Update README.md

README.md

@@ -6,7 +6,7 @@ license: apache-2.0
 Learn more about how to take advantage of the power of Habana HPUs to train Transformers models at [hf.co/Habana](https://huggingface.co/Habana).
 
 
-# RoBERTa Base model HPU configuration
+## RoBERTa Base model HPU configuration
 
 This model contains just the `GaudiConfig` file for running the [roberta-base](https://huggingface.co/roberta-base) model on Habana's Gaudi processors (HPU).
 
@@ -25,17 +25,27 @@ This enables to specify:
 ## Usage
 
 The model is instantiated the same way as in the Transformers library.
-The only difference is that a Gaudi configuration associated to this model has to be loaded and provided to the trainer.
+The only difference is that the Gaudi configuration has to be loaded and provided to the trainer:
 
 ```
-from transformers import RobertaTokenizer, RobertaModel
-from optimum.habana import GaudiTrainer, GaudiTrainingArguments, GaudiConfig
+from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments
+from transformers import RobertaModel, RobertaTokenizer
 
-tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-model = RobertaModel.from_pretrained('roberta-base')
-gaudi_config = GaudiConfig.from_pretrained("Habana/roberta-base")
-args = GaudiTrainingArguments(output_dir=path_to_my_output_dir, use_habana=True, use_lazy_mode=True)
 
-trainer = GaudiTrainer(model=model, gaudi_config=gaudi_config, args=args, tokenizer=tokenizer)
+tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+model = RobertaModel.from_pretrained("roberta-base")
+gaudi_config = GaudiConfig.from_pretrained("Habana/roberta-base")
+args = GaudiTrainingArguments(
+    output_dir="/tmp/output_dir",
+    use_habana=True,
+    use_lazy_mode=True,
+)
+
+trainer = GaudiTrainer(
+    model=model,
+    gaudi_config=gaudi_config,
+    args=args,
+    tokenizer=tokenizer,
+)
 trainer.train()
 ```