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huggingface/sentence-transformers

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TLDR

Python framework that converts text into numerical embeddings to measure semantic similarity, with 15,000+ pre-trained models for search, deduplication, and ranking tasks.

Mindmap

mindmap
  root((repo))
    What it does
      Text to embeddings
      Semantic similarity
      Search and ranking
    Model types
      Sentence Transformers
      Cross-Encoders
      Sparse Encoders
    Use cases
      Semantic search
      Duplicate detection
      Document clustering
      Result reranking
    Getting started
      15000+ models
      Fine-tuning tools
      Pip install

Things people build with this

USE CASE 1

Build a semantic search engine that finds documents by meaning rather than keyword matching.

USE CASE 2

Detect duplicate or near-duplicate content across a large text dataset.

USE CASE 3

Re-rank search results by relevance using cross-encoder models to improve ranking quality.

USE CASE 4

Group similar documents or customer feedback into clusters without manual labeling.

Tech stack

PythonPyTorchHugging Face TransformersNumPy

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

Sentence Transformers is a Python framework for converting text into numerical representations called embeddings, fixed-size lists of numbers that capture the meaning of the text. Two pieces of text with similar meanings end up with similar numbers, which makes it possible to measure how semantically related they are, even if they use completely different words. The library provides three main types of models. Sentence Transformer models (also called embedding models) convert text into dense embeddings useful for tasks like semantic search, finding duplicate content, and grouping similar documents. Cross-Encoder models (also called reranker models) take two pieces of text together and score how well they match, useful for re-ranking a list of search results to put the most relevant ones first. Sparse Encoder models produce a different kind of representation where most values are zero, which can be more efficient for certain retrieval scenarios. The framework includes over 15,000 pre-trained models that can be downloaded and used immediately, as well as tools for fine-tuning your own models on custom data. It is installed via pip and works with Python 3.10 and above. The full documentation is at sbert.net.

Copy-paste prompts

Prompt 1
Show me how to use Sentence Transformers to convert a list of product descriptions into embeddings and find the most similar products to a query.
Prompt 2
How do I fine-tune a Sentence Transformer model on my own dataset of similar and dissimilar sentence pairs?
Prompt 3
Use a cross-encoder model from Sentence Transformers to re-rank search results and explain the difference between cross-encoders and sentence transformers.
Prompt 4
Write code to detect duplicate customer reviews using Sentence Transformers embeddings and cosine similarity.
Prompt 5
How do I choose between Sentence Transformer, Cross-Encoder, and Sparse Encoder models for my use case?
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