SDK for BERT/XLNet-based NLP model training and deployment
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This library provides a simplified interface for fine-tuning and deploying BERT, RoBERTa, and XLNet models for text classification and language model tasks. It targets NLP practitioners and data scientists looking to leverage state-of-the-art transformer models with reduced boilerplate code, offering features like learning rate finding and FP16 training for efficiency.
How It Works
Fast-BERT abstracts the complexities of Hugging Face's Transformers library by providing BertDataBunch
and BertLearner
classes. BertDataBunch
handles data loading, preprocessing, and tokenization, supporting both multi-class and multi-label classification. BertLearner
encapsulates the training loop, optimization (including LAMB optimizer), metric calculation, and model saving/loading. It also includes a learning rate finder and supports FP16 training for faster execution.
Quick Start & Requirements
pip install fast-bert
Highlighted Details
Maintenance & Community
The project is actively maintained by kaushaltrivedi. Further community engagement details are not explicitly provided in the README.
Licensing & Compatibility
The library is released under the MIT License, allowing for commercial use and integration with closed-source projects.
Limitations & Caveats
The README mentions that the library is tested on Python 3.6+, implying potential compatibility issues with newer Python versions. Installation requires NVIDIA Apex, which can add complexity to the setup process.
11 months ago
Inactive