1/11/2024 0 Comments Downloading Inside Out![]() ![]() With pipįirst you need to install one of, or both, TensorFlow 2.0 and PyTorch. If you'd like to play with the examples, you must install it from source. Now, if you want to use □ Transformers, you can install it with pip. If you're unfamiliar with Python virtual environments, check out the user guide.Ĭreate a virtual environment with the version of Python you're going to use and activate it. You should install □ Transformers in a virtual environment. This repo is tested on Python 3.5+, PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1 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 Upload and share your fine-tuned models with the community Using provided scripts: GLUE, SQuAD and Text generation Using Pipelines: Wrapper around tokenizer and models to use finetuned models ![]() 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.10 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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