NanG01/m3-coercion-bert

NLP Coercion Detector — SecureWealth Twin (M3)

Fine-tuned google/muril-base-cased for detecting stress and coercion language in bank AI chat messages across English, Hindi (Devanagari), and Punjabi (Gurmukhi).

Part of the SecureWealth Twin AI system — a bank-grade fraud detection and financial intelligence platform.

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Model Details

Base model	google/muril-base-cased
Task	Binary text classification
Languages	English · Hindi · Punjabi
Max sequence length	128
Training epochs	6 (early stopping, patience=2)
Learning rate	2e-5
Batch size	16
Dataset size	~490 rows

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Labels

Label	ID	Meaning
Normal	0	Regular chat message
Coercion	1	Stress / coercion language detected

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Usage

Load and run inference

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id  = "NanG01/m3-coercion-bert"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

def predict_coercion(text: str) -> dict:
    enc = tokenizer(text, return_tensors="pt", truncation=True,
                    padding="max_length", max_length=128)
    with torch.no_grad():
        label = model(**enc).logits.argmax(-1).item()
    return {"chat_stress_language": label, "risk_pts": 20 if label == 1 else 0}

Examples

predict_coercion("They said I must transfer 50000 rupees urgent right now")
# → {"chat_stress_language": 1, "risk_pts": 20}

predict_coercion("Please fast, he told me this is the last warning from income tax department")
# → {"chat_stress_language": 1, "risk_pts": 20}

predict_coercion("Jaldi karo, do minute vich transfer karna hai nahi tan khat khatam ho jaavega")
# → {"chat_stress_language": 1, "risk_pts": 20}

predict_coercion("How can I increase my monthly SIP amount?")
# → {"chat_stress_language": 0, "risk_pts": 0}

predict_coercion("Mera portfolio performance dikhao")
# → {"chat_stress_language": 0, "risk_pts": 0}

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Output

Field	Type	Description
chat_stress_language	int	1 = coercion detected · 0 = normal
risk_pts	int	+20 if coercion detected, 0 otherwise

chat_stress_language feeds directly into M4 Coercion Risk Scorer as one of 12 binary signals (+20 risk pts).

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Trigger Patterns

Common coercion indicators the model detects:

urgent · hurry · they said · he told me · please fast · last warning · account will be blocked · income tax · jaldi karo · abhi transfer karo

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Training Data

~490 chat messages across 3 languages:

• 90 English coercion/normal messages
• 400 Hindi coercion/normal messages
• Punjabi messages included

Dataset: SecureWealthTwin_DL_Datasets_v2.xlsx (private)

Regularisation applied:

• Frozen BERT encoder layers 0–9 (only top 2 layers + classifier trained)
• Dropout 0.3 on hidden, attention, and classifier layers
• Weight decay 0.01
• Gradient clipping (max norm 1.0)
• Early stopping (patience=2)

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Part of SecureWealth Twin

This model is M3 in a 6-model AI system:

#	Model	Task
M1	BehaviorDNA	Behavioural anomaly detection
M2	BERT Txn Classifier	Transaction categorisation
M3	NLP Coercion Detector	Coercion language detection
M4	Coercion Risk Scorer	Composite risk scoring
M5	Monte Carlo Simulator	Wealth projection
M6	Predictive Early Warning	Financial distress prediction

GitHub: BlackBox-Wealth/AI_Models_2

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license: apache-2.0