# MentalBERT V5 Cleanlab — Cooperative Source-Aware (Label-Noise Cleaned)

## Overview
8-class mental health text classifier trained on the V5 dataset after **Confident Learning**
(cleanlab) label-noise removal on the Depression/Suicidal boundary.

**Architecture:** Cooperative source-aware MentalBERT  
**Base model:** `mental/mental-bert-base-uncased`  
**Classes:** ['Anxiety', 'Bipolar', 'Depression', 'Directed Aggression', 'Normal', 'Personality Disorder', 'Stress', 'Suicidal']

## Cleaning Methodology
- **Tool:** cleanlab 2.6.x with `filter_by='prune_by_noise_rate'`
- **OOF probabilities:** 3-fold stratified cross-validation (2 epochs/fold)
- **Surgical scope:** Depression and Suicidal rows only, non-CSSRS sources only
- **CSSRS excluded:** CSSRS is the clinician-annotated trust anchor (κ=0.79) — never cleaned
- **Total drops:** 4,745 samples removed from train+val pool

## Performance
| Metric | Value |
|---|---|
| Test Accuracy | 83.14% |
| F1 Macro | 0.8384 |
| F1 Weighted | 0.8313 |
| Dep→Sui bleed | 692 |
| Sui→Dep bleed | 575 |
| Total bleed | 1,267 |

## Inference Usage
```python
import torch, torch.nn as nn
from transformers import BertModel, BertTokenizerFast
from huggingface_hub import hf_hub_download
import joblib, json

REPO = 'itsLu/mentalbert-v5-cleanlab'

bert      = BertModel.from_pretrained(REPO)
tokenizer = BertTokenizerFast.from_pretrained(REPO)
config    = json.load(open(hf_hub_download(REPO, 'inference_config.json')))

cls_head = nn.Sequential(nn.Dropout(0.1), nn.Linear(768, config['n_classes']))
cls_head.load_state_dict(torch.load(hf_hub_download(REPO, 'cls_head.pt'), map_location='cpu'))

le_path  = hf_hub_download(REPO, 'label_encoder.joblib')
le       = joblib.load(le_path)

def predict(text, device='cpu'):
    bert.to(device).eval(); cls_head.to(device).eval()
    enc = tokenizer(text, max_length=128, padding='max_length',
                    truncation=True, return_tensors='pt')
    with torch.no_grad():
        pooled = bert(enc['input_ids'].to(device),
                      enc['attention_mask'].to(device)).pooler_output
        logits = cls_head(pooled)
    probs = torch.softmax(logits, dim=-1).squeeze()
    pred_id = probs.argmax().item()
    return le.classes_[pred_id], probs[pred_id].item()
```

## Leaderboard Context
| Run | Acc | F1 Macro | Bleed |
|---|---|---|---|
| V3 Two-Branch v1 | 86.82% | 0.8469 | 726 |
| V5 Flat baseline | 82.41% | 0.8308 | 1,265 |
| V5 Cooperative source-aware | 83.23% | 0.8381 | 1,259 |
| V5 DANN (ablation) | 81.50% | 0.8245 | 1,373 |
| **V5 Cleanlab (this)** | **83.14%** | **0.8384** | **1,267** |

## Project
Helwan University — Faculty of Engineering, 2025–2026