compasslkak.blogg.se

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.Dozens of architectures with over 1,000 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, T5, CTRL.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over thousands of pretrained models in 100+ languages and deep interoperability between PyTorch & TensorFlow 2.0. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0