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Add visual + link to Premium Support webpage (#11740) 

* Update README.md

* Update index.rst
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README.md
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@ -38,7 +38,7 @@ limitations under the License.
</p>
<h3 align="center">
<p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow
<p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow</p>
</h3>
🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.
@ -62,6 +62,12 @@ Here are a few examples:
**[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.
## If you are looking for custom support from the Hugging Face team
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
## Quick tour
To immediately use a model on a given text, we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:

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docs/source/index.rst
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This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`_.
If you are looking for custom support from the Hugging Face team
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.. raw:: html
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
Features
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