|Transformers Offers NLP For TensorFlow and PyTorch|
|Written by Kay Ewbank|
|Monday, 07 October 2019|
A Python library offering Natural Language Processing for TensorFlow 2.0 and PyTorch has been released by HuggingFace.
Transformers (formerly known as
Facebook developed ROBERTa, and the researchers describe their model as a robustly optimized method for pretraining natural language processing (NLP) systems that improves on Bidirectional Encoder Representations from Transformers, or BERT, the self-supervised method released by Google in 2018.
BERT and XLNet are models created by Google. BERT uses pre-training and fine-tuning to create NLP models tasks such as answering systems, sentiment analysis, and language inference, and is designed to pre-train deep bidirectional representations from unlabeled text. XLNet is an auto-regressive language model.
OpenAI created GPT-2, a transformer-based generative language model that was trained on 40GB of curated text from the internet.
HuggingFace themselves developed DistilBERT, which is based on BERT but uses a smaller language model with about half the total number of parameters of BERT base while retaining 95 percent of BERT’s performances on the language understanding benchmark GLUE.
The PyTorch version of the library has been installed more than 500,000 Pip installs this year. The library also includes an abstraction layer for each model to make it easier to integrate the model into a project. PyTorch-Transformers is already being used by large organisations including Microsoft and Apple.
The models included in Transformers are the best options for various NLP tasks, and some are very new. Their inclusion means anyone can make use of the many hours of training and large amounts of training data that has been undertaken by the original model creators using expensive GPU hardware which would be out of reach for developers unless they are working for a big technology company or research lab. The library makes this all available to anyone.
The library comes with 32 pretrained models in more than 100 languages, and the developers say it offers deep interoperability between TensorFlow 2.0 and PyTorch.
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