readme: ---
  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("CogComp/ZeroShotWiki")
  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
  ```