****** New ******
Thanks for your interest in our repo!
Probably you will think this as another "empty" repo of a preprint paper 🥱.
Wait a minute! The authors are working day and night 💪, to make the code and models available, so you can explore our state-of-the-art sentence embeddings.
We anticipate the code will be out * in one week *.
Please watch👀 us and stay tuned!
4/18: We released our paper. Check it out!
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This is the codebase for the paper SimCSE: Simple Contrastive Learning of Sentence Embeddings. We propose a simple contrastive learning framework that works with both unlabeled and labeled data. Unsupervised SimCSE simply takes an input sentence and predicts itself in a contrastive learning framework, with only standard dropout used as noise. Our supervised SimCSE incorporates annotated pairs from NLI datasets into contrastive learning by using entailment pairs as positives and contradiction pairs as hard negatives. The following figure is an illustration of our models.
