Practical overview
When it fits
Use it when an experiment needs a documented pipeline for training or testing sentence-pair similarity models across several architectures and supported NLP datasets.
Who it is for
- Researchers comparing Siamese-network architectures for sentence-pair similarity.
- Developers exploring paraphrase-identification experiments with the supported datasets.
- Practitioners who want to inspect multihead attention weights through the documented GUI demo.
Documented uses
- Train a CNN, RNN or multihead Siamese model on SNLI, Quora Question Pairs or ANLI.
- Compare semantic similarity between pairs of text inputs using a trained model.
- Test pretrained models through the prediction mode or GUI demo.
- Visualize attention weights for a multihead model in the GUI.
Requirements & limits
The repository is public and advertises an MIT license. Its README specifies Python 3.6 and TensorFlow 1.15.2, with separate CPU and GPU requirements files.
- The pipeline lists three datasets: SNLI, Quora Question Pairs and ANLI.
- Training configuration is editable, with separate model parameters for the attention, convolutional and recurrent architectures.
- The README describes pretrained-model prediction and a GUI demo, but using those workflows requires placing downloaded model files in model_dir.
- The README lists an example Colab notebook for GPU training on SNLI or QQP.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the supported datasets and install dependencies
Create the project’s corpus directory and install the listed CPU or GPU requirements before training.
Before you start
- Python 3.6, the version specified by the project
- A local checkout of the repository
- Network access for the data preparation script
Steps
From the repository, change to the bin directory.
Make prepare_data.sh executable and run it to create the corpora directory containing QQP, SNLI and ANLI data.
From the repository root, install requirements/requirements-cpu.txt for CPU use or requirements/requirements-gpu.txt for GPU use.
Expected outcome
The project’s dataset directory is prepared and the selected dependency set is installed.
Guide limitations
- The README notes that downloading the data can take time depending on network throughput.
- The project specifies Python 3.6 and TensorFlow 1.15.2; the guide does not establish compatibility with other versions.
Train a selected Siamese model on a supported dataset
Start a documented training run using one of the repository’s model and dataset choices.
Before you start
- The repository’s dependencies installed
- Prepared SNLI, QQP or ANLI data
- A selected model: cnn, rnn or multihead
Steps
Choose a supported dataset: SNLI, QQP or ANLI.
From the repository root, run python3 run.py train SELECTED_MODEL SELECTED_DATASET, replacing the placeholders with the chosen model and dataset.
If needed, add --experiment_name NAME to label the experiment or --gpu GPU_NUMBER to select a GPU.
Expected outcome
The repository starts the requested training pipeline with the selected architecture and dataset.
Guide limitations
- The README documents the command and options but does not specify a guaranteed training duration or outcome.
- GPU selection is an optional argument; the README also provides a CPU training example.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- tlatkowski/multihead-siamese-nets · Official homepage title · retained in its source language