# Contrastive learning

A dual encoder based on the Bloomz-560m-dpo-chat model, designed to map articles and queries into the same vector space, supporting cross-language retrieval in French and English.

This is a SetFit model trained on the sst2 dataset for English text classification tasks, utilizing efficient few-shot learning techniques.

MMLW (I Must Get Better News) is a Polish neural text encoder optimized for information retrieval tasks, capable of converting queries and passages into 768-dimensional vectors.

A SigLIP (Sigmoid Loss for Language-Image Pre-training) model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

A SigLIP (Sigmoid Loss for Language-Image Pretraining) model trained on the WebLI dataset, suitable for zero-shot image classification tasks.

This is a sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space and is suitable for semantic search and clustering tasks.

A lightweight sentence embedding model based on the MiniLM architecture that can map text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.

This is a sentence embedding model based on the MPNet architecture, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and sentence similarity tasks.

DeCLUTR-base is a universal sentence encoder model trained through deep contrastive learning for generating high-quality text representations.

DeCLUTR-small is a general-purpose sentence encoder model based on deep contrastive learning for generating high-quality sentence embeddings.

CODER is a knowledge-infused cross-language medical terminology embedding model focused on medical terminology standardization tasks.

CODER is a knowledge-fused cross-language medical terminology embedding model for medical terminology standardization.