Introducing FastBert — A simple Deep Learning library for BERT Models
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BERT What?
The little Sesame Street muppet has taken the world of Natural Language Processing by storm and the storm is picking up speed. We have seen a number of NLP problems solved by neural network architectures built on top of contextual representations of BERT. To name a few BERT based models have pushed the state of the art for SQUAD 2.0 question answering, GLUE multi task learning, Google natural questions task and Biomedical domain specific tasks — BioBERT.
Google research open sourced the TensorFlow implementation for BERT along with the pretrained weights. This opened the door for the amazing developers at Hugging Face who built the PyTorch port for BERT. With this library, geniuses i.e. developers and data scientists can use BERT models for text classification, question answering, fine tuning language model and more. Yours truly has contributed to the text classification capability by adding the feature for multi-label text classification.
Enter FastBert
FastBert is the deep learning library that allows developers and data scientists to train and deploy BERT based models for natural language processing tasks beginning with Text Classification. The work on FastBert is inspired by fast.ai and strives to make the cutting edge deep learning technologies accessible for the vast community of machine learning practitioners.
With FastBert, you will be able to:
- Train (more precisely fine-tune) BERT text classification models on your custom dataset
- Tune model hyper-parameters such as epochs, learning rate, batch size, optimiser schedule and more
- Save and deploy trained model for inference (including on AWS Sagemaker)
Starting today, FastBert will support both multi-class and multi-label text classification and in due course, it will support other NLU tasks such as Named Entity Recognition, Question Answering and Custom Corpus fine-tuning. I rely on the community to help make this happen :-)
Installation
pip install fast-bert
From Source: pip install git+https://github.com/kaushaltrivedi/fast-bert.git
