Trans-encoder-bi-simcse-roberta-large Open Source Model - Free Deployment to Boost Sentence Similarity Calculation

Trans Encoder Bi Simcse Roberta Large

Developed by cambridgeltl

An unsupervised sentence encoder based on RoBERTa-large, trained with self-distillation and mutual distillation techniques, suitable for sentence similarity calculation tasks.

Text Embedding

Transformers

#Unsupervised Sentence Embedding #Dual-Encoder Architecture #STS Task Optimization

Downloads 17

Release Time : 3/2/2022

Model Overview

This model is a dual-encoder architecture sentence embedding model specifically designed for calculating semantic similarity between sentences. It employs unsupervised training using sentence pairs sampled from multiple standard datasets.

Model Features

Unsupervised Training

Uses self-distillation and mutual distillation techniques, enabling training without manually annotated data.

Dual-Encoder Architecture

Employs independent encoders to process input sentences, improving computational efficiency.

Based on RoBERTa-large

Built on a powerful pre-trained language model to provide high-quality sentence representations.

Model Capabilities

Sentence Embedding Generation

Semantic Similarity Calculation

Unsupervised Learning

Use Cases

Information Retrieval

Document Similarity Search

Retrieves relevant documents by computing sentence embedding similarities.

Question Answering Systems

Question Matching

Identifies semantic similarity between user questions and those in a knowledge base.

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Trans Encoder Bi Simcse Roberta Large

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 cambridgeltl/trans-encoder-bi-simcse-roberta-large

📄 License

📚 Documentation

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