1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4model_id = "NanG01/m3-coercion-bert"
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForSequenceClassification.from_pretrained(model_id)
7model.eval()
8
9def predict_coercion(text: str) -> dict:
10 enc = tokenizer(text, return_tensors="pt", truncation=True,
11 padding="max_length", max_length=128)
12 with torch.no_grad():
13 label = model(**enc).logits.argmax(-1).item()
14 return {"chat_stress_language": label, "risk_pts": 20 if label == 1 else 0}
1predict_coercion("They said I must transfer 50000 rupees urgent right now")
2# → {"chat_stress_language": 1, "risk_pts": 20}
3
4predict_coercion("Please fast, he told me this is the last warning from income tax department")
5# → {"chat_stress_language": 1, "risk_pts": 20}
6
7predict_coercion("Jaldi karo, do minute vich transfer karna hai nahi tan khat khatam ho jaavega")
8# → {"chat_stress_language": 1, "risk_pts": 20}
9
10predict_coercion("How can I increase my monthly SIP amount?")
11# → {"chat_stress_language": 0, "risk_pts": 0}
12
13predict_coercion("Mera portfolio performance dikhao")
14# → {"chat_stress_language": 0, "risk_pts": 0}
