Pytorch bert


Nov 25, 2019 · Lightning does not add abstractions on to of PyTorch which means it plays nicely with other great packages like Huggingface! In this tutorial we’ll use their implementation of BERT to do a finetuning task in Lightning. In this tutorial we’ll do transfer learning for NLP in 3 steps: We’ll import BERT from the huggingface library. Models always output tuples ¶. The main breaking change when migrating from pytorch-pretrained-bert to transformers is that the models forward method always outputs a tuple with various elements depending on the model and the configuration parameters. from pytorch_pretrained_bert import BertTokenizer bert_tok = BertTokenizer. from_pretrained ("bert-base-uncased") BERT has multiple flavors, so we pass the class the name of the BERT model we’ll be using (in this post we’ll be using the uncased, smaller version). PyTorch-Transformers (formerly known as pytorch-pretrained-bert) is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models: