# Dataset Card for No Language Left Behind (NLLB - 200vo)
## 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:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper: https://arxiv.org/pdf/2207.04672.pdf**
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset was created based on [metadata](https://github.com/facebookresearch/fairseq/tree/nllb) for mined bitext released by Meta AI. It contains bitext for 148 English-centric and 1465 non-English-centric language pairs using the stopes mining library and the LASER3 encoders Heffernan et al. (2022).
#### How to use the data
There are two ways to access the data:
* Via the Hugging Face Python datasets library
```
from datasets import load_dataset
dataset = load_dataset("allenai/nllb")
```
* Clone the git repo
```
git lfs install
git clone https://huggingface.co/datasets/allenai/nllb
```
### Supported Tasks and Leaderboards
NA
### Languages
Language pairs can be found [here](https://huggingface.co/datasets/allenai/nllb/blob/main/nllb_lang_pairs.py).
## Dataset Structure
The dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.
### Data Instances
[More Information Needed]
### Data Fields
Every instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser_score', 'source_sentence_lid', 'target_sentence_lid', where 'lid' is language classification probability, 'source_sentence_source', 'source_sentence_url', 'target_sentence_source', 'target_sentence_url'.
* Sentence in first language
* Sentence in second language
* LASER score
* Language ID score for first sentence
* Language ID score for second sentence
* First sentence source (https://github.com/facebookresearch/LASER/tree/main/data/nllb200)
* First sentence URL if the source is crawl-data/\*; _ otherwise
* Second sentence source
* Second sentence URL if the source is crawl-data/\*; _ otherwise
Example:
```
{'translation': {'ace_Latn': 'Gobnyan hana geupeukeucewa gata atawa geutinggai meunan mantong gata."',
'ban_Latn': 'Ida nenten jaga manggayang wiadin ngutang semeton."'},
'laser_score': 1.2499876022338867,
'source_sentence_lid': 1.0000100135803223,
'target_sentence_lid': 0.9991400241851807,
'source_sentence_source': 'paracrawl9_hieu',
'source_sentence_url': '_',
'target_sentence_source': 'crawl-data/CC-MAIN-2020-10/segments/1581875144165.4/wet/CC-MAIN-20200219153707-20200219183707-00232.warc.wet.gz',
'target_sentence_url': 'https://alkitab.mobi/tb/Ula/31/6/\n'}
```
### Data Splits
The data is not split. Given the noisy nature of the overall process, we recommend using the data only for training and use other datasets like [Flores-200](https://github.com/facebookresearch/flores) for the evaluation.
## Dataset Creation
### Curation Rationale
Data was filtered based on language identification, emoji based filtering, and for some high-resource languages language model-based filtering. For more details on data filtering please refer to Section 5.2 (NLLB Team et al., 2022).
### Source Data
#### Initial Data Collection and Normalization
Monolingual data was collected from the following sources:
| Name in data | Source |
|------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| afriberta | https://github.com/castorini/afriberta |
| americasnlp | https://github.com/AmericasNLP/americasnlp2021/ |
| bho_resources | https://github.com/shashwatup9k/bho-resources |
| crawl-data/* | WET files from https://commoncrawl.org/the-data/get-started/ |
| emcorpus | http://lepage-lab.ips.waseda.ac.jp/en/projects/meiteilon-manipuri-language-resources/ |
| fbseed20220317 | https://github.com/facebookresearch/flores/tree/main/nllb_seed |
| giossa_mono | https://github.com/sgongora27/giossa-gongora-guarani-2021 |
| iitguwahati | https://github.com/priyanshu2103/Sanskrit-Hindi-Machine-Translation/tree/main/parallel-corpus |
| indic | https://indicnlp.ai4bharat.org/corpora/ |
| lacunaner | https://github.com/masakhane-io/lacuna_pos_ner/tree/main/language_corpus |
| leipzig | Community corpora from https://wortschatz.uni-leipzig.de/en/download for each year available |
| lowresmt2020 | https://github.com/panlingua/loresmt-2020 |
| masakhanener | https://github.com/masakhane-io/masakhane-ner/tree/main/MasakhaNER2.0/data |
| nchlt | https://repo.sadilar.org/handle/20.500.12185/299
https://repo.sadilar.org/handle/20.500.12185/302
https://repo.sadilar.org/handle/20.500.12185/306
https://repo.sadilar.org/handle/20.500.12185/308
https://repo.sadilar.org/handle/20.500.12185/309
https://repo.sadilar.org/handle/20.500.12185/312
https://repo.sadilar.org/handle/20.500.12185/314
https://repo.sadilar.org/handle/20.500.12185/315
https://repo.sadilar.org/handle/20.500.12185/321
https://repo.sadilar.org/handle/20.500.12185/325
https://repo.sadilar.org/handle/20.500.12185/328
https://repo.sadilar.org/handle/20.500.12185/330
https://repo.sadilar.org/handle/20.500.12185/332
https://repo.sadilar.org/handle/20.500.12185/334
https://repo.sadilar.org/handle/20.500.12185/336
https://repo.sadilar.org/handle/20.500.12185/337
https://repo.sadilar.org/handle/20.500.12185/341
https://repo.sadilar.org/handle/20.500.12185/343
https://repo.sadilar.org/handle/20.500.12185/346
https://repo.sadilar.org/handle/20.500.12185/348
https://repo.sadilar.org/handle/20.500.12185/353
https://repo.sadilar.org/handle/20.500.12185/355
https://repo.sadilar.org/handle/20.500.12185/357
https://repo.sadilar.org/handle/20.500.12185/359
https://repo.sadilar.org/handle/20.500.12185/362
https://repo.sadilar.org/handle/20.500.12185/364 |
| paracrawl-2022-* | https://data.statmt.org/paracrawl/monolingual/ |
| paracrawl9* | https://paracrawl.eu/moredata the monolingual release |
| pmi | https://data.statmt.org/pmindia/ |
| til | https://github.com/turkic-interlingua/til-mt/tree/master/til_corpus |
| w2c | https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0022-6133-9 |
| xlsum | https://github.com/csebuetnlp/xl-sum |
#### Who are the source language producers?
Text was collected from the web and various monolingual data sets, many of which are also web crawls. This may have been written by people, generated by templates, or in some cases be machine translation output.
### Annotations
#### Annotation process
Parallel sentences in the monolingual data were identified using LASER3 encoders. (Heffernan et al., 2022)
#### Who are the annotators?
The data was not human annotated.
### Personal and Sensitive Information
Data may contain personally identifiable information, sensitive or toxic content that was publicly shared on the Internet.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides data for training machine learning systems for many languages that have low resources available for NLP.
### Discussion of Biases
Biases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques: lower resource languages may have lower accuracy while data filtering techniques may remove certain less natural utterances.
### Other Known Limitations
Some of the translations are in fact machine translation. Indeed, some sites have evidence of WordPress translation plugins in their HTML source. These sites were not filtered out en mass because HTML source was not available in many cases.
## Additional Information
### Dataset Curators
The data was not curated.
### Licensing Information
The dataset is released under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound to the respective Terms of Use and License of the original source.
### Citation Information
NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022.
### Contributions
We thank the AllenNLP team at AI2 for hosting and releasing this data, including [Akshita Bhagia](https://akshitab.github.io/) (for engineering efforts to create the huggingface dataset), and [Jesse Dodge](https://jessedodge.github.io/) (for organizing the connection).