
[xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned for sentence embeddings with [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (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](https://github.com/princeton-nlp/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:
```bash
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](https://arxiv.org/abs/2305.13303)
```bibtex
@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",
}
```

{"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh"], "license": "mit", "pipeline_tag": "feature-extraction"}
transformers pytorch xlm-roberta feature-extraction multilingual af am ar as az be bg bn br bs ca cs cy da de el en eo es et eu fa fi fr fy ga gd gl gu ha he hi hr hu hy id is it ja jv ka kk km kn ko ku ky la lo lt lv mg mk ml mn mr ms my ne nl no om or pa pl ps pt ro ru sa sd si sk sl so sq sr su sv sw ta te th tl tr ug uk ur uz vi xh yi zh arxiv:2305.13303 license:mit endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #arxiv-2305.13303 #license-mit #endpoints_compatible #has_space #region-us 


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.: URL

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:


## Citation


## Citation
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #arxiv-2305.13303 #license-mit #endpoints_compatible #has_space #region-us 
 ## Citation