quick fix wording readme for community models (#3900)

README.md

@@ -22,14 +22,11 @@
 <p>State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
 </h3>
 
-🤗 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, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
+🤗 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 TensorFlow 2.0 and PyTorch.
 
 [![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/0)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/0)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/1)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/1)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/2)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/2)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/3)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/3)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/4)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/4)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/5)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/5)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/6)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/6)[![](https://sourcerer.io/fame/clmnt/huggingface/transformers/images/7)](https://sourcerer.io/fame/clmnt/huggingface/transformers/links/7)
 
 ### Features
-
-- As easy to use as pytorch-transformers
-- As powerful and concise as Keras
 - High performance on NLU and NLG tasks
 - Low barrier to entry for educators and practitioners
 
@@ -41,7 +38,7 @@ 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
-- 10 architectures with over 30 pretrained models, some in more than 100 languages
+- Dozens of architectures with over 1,000 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