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/roberta-large-ipu
 
-# RoBERTa Large 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 [roberta-large](https://huggingface.co/roberta-large) 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
-RoBERTa is based on BERT pretrain approach but it  t evaluates carefully a number of design decisions of BERT pretraining approach so that it found it is undertrained.  
+RoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained. 
 
-It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing mask pattern applied to the training data. 
+It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.
 
-As a result, it achieves state-of-the-art results on GLUE, RACE and SQuAD and so on on. 
+As a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.
 
 Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf)
 
+
+## Intended uses & limitations
+
+This model contains just the `IPUConfig` files for running the [roberta-large](https://huggingface.co/roberta-large) model on Graphcore IPUs.
+
+**This model contains no model weights, only an IPUConfig.** 
 ## Usage
 
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