Popular GitHub Repositories for Sbert

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

Build and train state-of-the-art natural language processing models using BERT

Search with BERT vectors in Solr, Elasticsearch, OpenSearch and GSI APU

sentence-transformers to onnx 让sbert模型推理效率更快

Rust port of sentence-transformers (https://github.com/UKPLab/sentence-transformers)

TextReducer - A Tool for Summarization and Information Extraction

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

KoBERTopic? BERTopic? ??? ???? ??? ? ??? ?????? BERT? ??? ?????.

基于sentence-transformers实现文本转向量的机器人

Building a model to recognize incentives for landscape restoration in environmental policies from Latin America, the US and India. Bringing NLP to the world of policy analysis through an extensible framework that includes scraping, preprocessing, active learning and text analysis pipelines.

Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.

Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)

ANN Search through the COVID CORD-19 Dataset using SBERT.

Run sentence-transformers (SBERT) compatible models in Node.js or browser.

Embedding Representation for Indonesian Sentences!

Powerful document clustering models are essential as they can efficiently process large sets of documents. These models can be helpful in many fields, including general research. Searching through large corpora of publications can be a slow and tedious task; such models can significantly reduce this time.

Livebook to run a Phoenix_LiveView documentation Retrieval-Augmented Generation (RAG) enhanced LLM

A tool for performing semantic search within pdf documents leveraging sentence transformers.

A semantic food search web application built with Django, Solr, SBERT, and Docker

Usage of BERT models for text clustering techniques using sentence embeddings