MTEB: Massive Text Embedding Benchmark
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Updated
Jun 1, 2024 - Python
MTEB: Massive Text Embedding Benchmark
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
Generative Representational Instruction Tuning
Rust port of sentence-transformers (https://github.com/UKPLab/sentence-transformers)
文本相似度,语义向量,文本向量,text-similarity,similarity, sentence-similarity,BERT,SimCSE,BERT-Whitening,Sentence-BERT, PromCSE, SBERT
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.
Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)
TextReducer - A Tool for Summarization and Information Extraction
This project builds a semantic search engine specifically designed for video content. It utilizes SBERT, to understand the meaning behind user queries and videos. This allows users to search for specific information within videos, skipping irrelevant parts and saving them valuable time.
Classification pipeline based on sentenceTransformer and Facebook nearest-neighbor search library
Run sentence-transformers (SBERT) compatible models in Node.js or browser.
Backend code for GitHub Recommendation Extension
Плагин для SmartApp Framework, осуществляющий векторизацию (получение embedding'ов) текстов с помощью различных моделей
Heterogenous, Task- and Domain-Specific Benchmark for Unsupervised Sentence Embeddings used in the TSDAE paper: https://arxiv.org/abs/2104.06979.
Embedding Representation for Indonesian Sentences!
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