Practical overview
When it fits
Use it when a neural NLP project can benefit from reusable architecture components or JSON-configured models, and you need a documented training, testing and prediction workflow.
Who it is for
- Engineers building neural NLP models and training or inference pipelines.
- Researchers experimenting with NLP model architectures and reusable neural components.
Documented uses
- Start model training from an existing JSON configuration in the Model Zoo.
- Design NLP model architectures by combining components from the Block Zoo.
- Train, test and predict with a configured model.
- Get predictions interactively or process cases in batch.
- Explore knowledge distillation for model compression.
Requirements & limits
The public GitHub repository identifies NeuronBlocks as open source under the MIT license.
- It separates reusable architecture components in Block Zoo from JSON-configured NLP models in Model Zoo.
- The README specifies Python 3.6 or later and PyTorch 0.4.1 or later.
- The quick-start instructions cover Linux and Windows, with CPU and GPU use.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train, test and predict with the demo configuration
Use the documented demo configuration for model training, testing and prediction.
Before you start
- Python 3.6 or later
- Python packages in requirements.txt
- PyTorch 0.4.1 or later
- A Linux or Windows environment; the README documents CPU and GPU use
Steps
Clone the repository and install its requirements with pip install -r requirements.txt.
Install PyTorch; for Linux, the README gives pip install "torch>=0.4.1", while it suggests Conda installation on Windows.
From the project root, run python train.py --conf_path=model_zoo/demo/conf.json.
Run python test.py --conf_path=model_zoo/demo/conf.json, then python predict.py --conf_path=model_zoo/demo/conf.json.
Expected outcome
The documented example trains and tests a model and runs prediction using the demo configuration.
Guide limitations
- The instructions use the repository's demo configuration; the guide does not specify results for other data or configurations.
Run interactive or batch prediction with the demo model
Use the prediction script to get an interactive result or write batch predictions to an output file.
Before you start
- NeuronBlocks installed with its documented Python and PyTorch requirements
- A trained model and the demo configuration
- For batch prediction, a cases file such as dataset/demo/predict.tsv
Steps
From the project root, start interactive prediction with python predict.py --conf_path=model_zoo/demo/conf.json --predict_mode='interactive'.
Enter a case following the prompt; enter "exit" to leave the interactive interface.
For batch mode, run python predict.py --conf_path=model_zoo/demo/conf.json --predict_mode='batch' --predict_data_path=dataset/demo/predict.tsv.
Expected outcome
Interactive mode returns predictions for entered cases; batch mode reads the cases file and writes prediction results to a prediction file.
Guide limitations
- The documented batch example uses the demo TSV file; the README does not specify a custom output path in the shown command.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- microsoft/NeuronBlocks · Official homepage title · retained in its source language