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