2K | 2.8M | 1.1M | 16.6M | 6.4M | 2.2 M | 5.9 M |
mam | 23K | 1.5K | 446.3K | 52.9K | 9.8M | 1.2M | 70.4M | 7.2M | 2.6 M | 30.7 M |
min | 28.2K | 1.5K | 500.9K | 75.6K | 10.2M | 1.4M | 70.5M | 9.9M | 2.6 M | 21.1 M |
ho | 2K | 1.5K | 57K | 47.8K | 1.8M | 1.3M | 12.3M | 7.8M | 1.9 M | 3.1 M |
pon | 5.7K | 1.5K | 167.8K | 48.7K | 3M | 1.1M | 18.3M | 6.7M | 2.1 M | 6.1 M |
mrj | 97.1K | 1.4K | 97.1K | 60.3K | 14.5M | 1.1M | 100.6M | 7.6M | 3.6 M | 40.8 M |
lu | 10.6K | 1.4K | 316K | 112.1K | 7.8M | 2.3M | 54.2M | 15.4M | 4.8 M | 18.0 M |
gom_Latn | 231.1K | 1.4K | 4.1M | 77.9K | 231.1K | 1.4K | 1M | 5.1K | 3.6 M | 240.6 M |
alt | 2.6K | 1.4K | 110.1K | 65.9K | 1.8M | 1.1M | 14.3M | 8.7M | 3.8 M | 6.4 M |
nzi | 2.5K | 1.4K | 2.5K | 71.8K | 2.5M | 1.7M | 14.4M | 9.4M | 3.1 M | 4.8 M |
tzo | 2.8K | 1.4K | 100.4K | 75.7K | 2.5M | 1.7M | 15.9M | 10.6M | 3.2 M | 4.9 M |
bci | 7.4K | 1.3K | 124.8K | 87.1K | 5M | 1.9M | 32.8M | 9M | 3.1 M | 9.4 M |
dtp | 4.6K | 1.3K | 51.2K | 7.9K | 1.9M | 419.4K | 12.7M | 3M | 1013.9 K | 4.5 M |
abt | 1.6K | 1.3K | 122.7K | 110.3K | 1.5M | 1.3M | 9.6M | 8.2M | 2.2 M | 2.7 M |
bbc | 72.3K | 1.3K | 718.3K | 73.2K | 21.7M | 1.7M | 151.3M | 10.6M | 3.6 M | 47.9 M |
pck | 8.9K | 1.3K | 8.9K | 69.7K | 6.8M | 2.1M | 39.8M | 11.5M | 4.2 M | 14.2 M |
mai | 54.3K | 1.2K | 1M | 60.2K | 24.6M | 1.2M | 156M | 6.8M | 3.6 M | 67.1 M |
mps | 2.7K | 1.2K | 132.8K | 71.9K | 2.8M | 1.6M | 16M | 8.7M | 2.3 M | 4.8 M |
emp | 3.6K | 1.2K | 106.4K | 75.4K | 1.9M | 999.1K | 14.5M | 7.4M | 2.4 M | 4.9 M |
mgh | 5.5K | 1.2K | 151.8K | 61.2K | 2.8M | 1.1M | 24.1M | 8.2M | 2.8 M | 8.3 M |
tab | 7.8K | 1.2K | 226.4K | 26.8K | 4.3M | 538.9K | 33.7M | 4.4M | 1.9 M | 15.7 M |
crh | 5.1K | 1.2K | 170.9K | 61.8K | 2.4M | 943K | 18.8M | 7.5M | 3.4 M | 8.9 M |
tbz | 5.1K | 1.1K | 128.7K | 37.5K | 3.5M | 893.4K | 22M | 4.8M | 1.9 M | 10.2 M |
ss | 8.1K | 1.1K | 8.1K | 30.4K | 2.7M | 568.3K | 23.7M | 5.5M | 1.8 M | 7.4 M |
chk | 2.8K | 1.1K | 98.8K | 44K | 2M | 1M | 12M | 5.8M | 1.8 M | 4.0 M |
bru | 3K | 1.1K | 89.7K | 48.2K | 2.4M | 938.1K | 12.9M | 4.8M | 1.5 M | 4.5 M |
nnb | 4.9K | 1.1K | 4.9K | 70.2K | 3.2M | 1.2M | 27.7M | 9.1M | 3.3 M | 10.0 M |
fon | 5.3K | 1.1K | 222.9K | 67.3K | 6.9M | 1.8M | 34M | 8.3M | 3.1 M | 14.8 M |
ppk | 2.6K | 1.1K | 85.8K | 34.9K | 1.9M | 801.8K | 13.2M | 5.5M | 1.6 M | 4.3 M |
tiv | 3.8K | 1.1K | 3.8K | 80.7K | 3.7M | 2.1M | 20.4M | 10.2M | 3.2 M | 6.0 M |
btx | 3.1K | 1K | 81.7K | 43.9K | 2M | 907.5K | 13.1M | 5.9M | 2.0 M | 4.6 M |
bg_Latn | 200.4K | 991 | 2.8M | 25.5K | 200.4K | 991 | 927.1K | 3.7K | 1.7 M | 143.6 M |
mbt | 1.6K | 969 | 86K | 45.4K | 2.4M | 1.3M | 14.6M | 7.5M | 2.2 M | 5.1 M |
ace | 65.5K | 966 | 632.5K | 32.5K | 19.9M | 1.1M | 146.1M | 7.4M | 2.2 M | 42.3 M |
tvl | 2.3K | 933 | 72.9K | 53.6K | 2.5M | 1.7M | 12.6M | 8.1M | 2.4 M | 3.8 M |
dov | 3.5K | 923 | 129.8K | 56.7K | 2.6M | 967.5K | 20.7M | 8M | 2.6 M | 7.1 M |
ach | 2K | 915 | 63K | 40.1K | 1.6M | 890.9K | 9M | 4.7M | 1.6 M | 3.0 M |
xal | 71.8K | 913 | 498.5K | 30.8K | 8.5M | 449.8K | 64.7M | 3.2M | 1.5 M | 24.4 M |
cuk | 4.1K | 899 | 76.5K | 34.3K | 2M | 469.9K | 24.7M | 4.6M | 1.5 M | 6.1 M |
kos | 2.2K | 881 | 44.6K | 27.8K | 1.1M | 780.1K | 6.5M | 4.2M | 1.4 M | 2.2 M |
crs | 7.6K | 873 | 282.4K | 40.1K | 7.3M | 1.2M | 40.1M | 6.8M | 2.2 M | 13.2 M |
wo | 36.4K | 871 | 303.4K | 25.4K | 30.7M | 850.7K | 213.4M | 4.5M | 1.7 M | 59.9 M |
bts | 3.2K | 869 | 109.1K | 29.1K | 3.1M | 663.3K | 20.8M | 4.2M | 1.4 M | 6.2 M |
ubu | 2.2K | 846 | 113.5K | 47.5K | 2.3M | 996.4K | 15.9M | 6.7M | 1.9 M | 4.7 M |
gym | 1.5K | 820 | 73.7K | 49.6K | 1.6M | 1.1M | 10.3M | 6.9M | 2.0 M | 3.2 M |
ibb | 74.1K | 818 | 516.5K | 36.3K | 26.4M | 776.1K | 190.9M | 4.9M | 1.5 M | 56.0 M |
ape | 7K | 814 | 147K | 56.1K | 12.4M | 881.5K | 71M | 5.8M | 1.6 M | 18.8 M |
stq | 111.9K | 809 | 111.9K | 27.7K | 34.4M | 600.4K | 243.1M | 3.8M | 1.5 M | 82.5 M |
ang | 66.5K | 803 | 1.8M | 86.7K | 28.5M | 1.7M | 193M | 9.8M | 3.4 M | 67.1 M |
enq | 7.1K | 793 | 241.9K | 39.1K | 11M | 718.8K | 68.5M | 4.8M | 1.3 M | 18.8 M |
tsg | 353.8K | 789 | 353.8K | 17.9K | 158M | 588.9K | 1.1B | 3.8M | 1.0 M | 309.9 M |
shn | 889 | 788 | 46.4K | 46.2K | 383.8K | 378.5K | 5.7M | 5.7M | 2.6 M | 2.6 M |
kri | 39.1K | 786 | 271.2K | 38.8K | 12.6M | 995.2K | 86.4M | 5M | 1.6 M | 20.9 M |
kek | 3.2K | 782 | 70.4K | 38.4K | 1.8M | 709K | 13.6M | 4.4M | 1.4 M | 4.7 M |
rmc | 2.4K | 738 | 2.4K | 25.8K | 1.3M | 545.4K | 7.9M | 3.2M | 1.1 M | 2.9 M |
acf | 4.9K | 730 | 81.9K | 24.6K | 2.1M | 602.2K | 11.6M | 3M | 1.1 M | 4.7 M |
fip | 3.7K | 729 | 165.6K | 49K | 3.5M | 916.8K | 25.7M | 6.6M | 2.1 M | 8.6 M |
syr | 3.5K | 716 | 326.4K | 197.1K | 4.6M | 1.9M | 31.5M | 14M | 6.1 M | 13.9 M |
qub | 972 | 705 | 61K | 51.1K | 589.2K | 455.5K | 5.9M | 4.4M | 1.4 M | 1.8 M |
bm | 21.9K | 702 | 172.3K | 24.5K | 7.1M | 583.1K | 48.4M | 3M | 1.1 M | 14.4 M |
tzh | 1.7K | 702 | 41.7K | 33.9K | 1.5M | 929.6K | 9.3M | 5.6M | 1.6 M | 2.6 M |
jiv | 1.7K | 696 | 80.9K | 32K | 1.1M | 418.9K | 9.6M | 3.5M | 1.1 M | 3.3 M |
kn_Latn | 72.9K | 688 | 765.9K | 10.1K | 72.9K | 688 | 328.1K | 2.5K | 430.8 K | 61.4 M |
kjh | 1.5K | 672 | 42.8K | 28.7K | 566.1K | 379.2K | 4.5M | 3.1M | 1.3 M | 2.0 M |
yap | 1.9K | 638 | 37.6K | 19.5K | 1.3M | 661.4K | 6.9M | 3.3M | 1.0 M | 2.2 M |
ban | 8K | 637 | 150.9K | 16.3K | 5M | 499.7K | 35.4M | 3.6M | 1.1 M | 12.0 M |
tuc | 3.5K | 635 | 193.2K | 50.3K | 2.9M | 703K | 17.2M | 4.1M | 1.2 M | 5.7 M |
tcy | 10.7K | 632 | 338.7K | 37.1K | 5.5M | 432.6K | 41.6M | 3.3M | 1.7 M | 20.9 M |
cab | 1.2K | 629 | 50.4K | 37.5K | 1M | 690.9K | 7.5M | 5.1M | 1.6 M | 2.4 M |
cak | 1.2K | 617 | 70.4K | 32.6K | 1.3M | 730.1K | 7.6M | 4.2M | 1.3 M | 2.4 M |
din | 128.4K | 611 | 885.8K | 23.6K | 31.6M | 541.7K | 210M | 2.9M | 1.1 M | 64.3 M |
zh_Latn | 739.4K | 602 | 10.7M | 45.1K | 739.4K | 602 | 3.4M | 2.3K | 2.0 M | 969.9 M |
arn | 2.4K | 593 | 64.5K | 26.2K | 1.5M | 541.9K | 10.2M | 3.7M | 1.2 M | 3.7 M |
lrc | 42.4K | 587 | 351.9K | 9K | 17.3M | 248.9K | 85.3M | 1.4M | 646.9 K | 37.5 M |
rwo | 938 | 572 | 938 | 45.5K | 734.8K | 590.4K | 5.1M | 4.2M | 1.1 M | 1.4 M |
hus | 825 | 569 | 26.5K | 23.7K | 733.4K | 542.1K | 4.4M | 3.1M | 967.6 K | 1.3 M |
bum | 4.7K | 559 | 103.8K | 36.5K | 3M | 805.5K | 18.8M | 4M | 1.3 M | 6.1 M |
mak | 1K | 555 | 32.5K | 20.4K | 761K | 457.4K | 6.1M | 3.7M | 1.1 M | 2.0 M |
frp | 148K | 550 | 3.5M | 8.2K | 71.2M | 230.2K | 535.4M | 1.4M | 518.3 K | 129.7 M |
seh | 5.6K | 545 | 68.8K | 37.2K | 2M | 650.6K | 14.9M | 4.9M | 1.5 M | 4.4 M |
twu | 2.5K | 539 | 109.9K | 24.4K | 2.4M | 571.2K | 14.2M | 3.2M | 1.0 M | 4.8 M |
kmb | 1.3K | 538 | 60.4K | 36.9K | 1.4M | 810.8K | 8.4M | 4.6M | 1.4 M | 2.6 M |
ksw | 560 | 536 | 16.1K | 16K | 219.9K | 218.8K | 2.9M | 2.9M | 1.4 M | 1.4 M |
sja | 1.3K | 527 | 67.7K | 24.9K | 982.5K | 459.3K | 7.7M | 3.4M | 1.1 M | 2.6 M |
amu | 1.8K | 511 | 72K | 25.2K | 1.5M | 443.3K | 9.6M | 3.2M | 1.0 M | 3.4 M |
mad | 103.8K | 509 | 500.6K | 18.5K | 16.2M | 386.7K | 111.8M | 2.8M | 960.3 K | 34.2 M |
quh | 1K | 501 | 42K | 29.9K | 624.4K | 396.8K | 5.8M | 3.7M | 1.2 M | 1.8 M |
dyu | 1.2K | 483 | 55.8K | 19.7K | 1.2M | 421.8K | 5.7M | 2M | 665.5 K | 1.9 M |
toj | 736 | 452 | 736 | 26.1K | 691.2K | 540.2K | 4.3M | 3.3M | 1.0 M | 1.3 M |
ch | 12.9K | 449 | 147.5K | 16K | 8.9M | 393.9K | 63.5M | 2.5M | 906.8 K | 10.0 M |
sus | 664 | 437 | 664 | 15.2K | 648K | 402.8K | 3.7M | 2.1M | 674.0 K | 1.0 M |
nog | 970 | 419 | 970 | 11K | 330.3K | 200.4K | 2.6M | 1.6M | 714.0 K | 1.2 M |
jam | 12.7K | 416 | 68.5K | 15.8K | 3.5M | 378.4K | 25.8M | 1.7M | 609.5 K | 7.6 M |
gui | 1.1K | 409 | 62.7K | 24.8K | 915K | 314K | 6.5M | 2M | 619.3 K | 2.1 M |
nia | 2K | 408 | 2K | 25K | 1.7M | 476.5K | 11.3M | 3.1M | 1.0 M | 3.9 M |
mas | 15.2K | 405 | 216.8K | 17.6K | 6.2M | 390.1K | 42.1M | 3M | 927.5 K | 13.4 M |
bzj | 983 | 404 | 33.6K | 26.4K | 824.3K | 565K | 4.5M | 2.9M | 981.2 K | 1.4 M |
mkn | 956 | 402 | 33.1K | 25.4K | 584.2K | 456.9K | 3.4M | 2.6M | 734.8 K | 1.0 M |
lhu | 46K | 377 | 975K | 15.7K | 29.1M | 441.2K | 208.6M | 2.5M | 623.0 K | 38.8 M |
ctu | 690 | 366 | 35.5K | 20.6K | 646.7K | 352.8K | 3.6M | 2M | 614.9 K | 1.2 M |
kg | 4.7K | 365 | 85.5K | 21.7K | 2.5M | 406.7K | 16.6M | 2.6M | 905.4 K | 5.7 M |
inb | 387 | 343 | 17.3K | 17K | 202.8K | 197K | 2M | 1.9M | 535.2 K | 555.6 K |
guh | 1.9K | 331 | 104.9K | 28.4K | 1.5M | 328.4K | 11.2M | 3M | 789.5 K | 3.5 M |
rn | 8.2K | 323 | 8.2K | 11.1K | 4.5M | 179K | 33.2M | 1.3M | 449.9 K | 11.8 M |
bus | 467 | 322 | 21.4K | 12.1K | 418.4K | 219.2K | 2.1M | 1.1M | 428.8 K | 830.9 K |
mfe | 7.5K | 320 | 198.8K | 18.2K | 4.6M | 374.8K | 26.9M | 2.1M | 716.4 K | 10.1 M |
sda | 1.6K | 317 | 43.2K | 6.2K | 2.5M | 218.3K | 15.8M | 1.6M | 529.0 K | 4.7 M |
bi | 71.9K | 311 | 308.5K | 13.6K | 19.4M | 359.4K | 132.4M | 1.9M | 546.9 K | 42.6 M |
cr_Latn | 19K | 303 | 170K | 8.9K | 19K | 303 | 81.8K | 1K | 590.4 K | 15.0 M |
gor | 1.7K | 303 | 53.3K | 6.5K | 1.4M | 227.1K | 9.4M | 1.7M | 494.0 K | 3.1 M |
jac | 8.2K | 303 | 61.6K | 11.9K | 1.8M | 271K | 15.7M | 1.7M | 530.3 K | 7.3 M |
chr | 964 | 301 | 33.8K | 7.5K | 629.9K | 172.3K | 4.7M | 1M | 564.1 K | 2.1 M |
mh | 4.6K | 296 | 235.1K | 13K | 3.6M | 393.5K | 24.9M | 2.2M | 778.4 K | 8.4 M |
mni | 1.2K | 290 | 38.1K | 13.2K | 841.3K | 245.5K | 6.4M | 1.8M | 866.6 K | 3.0 M |
wal | 2.6K | 286 | 128K | 14K | 2M | 203.4K | 17M | 1.7M | 525.7 K | 5.1 M |
teo | 2.8K | 274 | 131.5K | 13.7K | 2.3M | 221.4K | 15.3M | 1.6M | 564.9 K | 5.3 M |
gub | 31.7K | 271 | 160.4K | 25K | 4.7M | 286.2K | 44.7M | 1.6M | 431.3 K | 23.1 M |
qvi | 1.2K | 266 | 48.4K | 19.3K | 720.4K | 248.9K | 6.5M | 2.3M | 641.2 K | 1.9 M |
tdx | 1.7K | 262 | 26.3K | 13.2K | 1M | 238.5K | 7M | 1.6M | 503.6 K | 2.1 M |
rki | 331 | 251 | 331 | 7.8K | 119.7K | 113.7K | 1.6M | 1.5M | 751.3 K | 781.8 K |
djk | 560 | 246 | 30.9K | 24.4K | 669.5K | 455.6K | 3.7M | 2.2M | 644.3 K | 1.0 M |
nr | 10.7K | 246 | 10.7K | 11.3K | 5.3M | 162.5K | 49M | 1.5M | 519.7 K | 17.8 M |
zne | 1.3K | 239 | 61.9K | 21.3K | 1.4M | 504.6K | 8.2M | 2.8M | 882.3 K | 2.8 M |
izz | 423 | 237 | 21.7K | 14.5K | 382.8K | 194.5K | 2.1M | 1.1M | 382.2 K | 789.9 K |
noa | 902 | 234 | 902 | 11.5K | 821.1K | 243.9K | 5.2M | 1.6M | 534.3 K | 1.7 M |
bqc | 275 | 228 | 9.8K | 8.2K | 193K | 151.7K | 997K | 788.4K | 317.0 K | 408.1 K |
srm | 847 | 227 | 847 | 17.3K | 1.2M | 445.3K | 6.3M | 2M | 613.4 K | 1.7 M |
niq | 26.7K | 226 | 26.7K | 4.2K | 9.9M | 103.4K | 72.1M | 716.2K | 239.1 K | 20.9 M |
bas | 4.2K | 216 | 105.2K | 14.9K | 4.3M | 362.8K | 25.7M | 1.7M | 600.7 K | 7.6 M |
dwr | 452 | 215 | 22.1K | 11.1K | 269.4K | 139.5K | 2.2M | 1.2M | 375.4 K | 747.6 K |
guc | 537 | 214 | 22.9K | 12.5K | 422.4K | 218.1K | 3.4M | 1.8M | 540.1 K | 1.1 M |
jvn | 1K | 213 | 36.2K | 7.8K | 790.5K | 185.6K | 5.3M | 1.2M | 357.2 K | 1.7 M |
hvn | 737 | 200 | 33.9K | 7K | 779.7K | 239.4K | 4.3M | 1.2M | 378.5 K | 1.4 M |
sxn | 587 | 197 | 587 | 9.9K | 494K | 220.6K | 3.4M | 1.5M | 507.1 K | 1.2 M |
koi | 20.7K | 196 | 153.9K | 5K | 2.2M | 89.9K | 17.1M | 664.5K | 323.0 K | 7.1 M |
alz | 2.2K | 195 | 59.3K | 12.2K | 1.3M | 246.9K | 7.9M | 1.4M | 488.1 K | 2.9 M |
nyu | 1.2K | 195 | 1.2K | 11K | 988.7K | 210.5K | 7.7M | 1.6M | 492.6 K | 2.2 M |
bn_Latn | 98.7K | 191 | 1.3M | 12K | 98.7K | 191 | 458K | 730 | 314.7 K | 81.0 M |
suz | 226 | 186 | 226 | 11.3K | 169.6K | 140.5K | 1M | 855.2K | 339.5 K | 429.6 K |
pau | 1.7K | 185 | 1.7K | 13.1K | 2M | 394.6K | 12.4M | 2M | 600.1 K | 3.2 M |
nij | 1K | 183 | 1K | 9.2K | 741.6K | 186.1K | 4.7M | 1.2M | 389.6 K | 1.6 M |
sat_Latn | 39K | 183 | 39K | 5.5K | 39K | 183 | 183.8K | 601 | 276.1 K | 39.2 M |
gu_Latn | 58.2K | 179 | 688.4K | 5.4K | 58.2K | 179 | 260.8K | 673 | 241.0 K | 47.9 M |
msm | 520 | 177 | 520 | 8.6K | 410.8K | 190.5K | 2.5M | 1.1M | 339.7 K | 789.8 K |
maz | 585 | 170 | 21.3K | 8.2K | 452.9K | 174K | 2.9M | 951.7K | 304.7 K | 971.4 K |
qxr | 2.6K | 153 | 40.8K | 6.4K | 761.5K | 75.4K | 6.6M | 724K | 186.4 K | 1.9 M |
shp | 874 | 150 | 22.4K | 3.7K | 534.1K | 96.8K | 3.8M | 710.4K | 216.9 K | 1.2 M |
hne | 3K | 146 | 118.4K | 4.3K | 2.3M | 139.3K | 12M | 697K | 379.3 K | 6.5 M |
ktu | 3.3K | 144 | 115.5K | 7.8K | 3.2M | 196.9K | 18.5M | 1.1M | 300.1 K | 5.4 M |
laj | 6.5K | 144 | 61K | 6.4K | 2.4M | 140.1K | 15.8M | 730.5K | 233.5 K | 4.6 M |
pis | 1.1K | 139 | 62K | 7.2K | 1.3M | 136.8K | 7.7M | 764K | 212.7 K | 2.2 M |
mag | 631 | 138 | 62.6K | 22.1K | 2.1M | 544.2K | 10.7M | 2.6M | 1.4 M | 5.4 M |
gbm | 2.5K | 137 | 50.8K | 3.8K | 1.7M | 99.7K | 9.1M | 499.6K | 282.4 K | 4.5 M |
tzj | 471 | 136 | 11.1K | 7.3K | 299.9K | 150.8K | 1.9M | 884.2K | 272.0 K | 663.9 K |
oj | 2.5K | 135 | 2.5K | 1.6K | 1.2M | 35.9K | 9.6M | 337.1K | 117.6 K | 3.4 M |
ndc_ZW | 2.2K | 132 | 2.2K | 8.7K | 2.2K | 132 | 9.1K | 523 | 343.1 K | 2.2 M |
tks | 63.7K | 127 | 63.7K | 6.8K | 17.1M | 41.5K | 88.9M | 260.8K | 39.5 K | 33.0 M |
awa | 5.8K | 126 | 100.1K | 8.4K | 2.2M | 98.7K | 11.1M | 475K | 226.6 K | 5.8 M |
gvl | 37.9K | 126 | 213K | 6.9K | 21.1M | 161.1K | 141M | 789.2K | 257.8 K | 31.7 M |
knj | 229 | 126 | 10.1K | 9.2K | 202.6K | 171.8K | 1.1M | 855K | 253.1 K | 345.4 K |
spp | 733 | 123 | 733 | 5.8K | 902.7K | 141.8K | 4.4M | 682.5K | 217.8 K | 1.4 M |
mqy | 69.3K | 119 | 309K | 2.5K | 12.1M | 88.6K | 78.9M | 506.5K | 170.4 K | 16.3 M |
tca | 410 | 117 | 20K | 7.3K | 283K | 121.5K | 2.3M | 786K | 226.2 K | 781.2 K |
cce | 847 | 116 | 23.2K | 11K | 539.3K | 227.2K | 3.3M | 1.3M | 393.8 K | 1.1 M |
skr | 3.8K | 107 | 279.3K | 17.1K | 6.2M | 324K | 32.2M | 1.7M | 768.5 K | 15.4 M |
kmz_Latn | 24K | 106 | 361K | 2.4K | 24K | 106 | 108.6K | 401 | 231.8 K | 16.7 M |
dje | 913 | 100 | 40.2K | 3.7K | 816.3K | 97.5K | 4.7M | 480.7K | 161.2 K | 1.5 M |
gof | 2.8K | 97 | 33.8K | 5.5K | 703K | 68.8K | 5.5M | 506K | 159.1 K | 1.7 M |
agr | 465 | 93 | 16.1K | 3.6K | 295.4K | 67.2K | 2.3M | 554.5K | 177.0 K | 760.1 K |
qvz | 534 | 88 | 6.8K | 3.5K | 145.5K | 50.5K | 1.2M | 438.3K | 124.2 K | 382.7 K |
adh | 2.6K | 87 | 107.2K | 1K | 2.4M | 42.1K | 14.5M | 254.9K | 84.6 K | 5.0 M |
quf | 522 | 86 | 8.4K | 5.2K | 155.7K | 61.8K | 1.5M | 609K | 173.7 K | 542.8 K |
kjg | 113 | 84 | 3K | 2.9K | 67.6K | 67K | 408.5K | 399K | 159.2 K | 167.7 K |
tsc | 12.6K | 82 | 12.6K | 4K | 3.5M | 93.1K | 23.4M | 521.3K | 161.9 K | 7.0 M |
ber | 2.7K | 79 | 12.6K | 1.2K | 1.1M | 46.4K | 6.4M | 265.9K | 141.5 K | 3.0 M |
ify | 611 | 79 | 19.8K | 2.8K | 422.7K | 56.2K | 2.6M | 334K | 109.5 K | 913.1 K |
cbk | 10.1K | 78 | 43.8K | 2K | 1.7M | 64.3K | 10.3M | 339.3K | 93.4 K | 3.4 M |
quy | 588 | 78 | 28.1K | 2.7K | 423.3K | 37.3K | 4.5M | 368.2K | 114.5 K | 1.2 M |
ahk | 244 | 77 | 6.2K | 4.1K | 264K | 124.8K | 1.3M | 715.5K | 182.8 K | 359.7 K |
cac | 212 | 77 | 3.4K | 1.8K | 125.7K | 54.1K | 978.7K | 319.8K | 95.8 K | 280.3 K |
akb | 1K | 71 | 21.3K | 408 | 870.9K | 54.5K | 5.2M | 337.8K | 93.7 K | 1.6 M |
nut | 29K | 67 | 29K | 1.5K | 4.8M | 39.8K | 23.5M | 184.1K | 36.4 K | 8.3 M |
ffm | 1.8K | 65 | 30.1K | 2K | 745.6K | 39.1K | 4.6M | 236.1K | 83.8 K | 1.8 M |
taj | 146 | 65 | 21.6K | 14.3K | 309.7K | 203K | 2.3M | 1.4M | 503.0 K | 872.7 K |
ms_Arab | 698 | 63 | 698 | 320 | 698 | 63 | 2.9K | 239 | 64.7 K | 1016.0 K |
brx | 322 | 62 | 5.3K | 2.4K | 144.2K | 41K | 1.1M | 304.4K | 146.6 K | 515.7 K |
ann | 464 | 56 | 5K | 1.6K | 116.4K | 35.9K | 760.9K | 215.1K | 74.9 K | 295.2 K |
qup | 169 | 53 | 4.3K | 2.5K | 77.5K | 31.3K | 763.8K | 297.8K | 74.7 K | 207.3 K |
ms_Arab_BN | 2.6K | 46 | 2.6K | 374 | 2.6K | 46 | 10.5K | 171 | 50.0 K | 5.1 M |
miq | 236 | 45 | 6.4K | 3.5K | 183.7K | 80.2K | 1.2M | 485.6K | 157.6 K | 384.1 K |
msb | 811 | 41 | 811 | 1K | 705.9K | 28.8K | 4.4M | 167.5K | 53.3 K | 1.7 M |
bim | 410 | 40 | 31.1K | 6.3K | 669.8K | 167.4K | 3.2M | 793.4K | 252.7 K | 1.1 M |
raj | 1.8K | 40 | 1.8K | 5.7K | 1.3M | 81.1K | 7.1M | 405K | 226.2 K | 3.9 M |
kwi | 382 | 37 | 16.9K | 2.2K | 253.8K | 23.4K | 1.8M | 172.8K | 47.6 K | 536.2 K |
tll | 200 | 37 | 200 | 2.7K | 304.2K | 62.2K | 2.2M | 409.8K | 132.3 K | 664.5 K |
trp | 12.8K | 36 | 12.8K | 1.7K | 4.1M | 39K | 29.9M | 257.3K | 87.5 K | 10.2 M |
smt | 1.4K | 34 | 1.4K | 703 | 1M | 36.5K | 6.8M | 245.4K | 87.9 K | 2.5 M |
mrw | 11.3K | 29 | 11.3K | 1K | 4.2M | 45.7K | 27.8M | 257.2K | 81.3 K | 8.8 M |
dln | 236 | 28 | 5.2K | 969 | 150.8K | 21.5K | 860.5K | 118.3K | 36.8 K | 280.3 K |
qvc | 3.4K | 27 | 14.6K | 2.2K | 495.7K | 25.7K | 5M | 233.7K | 65.3 K | 2.6 M |
doi | 1.7K | 26 | 21.8K | 975 | 568.7K | 25.5K | 3.2M | 135.3K | 66.7 K | 1.6 M |
ff | 13.6K | 26 | 150K | 5K | 3.4M | 46.5K | 22.8M | 277.6K | 78.8 K | 8.5 M |
## Citation Information
~~~
@misc{kudugunta2023madlad400,
title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset},
author={Sneha Kudugunta and Isaac Caswell and Biao Zhang and Xavier Garcia and Christopher A. Choquette-Choo and Katherine Lee and Derrick Xin and Aditya Kusupati and Romi Stella and Ankur Bapna and Orhan Firat},
year={2023},
eprint={2309.04662},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
~~~
|
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ShengbinYue/DISC-Law-SFT
|
ShengbinYue
| 2023-09-25T14:47:18Z | 69 | 33 |
[
"size_categories:100M<n<1B",
"language:zh",
"license:apache-2.0",
"legal",
"arxiv:2309.11325",
"region:us"
] | null | 2023-09-23T07:56:07Z |
---
language:
- zh
tags:
- legal
size_categories:
- 100M<n<1B
license: apache-2.0
---
# DISC-Law-SFT Dataset
Legal Intelligent systems in Chinese require a combination of various abilities, including legal text understanding and generation. To achieve this, we have constructed a high-quality supervised fine-tuning dataset called DISC-Law-SFT, which covers different legal scenarios such as legal information extraction, legal judgment prediction, legal document summarization, and legal question answering. DISC-Law-SFT comprises two subsets, DISC-Law-SFT-Pair and DISC-Law-SFT-Triplet. The former aims to introduce legal reasoning abilities to the LLM, while the latter helps enhance the model's capability to utilize external legal knowledge. For more detailed information, please refer to our [technical report](https://arxiv.org/abs/2309.11325). The distribution of the dataset is:
<img src="" alt="" width=""/>
<table>
<tr>
<th>Dataset</th>
<th>Task/Source</th>
<th>Size</th>
<th>Scenario</th>
</tr>
<tr>
<td rowspan="10">DISC-Law-SFT-Pair</td>
<td>Legal information extraction</td>
<td>32K</td>
<td rowspan="7">Legal professional assistant</td>
</tr>
<tr>
<td>Legal event detection</td>
<td>27K</td>
</tr>
<tr>
<td>Legal case classification</td>
<td>20K</td>
</tr>
<tr>
<td>Legal judgement prediction</td>
<td>11K</td>
</tr>
<tr>
<td>Legal case matching</td>
<td>8K</td>
</tr>
<tr>
<td>Legal text summarization</td>
<td>9K</td>
</tr>
<tr>
<td>Judicial public opinion summarization</td>
<td>6K</td>
</tr>
<tr>
<td>Legal question answering</td>
<td>93K</td>
<td>Legal consultation services</td>
</tr>
<tr>
<td>Legal reading comprehension</td>
<td>38K</td>
<td rowspan="2">Judicial examination assistant</td>
</tr>
<tr>
<td>Judicial examination</td>
<td>12K</td>
</tr>
<tr>
<td rowspan="2">DISC-Law-SFT-Triple</td>
<td>Legal judgement prediction</td>
<td>16K</td>
<td>Legal professional assistant</td>
</tr>
<tr>
<td>Legal question answering</td>
<td>23K</td>
<td>Legal consultation services</td>
</tr>
<tr>
<td rowspan="2">General</td>
<td>Alpaca-GPT4</td>
<td>48K</td>
<td rowspan="2">General scenarios</td>
</tr>
<tr>
<td>Firefly</td>
<td>60K</td>
</tr>
<tr>
<td>Total</td>
<td colspan="3">403K</td>
</tr>
</table>
We currently open-source most of the DISC-Law-SFT Dataset.
More detail and news check our [homepage](https://github.com/FudanDISC/DISC-LawLLM) !
|
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CollectiveCognition/chats-data-2023-09-27
|
CollectiveCognition
| 2023-09-28T00:40:51Z | 69 | 16 |
[
"license:mit",
"region:us"
] | null | 2023-09-28T00:39:17Z |
---
license: mit
---
# Dataset Card for "Collective Cognition ChatGPT Conversations"
## 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)
## Dataset Description
### Dataset Summary
The "Collective Cognition ChatGPT Conversations" dataset is a collection of chat logs between users and the ChatGPT model. These conversations have been shared by users on the "Collective Cognition" website. The dataset provides insights into user interactions with language models and can be utilized for multiple purposes, including training, research, and analysis.
Visit Collective Cognition to browse or contribute chats here: https://collectivecognition.ai
### Supported Tasks
- **Language Model Finetuning**: Training through behavior cloning and knowledge instilling.
- **Dialogue Understanding**: Analyzing the flow and structure of conversations.
- **Sentiment Analysis**: Understanding the sentiment or emotion in the dialogues.
- **Topic Modeling**: Determining the topics discussed within the chats.
### Languages
The primary language of the dataset is English, but any language chat may be present in the dataset as users share more chats.
## Dataset Structure
### Data Instances
A typical data instance includes a chat log with a title, model name, whether the chat used custom instructions (currently not included if so), and the content of the message with the role of the sender (either "human" or "gpt").
The dataset is formatted to conform with ShareGPT standard.
This is an example entry in the dataset, noting that turns are variable and can be many or as few as two:
```json
{
"title": "Interest Formulas Clarification",
"model_name": "GPT-4",
"custom_instruction": false,
"conversations": [
{
"from": "human",
"value": "Is this correct? The formula for calculating simple interest (also known as Compound Interest) is given by I = P * r^n, where \n- I represents the final amount of money, also called principal or capital. This value will keep on increasing at a fixed rate per unit time denoted by 'r' and number of times denoted by 'n'. The initial investment represented by 'P', while the percentage rate expressed as an annual fraction, i.e., p/100 representing the percentage to be paid annually. In other words it\u2019s how much you earn in one year out of your original $100 invested with a 5% annual return compounded monthly."
},
{
"from": "assistant",
"value": "I'm sorry, but there seems to be some confusion in your question. The formula given and the definitions of the variables seem to be mixed up between simple interest and compound interest. Let me clarify:\n\n1. Simple Interest: This type of interest is calculated only on the original amount (principal) that you invested or loaned. The formula for simple interest is I = PRT, where:\n - I is the interest\n - P is the principal amount (initial investment)\n - R is the annual interest rate (in decimal form, so a 5% rate would be 0.05)\n - T is the time the money is invested or borrowed for, in years.\n\n2. Compound Interest: This type of interest is calculated on the initial principal and also on the accumulated interest of previous periods of a deposit or loan. The formula for compound interest is A = P(1 + r/n)^(nt), where:\n - A is the amount of money accumulated after n years, including interest.\n - P is the principal amount (the initial amount of money)\n - r is the annual interest rate (in decimal form, so a 5% rate would be 0.05)\n - n is the number of times that interest is compounded per year\n - t is the number of years the money is invested or borrowed for.\n\nIn both cases, the interest rate is usually expressed as an annual rate, regardless of the length of the term."
}
]
}
```
### Data Splits
Currently, the dataset is not divided into specific splits (train, test, validation).
## Dataset Creation
### Curation Rationale
The dataset was curated to provide insights into how users interact with language models and to contribute to the broader NLP community's resources.
### Source Data
The data originates from user contributions on the "Collective Cognition" website.
### Personal and Sensitive Information
All chats uploaded to the Collective Cognition website are made public, and are uploaded as a new dataset periodically. If you would like to have your chat removed, please email [email protected]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset offers a glimpse into the interaction dynamics between humans and AI models. It can be instrumental for researchers studying human-AI collaboration.
### Discussion of Biases
There might be biases in the dataset based on the types of users contributing chat logs and the topics they discuss with ChatGPT, particularly centered around what users may utilize ChatGPT for the most.
### Other Known Limitations
The dataset is dependent on the voluntary contributions of users. Hence, it might not represent the entire spectrum of interactions that users have with ChatGPT.
## Additional Information
### Licensing Information
MIT
|
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open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2
|
open-llm-leaderboard
| 2023-10-25T04:08:32Z | 69 | 0 |
[
"region:us"
] | null | 2023-10-09T15:58:17Z |
---
pretty_name: Evaluation run of caisarl76/Mistral-7B-guanaco1k-ep2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [caisarl76/Mistral-7B-guanaco1k-ep2](https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T04:08:20.324415](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2/blob/main/results_2023-10-25T04-08-20.324415.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002307046979865772,\n\
\ \"em_stderr\": 0.0004913221265094507,\n \"f1\": 0.06542994966442944,\n\
\ \"f1_stderr\": 0.001488633695023099,\n \"acc\": 0.4501858873976542,\n\
\ \"acc_stderr\": 0.010287740882080417\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094507,\n\
\ \"f1\": 0.06542994966442944,\n \"f1_stderr\": 0.001488633695023099\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1197877179681577,\n \
\ \"acc_stderr\": 0.008944213403553055\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7805840568271507,\n \"acc_stderr\": 0.01163126836060778\n\
\ }\n}\n```"
repo_url: https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|arc:challenge|25_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T04_08_20.324415
path:
- '**/details_harness|drop|3_2023-10-25T04-08-20.324415.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T04-08-20.324415.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T04_08_20.324415
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-08-20.324415.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-08-20.324415.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hellaswag|10_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-09T15-57-53.203212.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-09T15-57-53.203212.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-09T15-57-53.203212.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T04_08_20.324415
path:
- '**/details_harness|winogrande|5_2023-10-25T04-08-20.324415.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T04-08-20.324415.parquet'
- config_name: results
data_files:
- split: 2023_10_09T15_57_53.203212
path:
- results_2023-10-09T15-57-53.203212.parquet
- split: 2023_10_25T04_08_20.324415
path:
- results_2023-10-25T04-08-20.324415.parquet
- split: latest
path:
- results_2023-10-25T04-08-20.324415.parquet
---
# Dataset Card for Evaluation run of caisarl76/Mistral-7B-guanaco1k-ep2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [caisarl76/Mistral-7B-guanaco1k-ep2](https://huggingface.co/caisarl76/Mistral-7B-guanaco1k-ep2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T04:08:20.324415](https://huggingface.co/datasets/open-llm-leaderboard/details_caisarl76__Mistral-7B-guanaco1k-ep2/blob/main/results_2023-10-25T04-08-20.324415.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06542994966442944,
"f1_stderr": 0.001488633695023099,
"acc": 0.4501858873976542,
"acc_stderr": 0.010287740882080417
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094507,
"f1": 0.06542994966442944,
"f1_stderr": 0.001488633695023099
},
"harness|gsm8k|5": {
"acc": 0.1197877179681577,
"acc_stderr": 0.008944213403553055
},
"harness|winogrande|5": {
"acc": 0.7805840568271507,
"acc_stderr": 0.01163126836060778
}
}
```
### 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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worden1/ultra-feedback-paired
|
worden1
| 2023-10-24T04:21:21Z | 69 | 1 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-10-24T04:00:41Z |
---
task_categories:
- question-answering
- text-generation
language:
- en
pretty_name: ultra_feedback_paired
size_categories:
- 10M<n<100M
---
|
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orkidea/palabrero-guc-draft
|
orkidea
| 2023-10-28T18:57:13Z | 69 | 0 |
[
"region:us"
] | null | 2023-10-27T21:19:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 62556423.0
num_examples: 17
download_size: 60689485
dataset_size: 62556423.0
---
# Dataset Card for "palabrero-guc-draft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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