pytorch bert feature-extraction arxiv:2104.08027 transformers
---
language: en

tags:
- sentence-embeddings
- sentence-similarity

### cambridgeltl/mirror-bert-base-uncased-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model. Please use mean-pooling over *all tokens* as the representation of the input.

Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.

### Citation
```bibtex
@inproceedings{
	liu2021fast,
  title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
  author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
  booktitle={EMNLP 2021},
  year={2021}
}
```
