NeuronBlocks

microsoft/NeuronBlocks is ranked 10,282 among these 8 peers; Walter0807/MotionBERT is ranked 10,283.

The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: ***Block Zoo*** and ***Model Zoo***. In ***Block Zoo***, we provide commonly used neural network components as building blocks for model architecture design. In ***Model Zoo***, we provide a suite of NLP models for common NLP tasks, in the form of **JSON configuration** files. Language Supported English Chinese NLP Tasks Supported

Sentence Classification Sentiment Analysis Question Answering Matching Textual Entailment Slot tagging Machine Reading Comprehension Knowledge Distillation for Model Compression *More on-going* Toolkit Usage Users can either pick existing models (config files) in *Model Zoo* to start model training or create new models by leveraging neural network blocks in *Block Zoo* just like playing with Lego. Get Started in 60 Seconds Installation *Note: NeuronBlocks requires **Python 3. Install Python packages in requirements. Install PyTorch (*NeuronBlocks supports **PyTorch 0. Quick Start

Get started by trying the given examples. Both **Linux/Windows, GPU/CPU** are supported. For **Windows**, we suggest you to use PowerShell instead of CMD. *Tips: in the following instruction, PROJECTROOT denotes the root directory of this project. * For prediction, NeuronBlocks have two modes: **Interactive** and **Batch**. *Interactive Prediction Mode:* The interactive mode provides interactive interface, users can input case according to corresponding prompt message and get realtime prediction result from trained model, and input "exit" to exit interactive interface

. *Batch Prediction Mode:* For batched cases prediction, NeuronBlocks provides batch prediction mode which receives a cases file as input and write the prediction results in the prediction file. For more details, please refer to Tutorial. Who should consider using NeuronBlocks Engineers or researchers who face the following challenges when using neural network models to address NLP problems: Many frameworks to choose and high framework studying cost. It is difficult for engineers to understand the mathematical principles behind them. Model Code

optimization requires deep expertise. Model Platform Compatibility Requirement. It requires extra coding work for the model to run on different platforms, such as Linux/Windows, GPU/CPU. The advantages of leveraging NeuronBlocks for NLP neural network model training includes: ***Model Building***: for model building and parameter tuning, users only need to write simple JSON config files, which greatly minimize the effort of implementing new ideas. ***Model Sharing*** It is super easy to share models just through JSON files, instead of

nasty codes. For different models or tasks, our users only need to maintain one single centralized source code base. ***Code Reusability***: Common blocks can be easily shared across various models or tasks, reducing duplicate coding work. ***Platform Flexibility***: NeuronBlocks can run both on Linux and Windows machines, using both CPU and GPU. It also supports training on GPU platforms like Philly and PAI. ***Model Visualization***: A model visualizer is provided for visualization and configure correctness checking, which helps users to

visualize the model architecture easily during debugging. ***Extensibility***: NeuronBlocks is extensible, allowing users to contribute new blocks or contributing novel