Update README.md

README.md

@@ -1,33 +1,31 @@
----
-license: apache-2.0
-tags:
-- text-classification
-datasets:
-- Mimic III
----
-
-# Clinical BERT for ICD-10 Prediction
-
-The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries.  
- 
----
-
-## How to use the model
-
-Load the model via the transformers library:
-
-    from transformers import AutoTokenizer, BertForSequenceClassification
-    tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
-    model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
-    config = model.config
-
-Run the model with clinical diagonosis text:
-
-    text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive"
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-
-Return the Top-5 predicted ICD-10 codes:
-
-    results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5]
+---
+license: apache-2.0
+tags:
+- text-classification
+---
+
+# Clinical BERT for ICD-10 Prediction
+
+The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries.  
+ 
+---
+
+## How to use the model
+
+Load the model via the transformers library:
+
+    from transformers import AutoTokenizer, BertForSequenceClassification
+    tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
+    model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction")
+    config = model.config
+
+Run the model with clinical diagonosis text:
+
+    text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive"
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+
+Return the Top-5 predicted ICD-10 codes:
+
+    results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5]
     return [ config.id2label[ids] for ids in results]