Targeted Multilingual Models

The general structure of this repository is as follows:

• src contains the Python source code for conducting Language-Adaptive Pre-Training (LAPT), fine-tuning and evaluating models on downstream tasks (POS and NER), and re-initializing model embeddings as described in the paper
• configs contains YAML configuration files for each experiment in the study (LAPT and fine-tuning)
• output contains output logs from evaluation experiments, including scores
• scripts contains top-level shell scripts for common routines such as running LAPT, fine-tuning, sampling multilingual sets, and training vocabularies
• tools contains auxiliary software fulfilling miscillaneous functions such as pre-processing data, training sentencepiece models, and visualizing embeddings with PCA

Environment Setup

To manage your Python environment, we recommend you install anaconda/miniconda. Conda should then be used to create an environment with Python 3.9, using this command conda create --name txlm python=3.9.

After activating your new environment with conda activate txlm or source activate txlm, confirm that the result of the command which pip returns the path to the pip executable within your environment folder, e.g. ~/miniconda3/envs/txlm/bin.

Next, use conda/pip to install the version of PyTorch that is compatible with your system / CUDA version. Original experiments were conducted with PyTorch version 1.13.1 for CUDA 11.7. The command to install this version is conda install pytorch==1.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

Finally, in the main folder of the repository, run the command pip install -r requirements.txt to install the required packages.

Running Experiments

LAPT

Language-Adaptive Pre-training can be run with scripts/train_model.sh. The usage is:

./scripts/train_model.sh path_to_conda_folder environment_name cuda_devices config_name

For instance, if your miniconda folder is at the path ~/username/miniconda3, the environment is named txlm, you wish to use Cuda devices 0 and 1, and the experiment configuration is erzya_cpt-full.yml, the command is:

./scripts/train_model.sh ~/username txlm 0,1 configs/erzya_cpt-full.yml

NOTE: We use the term Language-Adaptive Pre-Training (LAPT) in the associated publication and readme. However, in configuration file names this is referred to as CPT (Continued Pre-Training).

Fine-tuning and Evaluation

Similarly, the usage for fine-tuning and evaluating a model on a downstream task is:

./scripts/eval_finetune.sh path_to_conda_folder environment_name cuda_devices config_name

The choice of downstream task and hyperparameters is specified in the configuration file. Please see the configs folder for examples.

Re-initializing Embeddings

The Python script for re-initializing embeddings can be used as follows:

python src/reinitialize_embeddings.py \
    --old_model_path path_to_base_model \
    --new_vocab_file path_to_new_vocab \
    --embedding_output_path path_to_new_embedding_matrix \
    [--old_tokenizer_path path_to_base_tokenizer] \
    [--reinit_by_script] \
    [--reinit_by_identity] \
    [--reinit_by_position] \
    [--focus_reinit] \
    [--focus_train_path path_to_focus_training_corpus]

old_model_path is the name of the base model for which you are re-initializing the vocabulary. For instance, XLM-R: xlm-roberta-base. new_vocab_file is the sentencepiece model for the new (specialized) vocabulary/tokenizer. embedding_output_path is the path at which to save the resulting embedding block (as a PyTorch data file). old_tokenizer_path is the optional path to the base (non-specialized) vocabulary/tokenizer, if it is at a different path than the base model (for xlm-roberta-base, the model and tokenizer path are the same). The reinit_by_<method> arguments are boolean flags for which technique to use for re-initializing embeddings; see our paper for details of these methods. reinit_by_position requires that reinit_by_script is also true.

Finally focus_reinit is the boolean flag to re-initialize embeddings by the FOCUS method (see paper for details). This method overrides all other re-initialization methods. It also requires a path to the training corpus for the FOCUS method, via the focus_train_path argument. The source code for FOCUS is not included in this repository. To use this method, place the FOCUS Python source code in a folder called src/focus. The FOCUS code requires additional dependencies: entmax, fasttext, requests, loguru.