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Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages

Introduction

This repository contains information about Glot500 model, data, and code.

• Glot500-m is an extended version of XLM-R-base, covering more than 500 languages compared to XLM-R's 104 languages. Glot500-m is available at huggingface-models.

• Glot2000-c comprises corpora for over 2000 languages, while Glot500-c is a subset of Glot2000-c for over 500 languages, including languages with more than 30,000 sentences.

  • Glot500-c dataset (the part that we can redistribute) is available at huggingface-dataset. For a more complete version of the data, you need to fill the data request form.

Glot500-m

You can use this model directly with a pipeline for masked language modeling:

>>> ! pip install transformers
>>> ! pip install sentencepiece

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='cis-lmu/glot500-base')
>>> unmasker("Hello I'm a <mask> model.")

Here is how to use this model to get the features of a given text in PyTorch:

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained('cis-lmu/glot500-base')
model = AutoModelForMaskedLM.from_pretrained("cis-lmu/glot500-base")

# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input, output_hidden_states=True)

Glot500-m Evaluation

We provide in-depth evaluation of Glot500-m model and baselines in our paper. Each number is an average over head languages, tail languages and all languages. See the paper for detailed results per task and language. Glot500-m outperforms XLM-R-B (base) in all tasks for head (except for POS) and tail languages and XLM-R-L (large) for tail languages. Best result per task/language set is in bold.

Glot500-c

This is an overview of the corpora included Glot500-c presented in our paper. Glot500-c will be sent via email upon filling the data request form. The part that we can redistribute is available at huggingface-dataset. For more information, check out the table below.

Disclaimer Please note that, while the data sources utilized in this study do not explicitly prohibit the reuse of data for research purposes, some sources do have copyright statements indicating that such use is permissible, while others do not. Additionally, certain sources prohibit the redistribution of data. As such, data from these sources is omitted from the published version of Glot500-c.
As regards the ND (NoDerivs) constraint for some datasets, we only change the format of the container while preserving the original contents. The first column of the table indicates the availability of each corpus in the downloadable Glot500-c (yes/no/partially).

We request all the users of Glot500-c to cite the original creators of the datsets and comply to each datasets' license. A BibTex file is available.

If you are a dataset owner and wish to update any part of this overview, or do not want your dataset to be included in Glot500-c, please send us an email at glot500@cis.lmu.de .

Glot500-c overview table:

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Training and Evalutaion Code

Prerequisites

We use two settings due to package conflict:

• Major: Python 3.9, requirements.txt
• Evaluation: Python 3.6, evaluation/requirements.txt

Data preparation

For training both tokenizer and model of Glot500-m, we need to prepare a balanced corpus covering all languages.

Go to 'preprocessing/' and run:

bash merge_files.sh

Specify --data_directory with the directory to data for each language and --save_directory with the directory for putting the merged file. For Glot500, we set --scale 1 for training tokenizer, --scale 30 for continued pretraining the model.

Vocabulary Extension

Go to 'tokenization/' and run:

bash train.sh

Specify --input_fname with the merged data file for training the tokenizer and --save_directory with the directory for saving the final tokenizer.

Continued Pretraining

Go to 'modeling/' and run:

bash train_bash.sh

Specify train_file with the merged data file for continued pretraining the model, --tokenizer_name with the trained Huggingface-style tokenizer, --output_dir with the directory for saving logs and checkpoints during training, and --cache_dir with the directory for saving Huggingface cache.

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Evaluation

Download Datasets

For downloading datasets for NER, POS, and Sentence Retrieval Tatoeba, first go to 'evaluation/download_data' and create a download folder with mkdir -p download. You then need to manually download panx_dataset (for NER) from here (note that it will download as AmazonPhotos.zip) to the download directory. Finally, run the following command under 'evaluation/download_data' to download and process the datasets:

bash download_data.sh

For downloading datasets for Sentence Retrieval Bible, Round-Trip Alignment, you can contact Michael Cysouw, Philipps University of Marburg, to request access to the Parallel Bible Corpus for academic purposes.

Sequence Labeling

For NER evaluation, go to 'evaluation/tagging' and run:

bash evaluate_ner.sh

Specify DATA_DIR with the directory for NER dataset, OUTPUT_DIR with the directory for saving the fine-tuned models and evaluation results.

For POS evaluation, go to 'evaluation/tagging' and run:

bash evaluate_pos.sh

Specify DATA_DIR with the directory for POS dataset, OUTPUT_DIR with the directory for saving the fine-tuned models and evaluation results.

Sentence Retrieval

For Sentence Retrieval Tatoeba evaluation, go to 'evaluation/retrieval' and run:

bash evaluate_retrieval_tatoeba.sh

Specify DATA_DIR with the directory for Sentence Retrieval Tatoeba dataset, OUTPUT_DIR with the directory for saving the fine-tuned models and evaluation results.

For Sentence Retrieval Bible evaluation, go to 'evaluation/retrieval' and run:

bash evaluate_retrieval_bible.sh

Specify DATA_DIR with the directory for Sentence Retrieval Bible dataset, OUTPUT_DIR with the directory for saving the fine-tuned models and evaluation results.

Round-Trip Alignment

For Round-Trip Alignment evaluation, go to 'evaluation/round-trip' and run:

python evaluate_roundtrip.py

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Citation

If you find our model, data or the overview of data useful for your research, please cite:

@inproceedings{imanigooghari-etal-2023-glot500,
	title        = {Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages},
	author       = {ImaniGooghari, Ayyoob  and Lin, Peiqin  and Kargaran, Amir Hossein  and Severini, Silvia  and Jalili Sabet, Masoud  and Kassner, Nora  and Ma, Chunlan  and Schmid, Helmut  and Martins, Andr{\'e}  and Yvon, Fran{\c{c}}ois  and Sch{\"u}tze, Hinrich},
	year         = 2023,
	month        = jul,
	booktitle    = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	publisher    = {Association for Computational Linguistics},
	address      = {Toronto, Canada},
	pages        = {1082--1117},
	url          = {https://aclanthology.org/2023.acl-long.61}
}

Acknowledgements

This repository is built on top of transformers and xtreme.