Update README.md

README.md

@@ -24,18 +24,18 @@ model-index:
 
 # distilbert-phmtweets-sutd
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for text classification to identify public health events through tweets. The dataset was used in an [Emory University Study on Detection of Personal Health Mentions in Social Media](https://arxiv.org/pdf/1802.09130v2.pdf), with this [custom dataset](https://github.com/emory-irlab/PHM2017).
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017).
 
 It achieves the following results on the evaluation set:
 - Accuracy: 0.877
 
 ## Usage
 
-`from transformers import AutoTokenizer, AutoModelForSequenceClassification`
-
-`tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")`
-
-`model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")`
+```Python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
+model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
+```
 
 
 ### Model Evaluation Results