⚠️ NOTE: If you want to train a MicroBERT for your language, please see lgessler/microbert2.

Introduction

MicroBERT is a BERT variant intended for training monolingual models for low-resource languages by reducing model sizes and using multitask learning on part of speech tagging and dependency parsing in addition to the usual masked language modeling.

For more information, please see our paper. If you'd like to cite our work, please use the following citation:

@inproceedings{gessler-zeldes-2022-microbert,
    title = "{M}icro{BERT}: Effective Training of Low-resource Monolingual {BERT}s through Parameter Reduction and Multitask Learning",
    author = "Gessler, Luke  and
      Zeldes, Amir",
    booktitle = "Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.mrl-1.9",
    pages = "86--99",
}

Pretrained Models

The following pretrained models are available. Note that each model's suffix indicates the tasks that were used to pretrain it: masked language modeling (m), XPOS tagging (x), or dependency parsing (p).

• microbert-ancient-greek-m
• microbert-ancient-greek-mx
• microbert-ancient-greek-mxp
• microbert-coptic-m
• microbert-coptic-mx
• microbert-coptic-mxp
• microbert-indonesian-m
• microbert-indonesian-mx
• microbert-indonesian-mxp
• microbert-maltese-m
• microbert-maltese-mx
• microbert-maltese-mxp
• microbert-uyghur-m
• microbert-uyghur-mx
• microbert-uyghur-mxp
• microbert-tamil-m
• microbert-tamil-mx
• microbert-tamil-mxp
• microbert-wolof-m
• microbert-wolof-mx
• microbert-wolof-mxp

Usage

Setup

1. Ensure submodules are initialized:

git submodule update --init --recursive

2. Create a new environment:

conda create --name embur python=3.9
conda activate embur

3. Install PyTorch etc.

conda install pytorch torchvision cudatoolkit -c pytorch

4. Install dependencies:

pip install -r requirements.txt

Experiments

This repo is exposed as a CLI with the following commands:

├── data                  # Data prep commands
│   ├── prepare-mlm
│   └── prepare-ner
├── word2vec              # Static embedding condition
│   ├── train
│   ├── evaluate-ner
│   └── evaluate-parser
├── mbert                 # Pretrained MBERT
│   ├── evaluate-ner
│   └── evaluate-parser
├── mbert-va              # Pretrained MBERT with Chau et al. (2020)'s VA method
│   ├── evaluate-ner
│   ├── evaluate-parser
│   └── train
├── bert                  # Monolingual BERT--main experimental condition
│   ├── evaluate-ner
│   ├── evaluate-parser
│   └── train
├── evaluate-ner-all      # Convenience to perform evals on all NER conditions
├── evaluate-parser-all   # Convenience to perform evals on all parser conditions
└── stats                 # Supporting commands for statistical summaries
    └── format-metrics

To see more information, add --help at the end of any partial subcommand, e.g. python main.py --help, python main.py bert --help, python main.py word2vec train --help.

Adding a language

For each new language to be added, you'll want to follow these conventions:

1. Put all data under data/$NAME/, with "raw" data going in some kind of subdirectory. (If it is a UD corpus, the standard UD name would be good, e.g. data/coptic/UD_Coptic-Scriptorium)
2. Ensure that it will be properly handled by the module embur.commands.data. Put a script at embur/scripts/$NAME_data_prep.py that will take the dataset's native format and write it out into data/$NAME/converted, if appropriate.
3. Update embur.language_configs with the language's information.

If you'd like to add a language's Wikipedia dump, see wiki-thresher.

Please don't hesitate to email me ([email protected]) if you have any questions.