appvision-ai/fast-bert

Health v2 · maintenance only

Adoption not part of the score

1917 stars · 339 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Fast-Bert is a Python deep learning library for training and deploying BERT, RoBERTa, and XLNet based models for NLP tasks, starting with multi-class and multi-label text classification. It wraps the Hugging Face PyTorch BERT library with a fast.ai-inspired API, including a learning rate finder, LAMB optimizer support, and AWS Sagemaker deployment.

Use cases

• fine-tune bert for text classification on my own dataset
• train a multi-label text classifier with transformers
• compare bert xlnet roberta for classification
• find the best learning rate for transformer fine-tuning
• deploy a bert model to aws sagemaker for inference
• train distilbert classifier with minimal code

When to choose

• you want a simple high-level API for BERT-style text classification
• you need multi-class or multi-label classification with BERT, RoBERTa, or XLNet
• you want built-in learning rate finding and LAMB optimizer support
• you plan to deploy trained models on AWS Sagemaker

When to avoid

• you need NLU tasks beyond text classification like NER or question answering
• you want the latest transformer features and broad model support (prefer Hugging Face Transformers)
• you need actively developed features or support for recent Python versions
• you are not working in PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning nlp llm-training machine-learning deep-learning python bert xlnet roberta text-classification transformers fastai fine-tuning pytorch sagemaker natural-language-processing

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markdown · JSON · MCP: product_card(name="appvision-ai/fast-bert")