Update README.md

README.md

@@ -35,7 +35,7 @@ To load the model:
 
 ```py
 from transformers import BertForSequenceClassification, AutoTokenizer
-model_path = 'biodatlab/MIReAD-Neuro'
+model_path = "biodatlab/MIReAD-Neuro"
 model = BertForSequenceClassification.from_pretrained(model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 ```
@@ -46,22 +46,21 @@ To create embeddings and for classification:
 # sample abstract & title text
 title = "Why Brain Criticality Is Clinically Relevant: A Scoping Review."
 abstract = "The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience..."
-
 text = title + tokenizer.sep_token + abstract
-
-tokens = tokenizer(text,
-                   max_length=512,
-                   padding=True,
-                   truncation=True,
-                   return_tensors="pt"
-                  )
-
-# for embeddings
+tokens = tokenizer(
+    text,
+    max_length=512,
+    padding=True,
+    truncation=True,
+    return_tensors="pt"
+)
+
+# to generate an embedding from a given title and abstract
 with torch.no_grad():
   output = model.bert(**tokens)
   embedding = output.last_hidden_state[:, 0, :]
 
-# for classification
+# to classify (200 journals) a given title and abstract
 output = model(**tokens)
 class = output.logits 
 ```