Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch. When TensorFlow 2.0 and/or PyTorch has been installed, □ Transformers can be installed using pip as follows: pip install transformers Please refer to TensorFlow installation page and/or PyTorch installation page regarding the specific install command for your platform. This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+), PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1 With pipįirst you need to install one of, or both, TensorFlow 2.0 and PyTorch. Migrating your code from pytorch-pretrained-bert to transformers Migrating from pytorch-pretrained-bert to pytorch-transformers Migrating your code from pytorch-transformers to transformers Migrating from pytorch-transformers to transformers Using provided scripts: GLUE, SQuAD and Text generation Train a TF 2.0 model in 10 lines of code, load it in PyTorch Tokenizers & models usage: Bert and GPT-2 Seamlessly pick the right framework for training, evaluation, productionĮxperimenting with this repo’s text generation capabilities.Move a single model between TF2.0/PyTorch frameworks at will.Deep interoperability between TensorFlow 2.0 and PyTorch models.Train state-of-the-art models in 3 lines of code. 8 architectures with over 30 pretrained models, some in more than 100 languagesĬhoose the right framework for every part of a model's lifetime.Practitioners can reduce compute time and production costs.Researchers can share trained models instead of always retraining.Lower compute costs, smaller carbon footprint Low barrier to entry for educators and practitioners.□ Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
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