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583 Bytes
| import sys | |
| from transformers import pipeline | |
| # Define candidate labels for classification | |
| candidate_labels_spam = ['Spam', 'not Spam'] | |
| candidate_labels_urgent = ['Urgent', 'not Urgent'] | |
| model="SpamUrgencyDetection" | |
| clf = pipeline("zero-shot-classification", model=model) 32 | |
| def predict(text): | |
| p_spam = clf(text, candidate_labels_spam)["labels"][0] | |
| p_urgent = clf(text, candidate_labels_urgent)["labels"][0] | |
| return p_spam,p_urgent | |
| import pandas as pd | |
| df = pd.read_csv("test.csv") | |
| texts=df["text"] | |
| for i in range( len(texts)): | |
| print(texts[i],predict(texts[i])) | |