# Question-Answer Matching

This is a cross-encoder model fine-tuned from nreimers/MiniLM-L6-H384-uncased, designed for computing scores of text pairs, suitable for text re-ranking and semantic search tasks.

This is a cross-encoder model fine-tuned from ModernBERT-base, designed for calculating scores of text pairs, suitable for text reordering and semantic search tasks.

This is a cross-encoder model fine-tuned from ModernBERT-base for text re-ranking and semantic search tasks.

A multilingual text semantic search model based on Siamese-BERT architecture, trained on 215 million (question, answer) pairs to generate 384-dimensional normalized embedding vectors

This is a DistilRoBERTa-based sentence transformer model capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like semantic search and clustering.

A sentence embedding model based on DistilBert TAS-B, optimized for semantic search tasks, capable of mapping text to a 768-dimensional vector space

This model is a cross-encoder trained on distilroberta-base for determining whether a given passage can answer a specific question, trained on the GLUE QNLI dataset.

This is a cross-encoder model based on the ELECTRA architecture, specifically designed for natural language inference (NLI) in question-answering tasks, determining whether a given question can be answered by a specific paragraph.