@@ -16,10 +16,10 @@ Here are some information on these models:
This PyTorch implementation of BERT is provided with [Google's pre-trained models](https://github.com/google-research/bert), examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided.
**OpenAI GPT** was released together with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
This PyTorch implementation of OpenAI GPT is provided with [OpenAI's pre-trained model](https://github.com/openai/finetune-transformer-lm) and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch.
This PyTorch implementation of OpenAI GPT is an adaptation of the [PyTorch implementation by HuggingFace](https://github.com/huggingface/pytorch-openai-transformer-lm) and is provided with [OpenAI's pre-trained model](https://github.com/openai/finetune-transformer-lm) and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch.
**Google/CMU's Transformer-XL** was released together with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](http://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
This PyTorch implementation of Transformer-XL is an adaptation of the original [PyTorch implementation](https://github.com/kimiyoung/transformer-xl) which has been slightly modifier to match the performances of the TensforFlow implementation and allow to re-use the pretrained weights. A command-line interface is provided to convert TensorFlow checkpoints in PyTorch models.
This PyTorch implementation of Transformer-XL is an adaptation of the original [PyTorch implementation](https://github.com/kimiyoung/transformer-xl) which has been slightly modified to match the performances of the TensforFlow implementation and allow to re-use the pretrained weights. A command-line interface is provided to convert TensorFlow checkpoints in PyTorch models.
