Siamese Nn Semantic Text Similarity: compare text models

Understand the project

Practical overview

Use it when you want to explore or adapt Python/PyTorch model examples for semantic similarity between text pairs.

Python developers prototyping semantic text-pair comparison.
Researchers or learners comparing Siamese neural-network architectures for text similarity.

Explore four neural-network architectures for semantic text similarity.
Use the documented examples as starting points for model initialization and training.

Before you choose

The public GitHub repository reports an Apache-2.0 license.

The examples have different training patterns: the LSTM, attention and Transformer examples use a training function, while BERT uses Transformers Trainer.
The README documents dependency installation and spaCy English tokenization setup.
Several example configurations require model dimensions, embeddings, data and dataloaders to be supplied.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the repository's listed dependencies and the English spaCy model used for tokenization.

Install dependencies with the command below.
Documented command
```
pip install -r requirements.txt
```
Download the spaCy English model with the command below.
Documented command
```
python -m spacy download en_core_web_sm
```

The README's documented dependency and tokenization-model setup steps are complete.

These steps cover setup only; they do not run an example or verify the environment.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 18 October 2026
Original website title: shahrukhx01/siamese-nn-semantic-text-similarity · Official homepage title · retained in its source language