Welcome to SciWing’s documentation!¶
SciWING is a modular and easy to extend framework, that enables easy experimentation of modern techniques for Scholarly Document Processing. It enables easy addition of datasets, and models and provides tools to easily experiment with them.
SciWING is a modern framework from WING-NUS to facilitate Scientific Document Processing. It is built on PyTorch and believes in modularity from ground up and easy to use interface. SciWING includes many pre-trained models for fundamental tasks in Scientific Document Processing for practitioners. It has the following advantages
- Modularity - The framework embraces modularity from ground-up. SciWING helps in creating new models by combining multiple re-usable modules. You can combine different modules and experiment with new approaches in an easy manner
- Pre-trained Models -SciWING has many pre-trained models for fundamental tasks like Logical SectionClassifier for scientific documents, Citation string Parsing(Take a look at some of the other project related to station parsing Parscit, Neural_Parscit . Easy access to pre-trained models are made available through web APIs.
- Run from Config File- SciWING enables you to declare datasets, models and experiment hyper-params in a TOML file. The models declared in a TOML file have a one-one correspondence with their respective class declaration in a python file. SciWING parses the model to a Directed Acyclic Graph and instantiates the model using the DAG’s topological ordering.
- Extensible - SciWING enables easy addition of new datasets and provides command line tools for it. It enables addition of custom modules which are PyTorch modules.
- Installation and Getting Started
- Installation from Pip
- Building from source
- Running API Services
- Running the Demos
- Accessing Models
- Package Documentation