bert4torch

Why we included this project

bert4torch reimplements the bert4keras design as a standalone PyTorch library, so you can load and fine-tune transformer models without adopting the full Hugging Face training stack. One uniform API covers classic checkpoints like BERT, RoBERTa, ALBERT, XLNet, ELECTRA, BART and T5 for jobs such as text classification, named-entity recognition and relation extraction, and the same interface can also load open-source LLM weights such as ChatGLM, Llama and BLOOM for inference and light fine-tuning. That is useful if you want a single codebase that moves from small encoder models to larger decoder LLMs. The implementation stays explicit and layer by layer, which makes it easier to read and modify than a black-box wrapper, a real plus for researchers and small teams who want to see what their model actually does. In short, it offers Keras-style training simplicity with PyTorch underneath.