WECHSEL
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
arXiv: https://arxiv.org/abs/2112.06598
Models from the paper are available on the HuggingFace Hub:
roberta-base-wechsel-french
roberta-base-wechsel-german
roberta-base-wechsel-chinese
roberta-base-wechsel-swahili
gpt2-wechsel-french
gpt2-wechsel-german
gpt2-wechsel-chinese
gpt2-wechsel-swahili
Installation
We distribute a Python Package via PyPI:
pip install wechsel
Alternatively, clone the repository, install requirements.txt
and run the code in wechsel/
.
Example usage
Transferring English roberta-base
to Swahili:
import torch
from transformers import AutoModel, AutoTokenizer
from datasets import load_dataset
from wechsel import WECHSEL, load_embeddings
source_tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = AutoModel.from_pretrained("roberta-base")
target_tokenizer = source_tokenizer.train_new_from_iterator(
load_dataset("oscar", "unshuffled_deduplicated_sw", split="train")["text"],
vocab_size=len(source_tokenizer)
)
wechsel = WECHSEL(
load_embeddings("en"),
load_embeddings("sw"),
bilingual_dictionary="swahili"
)
target_embeddings, info = wechsel.apply(
source_tokenizer,
target_tokenizer,
model.get_input_embeddings().weight.detach().numpy(),
)
model.get_input_embeddings().weight.data = torch.from_numpy(target_embeddings)
# use `model` and `target_tokenizer` to continue training in Swahili!
Bilingual dictionaries
We distribute 3276 bilingual dictionaries from English to other languages for use with WECHSEL in dicts/
.
Citation
Please cite WECHSEL as
@misc{minixhofer2021wechsel,
title={WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models},
author={Benjamin Minixhofer and Fabian Paischer and Navid Rekabsaz},
year={2021},
eprint={2112.06598},
archivePrefix={arXiv},
primaryClass={cs.CL}
}