Compositional-BERT-Large-Uncased open-source model - A practical tool for free calculation of sentence similarity

Compositional Bert Large Uncased

Developed by perceptiveshawty

CompCSE and SimCSE are contrastive learning-based sentence embedding models for calculating sentence similarity.

EnglishOpen Source License:Apache-2.0 #English sentence embedding #Semantic similarity calculation #Contrastive learning optimization

Downloads 754

Release Time : 7/25/2023

Model Overview

These models are trained using contrastive learning techniques, enabling them to map semantically similar sentences to nearby vector spaces, primarily used for sentence similarity calculation and semantic search tasks.

Model Features

Contrastive learning

Uses contrastive learning techniques to optimize sentence embeddings, bringing similar sentences closer in the vector space.

Efficient training

Trains using unsupervised or weakly supervised methods, reducing reliance on labeled data.

Semantic understanding

Capable of capturing deep semantic information of sentences, not just surface-level word matching.

Model Capabilities

Sentence similarity calculation

Semantic search

Text clustering

Information retrieval

Use Cases

Information retrieval

Document similarity search

Quickly find semantically similar documents in a large-scale document repository.

Improves search accuracy and recall rate

Question answering systems

Question matching

Match user questions with similar questions in the knowledge base.

Improves response accuracy of the QA system

Property	Details
Datasets	perceptiveshawty/datasets-for-compcse, princeton-nlp/datasets-for-simcse

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Compositional Bert Large Uncased

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Sentence Similarity with Transformers

📦 Installation

📚 Documentation

Datasets

Pipeline Tag

📄 License