Update README.md

README.md

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   results: []
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-# roberta-base-squad2
+# Graphcore/roberta-base-squad2
+
+
+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).
+
+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 is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
 
 ## 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 achieved state-of-the-art results on GLUE, RACE and SQuAD.
 
-As a result, it achieves state-of-the-art results on GLUE, RACE and SQuAD and so on on. 
 
 Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf)
 
+## Intended uses & limitations
+
+This model is a fine-tuned version of [HuggingFace/roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
+
 ## Training and evaluation data
 
-Trained and evaluated on the [squad_v2 dataset](https://huggingface.co/datasets/squad_v2).
+Trained and evaluated on the SQuAD v2 dataset:
+- [HuggingFace/squad_v2](https://huggingface.co/datasets/squad_v2).
 
 ## Training procedure