Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman With the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, PiyushīART (from Facebook) released with the paper BART: Denoising Sequence-to-Sequence

Hub where they are uploaded directly by users andĪLBERT (from Google Research and the Toyota Technological Institute at Chicago) released

The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!Īll the model checkpoints are seamlessly integrated from the model Seamlessly pick the right framework for training, evaluation, production Move a single model between Jax/PyTorch/TensorFlow frameworks at will Train state-of-the-art models in 3 lines of codeĭeep interoperability between Jax, Pytorch and TensorFlow models Practitioners can reduce compute time and production costsĨ architectures with over 30 pretrained models, some in more than 100 languagesĬhoose the right framework for every part of a model’s lifetime: Researchers can share trained models instead of always retraining Lower compute costs, smaller carbon footprint: Low barrier to entry for educators and practitioners Performance and Scalability: How To Fit a Bigger Model and Train It Faster.How to add a pipeline to 🤗 Transformers?.

Transformers 2 download free