Top2Vec learns jointly embedded topic, document and word vectors.
MTEB: Massive Text Embedding Benchmark
#自然语言处理#Efficient few-shot learning with Sentence Transformers
#自然语言处理#A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
#自然语言处理#Fast State-of-the-Art Static Embeddings
#自然语言处理#Efficient Retrieval Augmentation and Generation Framework
#自然语言处理#Running Llama 2 and other Open-Source LLMs on CPU Inference Locally for Document Q&A
#自然语言处理#unified embedding model
#大语言模型#Empower Large Language Models (LLM) using Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) for knowledge intensive tasks
#大语言模型#An editing tool that uses AI to transcribe, understand content and search for anything in your footage, integrated with ChatGPT and other AI models
On-premises conversational RAG with configurable containers
#大语言模型#Local first semantic code search and chat | Leverage custom copilots with fine-tuning datasets from code in Alpaca, Conversational, Completion and Instruction format
Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"
MinT: Minimal Transformer Library and Tutorials
#自然语言处理#This repository contains an easy and intuitive approach to few-shot classification using sentence-transformers or spaCy models, or zero-shot classification with Huggingface.
sentence-transformers to onnx 让sbert模型推理效率更快
#自然语言处理#Making BERT stretchy. Semantic Elasticsearch with Sentence Transformers
Open Source Text Embedding Models with OpenAI Compatible API
#自然语言处理#Simply, faster, sentence-transformers
Optimize Document Retrieval with Fine-Tuned KnowledgeBases