ZurichNLP/unsup-simcse-xlm-roberta-base

xlm-roberta-base fine-tuned for sentence embeddings with SimCSE (Gao et al., EMNLP 2021).

See a similar English model released by Gao et al.: https://huggingface.co./princeton-nlp/unsup-simcse-roberta-base.

Fine-tuning was done using the reference implementation of unsupervised SimCSE and the 1M sentences from English Wikipedia released by the authors. As a sentence representation, we used the average of the last hidden states (pooler_type=avg), which is compatible with Sentence-BERT.

Fine-tuning command:

python train.py \
    --model_name_or_path xlm-roberta-base \
    --train_file data/wiki1m_for_simcse.txt \
    --output_dir unsup-simcse-xlm-roberta-base \
    --num_train_epochs 1 \
    --per_device_train_batch_size 32 \
    --gradient_accumulation_steps 16 \
    --learning_rate 1e-5 \
    --max_seq_length 128 \
    --pooler_type avg \
    --overwrite_output_dir \
    --temp 0.05 \
    --do_train \
    --fp16 \
    --seed 28852

Citation

@inproceedings{vamvas-sennrich-2023-rsd,
      title={Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents},
      author={Jannis Vamvas and Rico Sennrich},
      month = dec,
      year = "2023",
      booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
      address = "Singapore",
      publisher = "Association for Computational Linguistics",
}