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Transformers

A JAX-based implementation of various transformer models, including BERT, RoBERTa and other variants.

This is a collection of transformer models that can be used as a starting point for your own research or development projects. The models are implemented using the JAX library, which provides a high-performance and flexible framework for building machine learning models.

The transformers in this repository include:

• BERT (Bidirectional Encoder Representations from Transformers)
• RoBERTa (Robustly Optimized BERT Pretraining Approach)
• DistilBERT (Distilled version of BERT)
• XLNet (Extreme Language Modeling for Text Generation)

This repository provides a convenient way to access and use these transformer models in your own projects, with pre-trained weights available for download. You can also use this as a starting point to experiment with different architectures or modifications.