Vinuit/SentinelAI-Filter-ONNX

SentinelAI BERT Filter - LoRA Adapters

LoRA fine-tuned BERT model for employee mental health classification in workplace messages. Part of the SentinelAI system for automated burnout detection via Slack message analysis.

Model Description

• Base Model: bert-base-uncased (110M parameters)
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Task: Dual-head classification (category + severity)
• Trainable Parameters: 303,371 / 109,785,611 (0.28%)
• Developed by: Team Rocket Number One, King's College London
• License: MIT (project-specific)

Architecture

Dual-Head Classifier:

• Category Head: 7-class classification
  • neutral, humor_sarcasm, stress, burnout, depression, harassment, suicidal_ideation
• Severity Head: 4-stage classification
  • none, early, middle, late
• Binary Routing: 5 risk categories trigger escalation to LLM agents

Training Details

Dataset

• Total Examples: 7,000 (mixed dataset for quality + diversity)
  • 5,000 from v0.1 (natural Slack-style phrasing)
  • 2,000 from v0.2 (lexically diverse, synonym-enhanced)
• Splits: 80% train (5,600), 10% val (700), 10% test (700)
• Lexical Diversity: TTR 0.35 (exceeds 0.3 quality threshold)
• Clinical Grounding: Maslach Burnout Inventory (MBI), DSM-5, UK Equality Act 2010

Hyperparameters

LoRA Configuration:
  r: 8
  lora_alpha: 16
  lora_dropout: 0.1
  target_modules: ["query", "value"]
  task_type: FEATURE_EXTRACTION

Training:
  epochs: 3
  batch_size: 16
  learning_rate: 3e-4
  optimizer: AdamW
  scheduler: Linear warmup + decay
  max_sequence_length: 128
  loss_function: CrossEntropyLoss (category + severity summed)

Hardware & Performance

• GPU: NVIDIA GeForce GTX 1080 (8GB VRAM)
• Training Time: ~3 minutes (1 min/epoch)
• Training Regime: fp32

Results

Test Set Performance

Metric	Score
Category Accuracy	76.29%
Severity Accuracy	78.29%
Test Loss	1.1840

Performance Context:

• Category: 5.3x better than random (7-class baseline: 14.3%)
• Severity: 3.1x better than random (4-class baseline: 25%)
• Low/no overfitting: Test accuracy matches validation accuracy

Usage

Loading the Model (Production Pattern)

The repository uses a centralised Model Factory to handle architecture initialisation and weight loading. It includes Auto-Download logic that pulls the latest checkpoint from Hugging Face Hub if it is not found locally.

import torch
from services.model_factory import load_production_model

# Inference
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# This will:

# 1. Initialise DualHeadBERTClassifier
# 2. Apply LoRA adapters
# 3. Check for 'dual_head_classifier.pt' locally
# 4. If missing, download latest from OguzhanKOG/sentinelai-bert-filter
# 5. Load trained weights and return model in eval mode

model = load_production_model(device=device)

# Model is ready for inference
message = "I'm completely overwhelmed with work and can't sleep anymore"
# ... standard tokenization using config.MODEL_NAME ...

Configuration

All parameters (LoRA rank, Alpha, Model Backbone, Paths) are centralised in filter/config.py. To change the backbone or parameters across the entire service, update this file only.

Limitations

• Synthetic Training Data: Model trained on generated examples, not real workplace messages
• English Only: No multilingual support
• Context Window: Limited to 128 tokens (Slack message-sized)
• Not a Clinical Tool: Designed for workplace wellbeing monitoring, not medical diagnosis
• Bias Risk: May reflect biases in synthetic data generation process

Intended Use

Primary Use Case: Fast, cost-effective gatekeeper filter in SentinelAI architecture. Routes high-risk messages to expensive LLM agents for detailed analysis, while filtering out low-risk neutral messages.

Architecture Position:

Slack Message → BERT Filter (this model) → [if risk] → LLM Agent Analysis → HR Alert

Not Intended For:

• Clinical diagnosis or medical decision-making
• Standalone mental health assessment
• Real-time crisis intervention (human oversight required)
• Legal or disciplinary actions without human review

Training Logs

Full training metrics available in training_log.json:

• Epoch-by-epoch train/val losses
• Category and severity accuracies per epoch
• Final test set evaluation results

Repository

Full implementation available in the project repository (Private).

Branch: feature/filter

Framework versions

• PEFT 0.18.1