| | --- |
| | 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 |
| | ``` |
