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SimCSE (Simple Contrastive Learning of Sentence Embeddings) is a machine learning framework for training sentence embeddings using contrastive learning. It improves representation learning for NLP tasks.
Features
- Uses contrastive learning for sentence embeddings
- Pretrained on large-scale datasets for better performance
- Supports both supervised and unsupervised training
- Compatible with Hugging Face Transformers
- Outperforms traditional sentence embedding models
- Useful for semantic similarity, retrieval, and clustering
Project Samples
Categories
Natural Language Processing (NLP)License
MIT LicenseFollow SimCSE
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