If you want to reproduce the original tokenization process of the OpenAI GPT paper, you will need to install ftfy (limit to version 4.4.3 if you are using Python 2) and SpaCy : pip install spacy ftfy = 4.4.3 PyTorch pretrained bert can be installed by pip as follows: pip install pytorch-pretrained-bert This repo was tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 0.4.1/1.0.0 With pip Notes on TPU support and pretraining scriptsĬonvert a TensorFlow checkpoint in a PyTorch dump Introduction on the provided Jupyter Notebooks Content Sectionĭetailed examples on how to fine-tune Bert This PyTorch implementation of OpenAI GPT-2 is an adaptation of the OpenAI's implementation and is provided with OpenAI's pre-trained model and a command-line interface that was used to convert the TensorFlow checkpoint in PyTorch. OpenAI GPT-2 was released together with the paper Language Models are Unsupervised Multitask Learners by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**. 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 which has been slightly modified to match the performances of the TensorFlow implementation and allow to re-use the pretrained weights. Google/CMU's Transformer-XL was released together with the paper Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. This PyTorch implementation of OpenAI GPT is an adaptation of the PyTorch implementation by HuggingFace and is provided with OpenAI's pre-trained model and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch. OpenAI GPT was released together with the paper Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. This PyTorch implementation of BERT is provided with Google's pre-trained models, examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided. Here are some information on these models:īERT was released together with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. You can find more details in the Examples section below. ![]() ~91 F1 on SQuAD for BERT, ~88 F1 on RocStories for OpenAI GPT and ~18.3 perplexity on WikiText 103 for the Transformer-XL). ![]() These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for: PyTorch Pretrained BERT: The Big & Extending Repository of pretrained Transformers
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