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
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- sentence-similarity
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- dual-encoder
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### cambridgeltl/trans-encoder-bi-simcse-
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An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). 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](https://huggingface.co/princeton-nlp/unsup-simcse-bert-large-uncased) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
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### Citation
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- sentence-similarity
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- dual-encoder
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### cambridgeltl/trans-encoder-bi-simcse-bert-large
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An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). 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](https://huggingface.co/princeton-nlp/unsup-simcse-bert-large-uncased) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
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### Citation
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