Update model description

README.md

@@ -1,18 +1,24 @@
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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).
-
-# BART Base model IPU config
-
-This model contains just the `IPUConfig` files for running the BART base model (e.g. [facebook/bart-base](https://huggingface.co/facebook/bart-base)) on Graphcore IPUs.
-
-**This model contains no model weights, only an IPUConfig.** 
-
-## Usage
-
-```
-from optimum.graphcore import IPUConfig
-ipu_config = IPUConfig.from_pretrained("Graphcore/bart-base-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).
+
+# BART Base model IPU config
+
+This model contains just the `IPUConfig` files for running the BART base model (e.g. [facebook/bart-base](https://huggingface.co/facebook/bart-base)) on Graphcore IPUs.
+
+**This model contains no model weights, only an IPUConfig.** 
+
+## Model description
+
+BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. 
+
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). 
+
+## Usage
+
+```
+from optimum.graphcore import IPUConfig
+ipu_config = IPUConfig.from_pretrained("Graphcore/bart-base-ipu")
 ```