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Mental Health Text Classifier (MentalBERT Fine-tuned)

A fine-tuned BERT-based model for multi-class mental health text classification, achieving 89.7% accuracy on held-out test data.

Model Description

This model is a fine-tuned version of mental/mental-bert-base-uncased designed to classify text into four mental health categories:

Anxiety
Depression
Normal
Suicidal

Base Model: MentalBERT (BERT-Base uncased, pre-trained on mental health-related Reddit posts) Architecture: BertForSequenceClassification with 4 output labels
Parameters: ~110M (BERT-Base: 12 layers, 768 hidden dimensions, 12 attention heads)

Intended Use

Primary Use Cases

Research on mental health text analysis
Early detection support systems for mental health concerns in online social content
Sentiment analysis in mental health contexts
Supporting mental health monitoring and research

Target Users

Mental health researchers
Clinical researchers
Data scientists working on mental health NLP projects
Social workers and support organizations

⚠️ Important: This model is NOT intended for clinical diagnosis. It is a supplementary research tool and should not replace professional mental health evaluation or therapy. Model predictions are not psychiatric diagnoses, and anyone struggling with mental health issues should seek professional help.

Training Data

Datasets

The model was trained on a combined dataset from multiple sources, which were then integrated into a unified 4‑class corpus published as ourafla/Mental-Health_Text-Classification_Dataset.

Mental Health Text Classification Dataset (4‑Class)
- Curated, cleaned, and relabeled 4‑class dataset combining several public mental‑health corpora
- Hosted on Hugging Face Hub as:
  ourafla/Mental-Health_Text-Classification_Dataset
- Reference:
  
  Mukherjee, P. (2025). Mental Health Text Classification Dataset (4‑Class) [Dataset]. Hugging Face Hub. https://huggingface.co/datasets/ourafla/Mental-Health_Text-Classification_Dataset
Suicide Detection Dataset
- 232,074 samples
- Kaggle link
Mental Health Social Media Posts
- Reddit-based posts (downloaded from this folder)
- Google Drive link
Sentiment Analysis for Mental Health
- Over 44,000 samples
- Kaggle link

The final training corpus used for this model corresponds to the processed version released as ourafla/Mental-Health_Text-Classification_Dataset.

Data Preprocessing

Text normalization and cleaning
Label standardization across datasets
Duplicate removal
Class balancing to ensure equal representation (248 samples per class in test set)

Data Split

Training: ~49,382 samples (balanced across 4 classes)
Validation: ~5,487 samples (10% holdout)
Test: 992 samples (248 per class, balanced)

Training Procedure

Hyperparameters

Optimizer: AdamW
- Learning rate: 2e-5
- Weight decay: 1e-2
Epochs: 5
Batch size: 16 (training), 32 (validation/test)
Max sequence length: 128 tokens
Scheduler: Linear warmup (10% of training steps)
Loss function: CrossEntropyLoss with class weights

Training Environment

Platform: Google Colab with GPU (Tesla T4)
Framework: PyTorch with Hugging Face Transformers 4.45.1
Training time: ~80.39 minutes (5 epochs)

Training Phases

The model underwent 3 development phases:

Phase 1: Initial baseline training
Phase 2: Refined preprocessing and model optimization
Phase 3: Enhanced training with improved data balancing and class weights

Performance

Test Set Results (Phase 3)

Metric	Score
Accuracy	89.72%
Macro Precision	89.56%
Macro Recall	89.72%
Macro F1-Score	89.54%

Per-Class Performance

Class	Precision	Recall	F1-Score	Support
Anxiety	0.88	0.85	0.87	248
Depression	0.86	0.78	0.82	248
Normal	0.94	0.98	0.96	248
Suicidal	0.91	0.98	0.94	248

Key Observations

Strongest performance: Normal (96% F1) and Suicidal (94% F1) classes
Moderate performance: Anxiety (87% F1) and Depression (82% F1) classes
Challenge: Some confusion between Anxiety and Depression classes (common in mental health classification)
The model demonstrates strong generalization across all four mental health categories

Usage

Installation

pip install transformers torch

Basic Inference

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
model_name = "mental/mental-bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=4)

# Load fine-tuned weights
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.load_state_dict(torch.load("best_phase3.pth", map_location=device))
model.to(device)
model.eval()

# Define label mapping
id2label = {0: "Anxiety", 1: "Depression", 2: "Normal", 3: "Suicidal"}

# Example inference
text = "I've been feeling really overwhelmed and anxious lately"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()
    
print(f"Predicted class: {id2label[prediction]}")

Batch Inference

texts = [
    "I feel hopeless and don't see the point anymore",
    "Had a great day today, feeling positive!",
    "My heart is racing and I can't stop worrying"
]

inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)
    
for text, pred in zip(texts, predictions):
    print(f"Text: {text[:50]}...")
    print(f"Predicted: {id2label[pred.item()]}\n")

Limitations and Considerations

Known Limitations

Not a diagnostic tool: Cannot replace professional mental health assessment
Text-only analysis: Does not consider non-verbal cues, medical history, or clinical context
Class imbalance challenges: Some confusion between Depression and Anxiety categories
Language bias: Trained primarily on English text from social media
Cultural context: May not generalize well across different cultural expressions of mental health
Temporal limitations: Trained on historical data; language use evolves

Ethical Considerations

Privacy: All training data was from publicly available, anonymized sources
Bias: Model may reflect biases present in training data (Reddit demographics)
Responsible use: Should be used as a screening tool only, not for definitive diagnosis
Professional oversight: Any clinical applications must involve mental health professionals
Informed consent: Users should be aware that their text is being analyzed

Potential Biases

Reddit user demographics (younger, predominantly Western)
Self-reported mental health states (not clinically verified)
Language and expression styles specific to online communities
Underrepresentation of certain mental health conditions

Model Card Authors

Priyangshu Mukherjee

Model Card Contact

For questions or concerns about this model:

Email: [email protected]
Issues: [Create an issue in the model repository]

Citation

If you use this model in your research, please cite:

@software{mental_health_classifier_2025,
  author = {Mukherjee, Priyangshu},
  title = {Mental Health Text Classifier (MentalBERT Fine-tuned)},
  year = {2025},
  note = {Fine-tuned model for multi-class mental health text classification}
}

Base Model Citation:

@inproceedings{ji2022mentalbert,
  title = {{MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare}},
  author = {Shaoxiong Ji and Tianlin Zhang and Luna Ansari and Jie Fu and Prayag Tiwari and Erik Cambria},
  year = {2022},
  booktitle = {Proceedings of LREC}
}

Acknowledgments

Base Model: mental/mental-bert-base-uncased by Shaoxiong Ji et al.
Frameworks: Hugging Face Transformers, PyTorch
Datasets: Suicide Detection Dataset, Reddit Mental Health Posts
Compute: Google Colab GPU resources

License

This model is released for research and non-commercial use. Please check the base model license at mental/mental-bert-base-uncased for additional terms.

Additional Resources

MentalBERT Paper (LREC 2022)
Hugging Face Transformers Documentation
Mental Health Research Guidelines
[Crisis Resources: National Suicide Prevention Lifeline (US): 988]

Version History

v3.0 (Phase 3): Enhanced model with improved data balancing and class weights
- Test Accuracy: 89.72%
- Macro F1: 89.54%
- Balanced 4-class classification
v2.0 (Phase 2): Refined preprocessing and training procedures
v1.0 (Phase 1): Initial baseline model

Technical Specifications

Model Architecture

BertForSequenceClassification(
  (bert): BertModel(
    (embeddings): BertEmbeddings(...)
    (encoder): BertEncoder(
      12 x BertLayer(...)
    )
    (pooler): BertPooler(...)
  )
  (dropout): Dropout(p=0.1)
  (classifier): Linear(768 -> 4)
)

Tokenization

Tokenizer: BertTokenizer (uncased)
Vocabulary size: 30,522
Special tokens: [CLS], [SEP], [PAD], [UNK], [MASK]
Max position embeddings: 512 (used: 128)

Input Format

Text preprocessing: Lowercase, Unicode normalization
Padding: Max length (128 tokens)
Truncation: Enabled
Return type: PyTorch tensors

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Safetensors

Model size

0.1B params

Tensor type

F32

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