Update README.md

README.md

@@ -2,11 +2,31 @@
 license: apache-2.0
 ---
 
+# Model description
+
+A BertForSequenceClassification model that is finetuned on Wikipedia for zero-shot text classification. For details, see our NAACL'22 paper. 
+
+
 # Usage
 
+Concatenate the text sentence with each of the candidate labels as input to the model. The model will output a score for each label. Below is an example.
+
 ```
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 model = AutoModelForSequenceClassification.from_pretrained("CogComp/ZeroShotWiki")
+
+labels = ["sports", "business", "politics"]
+texts = ["As of the 2018 FIFA World Cup, twenty-one final tournaments have been held and a total of 79 national teams have competed."]
+
+with torch.no_grad():
+    for text in texts:
+        label_score = {}
+        for label in labels:
+            inputs = tokenizer(text, label, return_tensors='pt')
+            out = model(**inputs)
+            label_score[label]=float(torch.nn.functional.softmax(out[0], dim=-1)[0][0])
+        print(label_score)  # Predict the label with the highest score
 ```