Main features — TextBrewer 0.2.1.post1 documentation (original) (raw)
TextBrewer is a PyTorch-based model distillation toolkit for natural language processing.
It includes various distillation techniques from both NLP and CV field and provides an easy-to-use distillation framework, which allows users to quickly experiment with the state-of-the-art distillation methods to compress the model with a relatively small sacrifice in the performance, increasing the inference speed and reducing the memory usage.
- Wide-support : it supports various model architectures (especially transformer-based models).
- Flexibility : design your own distillation scheme by combining different techniques.
- Easy-to-use : users don’t need to modify the model architectures.
- Built for NLP : it is suitable for a wide variety of NLP tasks: text classification, machine reading comprehension, sequence labeling, …
Paper: TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
Getting Started
Experiments
API Reference
Appendices