Update model description

README.md

@@ -1,18 +1,26 @@
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-license: apache-2.0
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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).
-
-# T5 Small model IPU config
-
-This model contains just the `IPUConfig` files for running the BERT base model (e.g. [t5-small](https://huggingface.co/t5-small)) on Graphcore IPUs.
-
-**This model contains no model weights, only an IPUConfig.** 
-
-## Usage
-
-```
-from optimum.graphcore import IPUConfig
-ipu_config = IPUConfig.from_pretrained("Graphcore/t5-small-ipu")
+---
+license: apache-2.0
+---
+
+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).
+
+# T5 Small model IPU config
+
+This model contains just the `IPUConfig` files for running the BERT base model (e.g. [t5-small](https://huggingface.co/t5-small)) on Graphcore IPUs.
+
+**This model contains no model weights, only an IPUConfig.** 
+
+## Model description
+
+Text-to-Text Transfer Transformer (T5), is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces  an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks.  
+
+Paper link :[Exploring the Limits of Transfer Learning with a Unified
+Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) 
+
+
+## Usage
+
+```
+from optimum.graphcore import IPUConfig
+ipu_config = IPUConfig.from_pretrained("Graphcore/t5-small-ipu")
 ```