SimCSE
Explore how the SimCSE framework, employing unsupervised and supervised methods, advances sentence embeddings with contrastive learning. The unsupervised model leverages dropout as noise to predict input sentences, while the supervised model employs entailment and contradiction pairs from NLI datasets for better embeddings. Easy installation via PyPI and Huggingface compatibility ensures seamless model integration. Recent updates highlight EMNLP acceptance and enhanced model performance. Investigate SimCSE for optimizing sentence embeddings.
