Instructions to use cambridgeltl/trans-encoder-bi-simcse-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cambridgeltl/trans-encoder-bi-simcse-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cambridgeltl/trans-encoder-bi-simcse-bert-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/trans-encoder-bi-simcse-bert-large") model = AutoModel.from_pretrained("cambridgeltl/trans-encoder-bi-simcse-bert-large") - Notebooks
- Google Colab
- Kaggle
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use [CLS] (before pooler) as the representation of the input.
Citation
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}