Update README.md

README.md

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-Graphcore and Hugging Face are working together to make training of Transformer models on IPUs fast and easy. Learn more about how to take advantage of the power of Graphcore IPUs to train Transformers models at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
+# Graphcore/lxmert-base-ipu
 
-# LXMERT model IPU config
+Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
 
-This model contains just the `IPUConfig` files for running the [lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) model on Graphcore IPUs.
+Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
 
-**This model contains no model weights, only an IPUConfig.** 
 
 ## Model description
 
-LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. It acheives the state-of-the-art results on VQA anad GQA. 
+LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-language text alignment, ROI-feature regression, masked visual-attribute modelling, masked visual-object modelling, and visual-question answering objectives. It achieves the state-of-the-art results on VQA and GQA.
 
 Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf) 
 
+
+## Intended uses & limitations
+
+This model contains just the `IPUConfig` files for running the [lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) model on Graphcore IPUs.
+
+**This model contains no model weights, only an IPUConfig.** 
+
 ## Usage
 
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