albertvillanova
HF staff
Convert dataset sizes from base 2 to base 10 in the dataset card (#1)
3a54beb
metadata
annotations_creators:
- crowdsourced
language:
- az
- be
- en
- es
- fr
- gl
- he
- it
- pt
- ru
- tr
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: TEDHrlr
size_categories:
- 1M<n<10M
source_datasets:
- extended|ted_talks_iwslt
task_categories:
- translation
task_ids: []
paperswithcode_id: null
dataset_info:
- config_name: az_to_en
features:
- name: translation
dtype:
translation:
languages:
- az
- en
splits:
- name: test
num_bytes: 186540
num_examples: 904
- name: train
num_bytes: 1226853
num_examples: 5947
- name: validation
num_bytes: 122709
num_examples: 672
download_size: 131005909
dataset_size: 1536102
- config_name: aztr_to_en
features:
- name: translation
dtype:
translation:
languages:
- az_tr
- en
splits:
- name: test
num_bytes: 186540
num_examples: 904
- name: train
num_bytes: 39834469
num_examples: 188397
- name: validation
num_bytes: 122709
num_examples: 672
download_size: 131005909
dataset_size: 40143718
- config_name: be_to_en
features:
- name: translation
dtype:
translation:
languages:
- be
- en
splits:
- name: test
num_bytes: 186606
num_examples: 665
- name: train
num_bytes: 1176899
num_examples: 4510
- name: validation
num_bytes: 59328
num_examples: 249
download_size: 131005909
dataset_size: 1422833
- config_name: beru_to_en
features:
- name: translation
dtype:
translation:
languages:
- be_ru
- en
splits:
- name: test
num_bytes: 186606
num_examples: 665
- name: train
num_bytes: 59953616
num_examples: 212615
- name: validation
num_bytes: 59328
num_examples: 249
download_size: 131005909
dataset_size: 60199550
- config_name: es_to_pt
features:
- name: translation
dtype:
translation:
languages:
- es
- pt
splits:
- name: test
num_bytes: 343640
num_examples: 1764
- name: train
num_bytes: 8611393
num_examples: 44939
- name: validation
num_bytes: 181535
num_examples: 1017
download_size: 131005909
dataset_size: 9136568
- config_name: fr_to_pt
features:
- name: translation
dtype:
translation:
languages:
- fr
- pt
splits:
- name: test
num_bytes: 311650
num_examples: 1495
- name: train
num_bytes: 8755387
num_examples: 43874
- name: validation
num_bytes: 212317
num_examples: 1132
download_size: 131005909
dataset_size: 9279354
- config_name: gl_to_en
features:
- name: translation
dtype:
translation:
languages:
- gl
- en
splits:
- name: test
num_bytes: 193213
num_examples: 1008
- name: train
num_bytes: 1961363
num_examples: 10018
- name: validation
num_bytes: 137929
num_examples: 683
download_size: 131005909
dataset_size: 2292505
- config_name: glpt_to_en
features:
- name: translation
dtype:
translation:
languages:
- gl_pt
- en
splits:
- name: test
num_bytes: 193213
num_examples: 1008
- name: train
num_bytes: 11734254
num_examples: 61803
- name: validation
num_bytes: 137929
num_examples: 683
download_size: 131005909
dataset_size: 12065396
- config_name: he_to_pt
features:
- name: translation
dtype:
translation:
languages:
- he
- pt
splits:
- name: test
num_bytes: 361378
num_examples: 1624
- name: train
num_bytes: 10627615
num_examples: 48512
- name: validation
num_bytes: 230725
num_examples: 1146
download_size: 131005909
dataset_size: 11219718
- config_name: it_to_pt
features:
- name: translation
dtype:
translation:
languages:
- it
- pt
splits:
- name: test
num_bytes: 324726
num_examples: 1670
- name: train
num_bytes: 8905825
num_examples: 46260
- name: validation
num_bytes: 210375
num_examples: 1163
download_size: 131005909
dataset_size: 9440926
- config_name: pt_to_en
features:
- name: translation
dtype:
translation:
languages:
- pt
- en
splits:
- name: test
num_bytes: 347803
num_examples: 1804
- name: train
num_bytes: 9772911
num_examples: 51786
- name: validation
num_bytes: 207960
num_examples: 1194
download_size: 131005909
dataset_size: 10328674
- config_name: ru_to_en
features:
- name: translation
dtype:
translation:
languages:
- ru
- en
splits:
- name: test
num_bytes: 1459576
num_examples: 5477
- name: train
num_bytes: 58778442
num_examples: 208107
- name: validation
num_bytes: 1318357
num_examples: 4806
download_size: 131005909
dataset_size: 61556375
- config_name: ru_to_pt
features:
- name: translation
dtype:
translation:
languages:
- ru
- pt
splits:
- name: test
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num_examples: 1589
- name: train
num_bytes: 11882860
num_examples: 47279
- name: validation
num_bytes: 276866
num_examples: 1185
download_size: 131005909
dataset_size: 12568788
- config_name: tr_to_en
features:
- name: translation
dtype:
translation:
languages:
- tr
- en
splits:
- name: test
num_bytes: 1026406
num_examples: 5030
- name: train
num_bytes: 38607636
num_examples: 182451
- name: validation
num_bytes: 832358
num_examples: 4046
download_size: 131005909
dataset_size: 40466400
Dataset Card for "ted_hrlr"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository: https://github.com/neulab/word-embeddings-for-nmt
- Paper: When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 1.83 GB
- Size of the generated dataset: 281.66 MB
- Total amount of disk used: 2.12 GB
Dataset Summary
Data sets derived from TED talk transcripts for comparing similar language pairs where one is high resource and the other is low resource.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
az_to_en
- Size of downloaded dataset files: 131.01 MB
- Size of the generated dataset: 1.53 MB
- Total amount of disk used: 132.54 MB
An example of 'train' looks as follows.
{
"translation": {
"az": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
aztr_to_en
- Size of downloaded dataset files: 131.01 MB
- Size of the generated dataset: 40.14 MB
- Total amount of disk used: 171.15 MB
An example of 'train' looks as follows.
{
"translation": {
"az_tr": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
be_to_en
- Size of downloaded dataset files: 131.01 MB
- Size of the generated dataset: 1.43 MB
- Total amount of disk used: 132.42 MB
An example of 'train' looks as follows.
{
"translation": {
"be": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
beru_to_en
- Size of downloaded dataset files: 131.01 MB
- Size of the generated dataset: 60.20 MB
- Total amount of disk used: 191.21 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"translation": "{\"be_ru\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"en\": \"when i was..."
}
es_to_pt
- Size of downloaded dataset files: 131.01 MB
- Size of the generated dataset: 9.13 MB
- Total amount of disk used: 140.14 MB
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"translation": "{\"es\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"pt\": \"when i was 11..."
}
Data Fields
The data fields are the same among all splits.
az_to_en
translation
: a multilingualstring
variable, with possible languages includingaz
,en
.
aztr_to_en
translation
: a multilingualstring
variable, with possible languages includingaz_tr
,en
.
be_to_en
translation
: a multilingualstring
variable, with possible languages includingbe
,en
.
beru_to_en
translation
: a multilingualstring
variable, with possible languages includingbe_ru
,en
.
es_to_pt
translation
: a multilingualstring
variable, with possible languages includinges
,pt
.
Data Splits
name | train | validation | test |
---|---|---|---|
az_to_en | 5947 | 672 | 904 |
aztr_to_en | 188397 | 672 | 904 |
be_to_en | 4510 | 249 | 665 |
beru_to_en | 212615 | 249 | 665 |
es_to_pt | 44939 | 1017 | 1764 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{qi-etal-2018-pre,
title = "When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?",
author = "Qi, Ye and
Sachan, Devendra and
Felix, Matthieu and
Padmanabhan, Sarguna and
Neubig, Graham",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2084",
doi = "10.18653/v1/N18-2084",
pages = "529--535",
}
Contributions
Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.