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0cd3d8d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | #!/usr/bin/env python3
"""Inference script for HR conversation multi-label classifier."""
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
MODEL_ID = "AurelPx/hr-conversations-classifier"
LABELS = [
"Benefits", "Career Development", "Compliance & Legal", "Contracts",
"Diversity, Equity & Inclusion", "Expense Management", "Harassment", "Health",
"IT & Equipment", "Leave & Absence", "Mobility", "Offboarding",
"Onboarding", "Payroll", "Performance Management", "Recruitment",
"Safety", "Timetracking", "Training", "Work Arrangements",
]
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()
def classify(text: str, threshold: float = 0.5):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).numpy()[0]
predicted = [LABELS[i] for i, p in enumerate(probs) if p >= threshold]
probs_dict = {LABELS[i]: round(float(p), 3) for i, p in enumerate(probs)}
return predicted, probs_dict
if __name__ == "__main__":
sample = (
"USER: I haven't received my payslip for March yet. Could you please check what's going on?\n"
"AGENT: Good morning. I've checked the payroll system and it appears your March payslip "
"was generated on the 28th but there was a distribution delay. I've resent it to your "
"registered email. You should receive it within the next hour."
)
preds, probs = classify(sample, threshold=0.5)
print(f"Predicted: {preds}")
print(f"Top probs: {sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]}")
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