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docs/source/en/index.mdx

@@ -16,7 +16,7 @@ State-of-the-art Transformers models in PyTorch, TensorFlow, and JAX for researc
 
 🤗 Transformers is dedicated to providing inference, finetuning, and pretraining for Transformer-based models on text, computer vision, speech, and multimodal tasks. Each model is defined by three classes - a configuration, a model, and a preprocessor - which can be initialized from pretrained instances stored on the [Hugging Face Hub](https://huggingface.co/models). The library also offers convenient helper tools like a simple pipeline to use a model for inference on a given task and a feature-complete training loop optimized for 🤗 Transformers models.
 
-Models are available in PyTorch, TensorFlow, and JAX. This framework interoperability grants the flexibility to use a different framework at each stage of a model's lifetime. 🤗 Transformers also supports exporting a model to a format like ONNX and TorchScript for deployment in production environments.
+🤗 Transformers supports framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another framework. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
 
 Join the growing community on the [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), or [Discord](https://discord.com/invite/JfAtkvEtRb) today.