Fast.ai conveniences for BERT and XLNet classifiers

What it does

Fast-Bert is a higher-level Python library that sits on top of the Hugging Face transformers library and borrows the DataBunch and Learner patterns from fast.ai. It handles data ingestion, tokenization, and the training/validation lifecycle for multi-class and multi-label text classification using BERT, RoBERTa, XLNet, or DistilBERT. The goal is to let you point at a CSV and a pretrained model name rather than writing another custom training loop.

The interesting bit

The library treats the messy parts of transformer fine-tuning as infrastructure. It auto-instantiates the correct tokenizer from a model name string, manages multi-GPU and mixed-precision (FP16) setup, and borrows a learning-rate finder from another PyTorch project. The LAMB optimizer is also baked in, which the README claims helps with faster training.

Key highlights

• Supports multi-class and multi-label text classification using BERT, RoBERTa, XLNet, or DistilBERT
• Auto-instantiates the correct tokenizer and data loaders via a BertDataBunch object
• Encapsulates training, validation, metrics, and inference in a BertLearner object
• Includes a learning-rate finder and supports the LAMB optimizer and mixed-precision training
• Models can be exported for inference deployment, including on AWS SageMaker

Caveats

• Only text classification is implemented; NER and Question Answering are listed as future work
• README badges target Python 3.6 and 3.7, and installation requires NVIDIA Apex
• Loading fine-tuned language-model weights is marked as experimental

Verdict

Worth a look if you want fast.ai-like ergonomics for standard transformer classification tasks without writing glue code. Skip it if you need non-classification NLP tasks or full control over the training loop.