Dataset Card for No Language Left Behind (NLLB - 200vo)
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
This dataset was created based on metadata 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
[More Information Needed]
Languages
[More Information Needed]
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
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 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
The monolingual data is from Common Crawl and ParaCrawl.
Who are the source language producers?
The source language was produced by writers of each website that have been crawled by Common Crawl and ParaCrawl.
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
The data in CommonCrawl and ParaCrawl 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
[More Information Needed]
Additional Information
Dataset Curators
The data was not curated.
Licensing Information
The dataset is released under the terms of ODC-BY. By using this, you are also bound by the Internet Archive Terms of Use in respect of the content contained in the dataset.
Citation Information
NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022.
Contributions
Thanks to @akshitab for adding this dataset.