Stsb Roberta Base

🚀 Cross-Encoder for Semantic Textual Similarity

This model is designed to predict the semantic similarity score between two sentences, trained with advanced techniques.

🚀 Quick Start

This model was trained using SentenceTransformers Cross-Encoder class.

✨ Features

• The model predicts a score between 0 and 1 for the semantic similarity of two sentences.
• It can be used with sentence_transformers or the AutoModel class from Transformers.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/stsb-roberta-base')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])

The model will predict scores for the pairs ('Sentence 1', 'Sentence 2') and ('Sentence 3', 'Sentence 4').

Advanced Usage

You can use this model also without sentence_transformers and by just using Transformers AutoModel class

📚 Documentation

Training Data

This model was trained on the STS benchmark dataset.

📄 License

This project is licensed under the Apache-2.0 license.