semantic-text-similarity 1.0.1

implementations of models and metrics for semantic text similarity. that's it.

semantic-text-similarity

an easy-to-use interface to fine-tuned BERT models for computing semantic similarity. that's it.

This project contains an interface to fine-tuned, BERT-based semantic text similarity models. It modifies pytorch-transformers by abstracting away all the research benchmarking code for ease of real-world applicability.

Model	Training Dataset	Dev. Correlation
Web STS BERT	STS-B	0.893
Clinical STS BERT	MED-STS

Installation

Install with pip:

pip install semantic-text-similarity

or directly:

pip install git+https://github.com/AndriyMulyar/semantic-text-similarity

Use

Maps batches of sentence pairs to real-valued scores in the range [0,5]

from semantic_text_similarity.models import WebBertSimilarity

model = WebBertSimilarity(device='cpu', batch_size=10) #defaults to GPU prediction

model.predict([("She won an olympic gold medal","The women is an olympic champion")])

More examples.

Notes

• You will need a GPU to apply these models if you would like any hint of speed in your predictions.
• Model downloads are cached in ~/.cache/torch/semantic_text_similarity/. Try clearing this folder if you have issues.