AurelPx/hr-conversations-classifier

HR Conversations Multi-Label Classifier

A fine-tuned DistilBERT-base-uncased (66M parameters) for multi-label classification of HR support conversations.

Model Details

Attribute	Value
Base Model	distilbert/distilbert-base-uncased
Task	Multi-label text classification
Labels	20 HR topics
Training Data	100 synthetic HR conversations
Framework	Hugging Face Transformers

20 HR Topic Labels

1. Benefits
2. Career Development
3. Compliance & Legal
4. Contracts
5. Diversity, Equity & Inclusion
6. Expense Management
7. Harassment
8. Health
9. IT & Equipment
10. Leave & Absence
11. Mobility
12. Offboarding
13. Onboarding
14. Payroll
15. Performance Management
16. Recruitment
17. Safety
18. Timetracking
19. Training
20. Work Arrangements

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "AurelPx/hr-conversations-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

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"
]

def classify(text, threshold=0.3):
    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]
    return [LABELS[i] for i, p in enumerate(probs) if p >= threshold]

# Example
conversation = "USER: I haven't received my payslip for March yet..."
print(classify(conversation))  # ['Payroll']

Training Notes

• Dataset size: 100 conversations (small dataset)
• Split: 80 train / 20 validation
• Epochs: 4-8 with early stopping
• Limitations: With only 100 samples across 20 classes, the model is in a very low-data regime. For production use, collect >500 samples per label or apply data augmentation.

Links

• Dataset: AurelPx/ml-intern-a2d69eee-datasets