| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - mental-health |
| - text-classification |
| - mentalbert |
| pipeline_tag: text-classification |
| widget: |
| - text: "I have been feeling hopeless for months and I just want it to end." |
| example_title: Suicidal |
| - text: "My heart races constantly and I can't stop worrying." |
| example_title: Anxiety |
| - text: "I will kill every single one of you, you will regret it." |
| example_title: Directed Aggression |
| --- |
| |
| # MentalBERT V5 — Flat 8-Class Mental Health Classifier |
|
|
| Single-pass MentalBERT fine-tuned on the V5 mental-health dataset. Predicts one of 8 classes: |
|
|
| `Anxiety`, `Bipolar`, `Depression`, `Directed Aggression`, `Normal`, `Personality Disorder`, `Stress`, `Suicidal`. |
|
|
| ## Test Set Results (V5 stratified 70/10/20, random_state=42) |
| |
| | Metric | Value | |
| |---|---| |
| | Accuracy | 82.84% | |
| | F1 macro | 0.8350 | |
| | F1 weighted | 0.8280 | |
| | Sui→Dep (missed crises) | 516 | |
| | Total Dep↔Sui bleed | 1249 | |
| | ROC AUC (macro) | 0.9638 | |
| |
| ## Quick Start (Python) |
| |
| ```python |
| from transformers import pipeline |
| |
| clf = pipeline("text-classification", model="<YOUR_USERNAME>/mentalbert-v5-flat-8class") |
| result = clf("I haven't slept in days, I feel like everything is falling apart.") |
| print(result) # [{'label': 'Stress', 'score': 0.87}] |
| ``` |
| |
| ## API Call (HF Inference Endpoint) |
| |
| ```python |
| import requests |
| HF_TOKEN = "hf_..." |
| URL = "https://api-inference.huggingface.co/models/<YOUR_USERNAME>/mentalbert-v5-flat-8class" |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} |
| r = requests.post(URL, headers=headers, json={"inputs": "I want to end it all."}) |
| print(r.json()) # [{'label': 'Suicidal', 'score': 0.91}, ...] |
| ``` |
| |
| For top-k probabilities over all classes, pass `{"inputs": text, "parameters": {"top_k": 8}}`. |
| |
| ## Limitations |
| |
| - This is a screening signal, not a clinical diagnosis. Use only as one input among many. |
| - Sui→Dep (516) errors are missed crisis cases. Pair with a safety threshold or with the |
| hierarchical companion model (`mentalbert-v5-hierarchical-longformer`) for safety-critical applications. |
| - Trained on Reddit-style English text; out-of-distribution domains (clinical notes, formal prose) may degrade. |
| |
| ## Training Details |
| |
| - Backbone: `mental/mental-bert-base-uncased` |
| - MAX_LEN: 128, batch=32, LR=2e-05, epochs=4, label_smoothing=0.05 |
| - Imbalance: `WeightedRandomSampler` + class-weighted CrossEntropy (cap=3.0) |
| - Hardware: NVIDIA T4 (Kaggle) |
| |
