# Information Retrieval

Chonky is a Transformer model capable of intelligently splitting text into meaningful semantic chunks, suitable for RAG systems.

Set-Encoder is a cross-encoder architecture specifically designed for efficient and permutation-invariant paragraph reordering, particularly suitable for novelty-aware re-ranking tasks.

This is a text ranking model used for sorting text by relevance.

rank1 is an information retrieval reranking model trained on Qwen2.5-0.5B, improving relevance judgment accuracy through generated reasoning chains.

Set-Encoder is a cross-encoder architecture specifically designed for efficient and permutation-invariant paragraph reordering.

A sentence embedding model based on the transformers library, used for generating vector representations of sentences, supporting text ranking tasks.

SPLADE-v3-Doc is the document version of the SPLADE model, focusing on document-side inference and suitable for scenarios such as information retrieval.

This is a sentence-transformers-based sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.

GTE-Large is a text embedding model ported to ONNX, suitable for text classification and similarity search tasks.

GTE Tiny is a small general-purpose text embedding model suitable for various natural language processing tasks.

DRAGON+ is a dense retrieval model based on the BERT architecture, employing an asymmetric dual-encoder architecture, suitable for text retrieval tasks.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

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

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

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 is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.

This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.

DPR question encoder model based on BERT architecture for dense passage retrieval tasks

This is a sentence embedding model based on DistilBERT, capable of converting text into 768-dimensional vector representations, suitable for sentence similarity calculation and semantic search tasks.

This is a cross-lingual cross-encoder model suitable for English-German bilingual paragraph re-ranking tasks, trained based on the MS Marco passage ranking task.

A fine-tuned version of the Contriever pre-trained model, optimized for dense information retrieval tasks and trained using contrastive learning methods

This is a sentence embedding model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic search and text similarity calculation.

This model is a multilingual Q&A model fine-tuned on the TyDi QA dataset based on Google's mT5-base, supporting Q&A tasks in 101 languages.