From e16d46843a19ab289b82138e4eccec5610a76de7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20Peller=20=28dataista=29?= Date: Tue, 22 Oct 2019 16:11:02 -0300 Subject: [PATCH] Fix architectures count --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ad771f2ab1..e8506d6a39 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,7 @@ State-of-the-art NLP for everyone Lower compute costs, smaller carbon footprint - Researchers can share trained models instead of always retraining - Practitioners can reduce compute time and production costs -- 8 architectures with over 30 pretrained models, some in more than 100 languages +- 10 architectures with over 30 pretrained models, some in more than 100 languages Choose the right framework for every part of a model's lifetime - Train state-of-the-art models in 3 lines of code @@ -111,7 +111,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training ## Model architectures -🤗 Transformers currently provides 8 NLU/NLG architectures: +🤗 Transformers currently provides 10 NLU/NLG architectures: 1. **[BERT](https://github.com/google-research/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. 2. **[GPT](https://github.com/openai/finetune-transformer-lm)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.