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Fix architectures count

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Julián Peller (dataista) 2019-10-22 16:11:02 -03:00 committed by Lysandre Debut
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@ -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.