README.md

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.

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Model Description

This model is a fine-tuned version of mental/mental-bert-base-uncased[18] 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)[18]
Architecture: BertForSequenceClassification with 4 output labels
Parameters: ~110M (BERT-Base: 12 layers, 768 hidden dimensions, 12 attention heads)[18]

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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[8][11].

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Training Data

Datasets

The model was trained on a combined dataset from multiple sources:

1. Suicide Detection Dataset

   • 232,074 samples
   • Kaggle link

2. Mental Health Social Media Posts

   • Reddit-based posts (downloaded from this folder)
   • Google Drive link

3. Sentiment Analysis for Mental Health

   • Over 44,000 samples
   • Kaggle link

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)[7]

Data Split

• Training: ~49,382 samples (balanced across 4 classes)[6]
• Validation: ~5,487 samples (10% holdout)[6]
• Test: 992 samples (248 per class, balanced)[5]

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Training Procedure

Hyperparameters

• Optimizer: AdamW
  • Learning rate: 2e-5
  • Weight decay: 1e-2
• Epochs: 5[6]
• Batch size: 16 (training), 32 (validation/test)[5][6]
• Max sequence length: 128 tokens[5]
• Scheduler: Linear warmup (10% of training steps)[6]
• Loss function: CrossEntropyLoss with class weights[6]

Training Environment

• Platform: Google Colab with GPU (Tesla T4)[6]
• Framework: PyTorch with Hugging Face Transformers 4.45.1[5]
• Training time: ~80.39 minutes (5 epochs)[6]

Training Phases

The model underwent 3 development phases:

1. Phase 1: Initial baseline training
2. Phase 2: Refined preprocessing and model optimization
3. Phase 3: Enhanced training with improved data balancing and class weights[5]

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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)[5]
• The model demonstrates strong generalization across all four mental health categories

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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")

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Limitations and Considerations

Known Limitations

1. Not a diagnostic tool: Cannot replace professional mental health assessment
2. Text-only analysis: Does not consider non-verbal cues, medical history, or clinical context
3. Class imbalance challenges: Some confusion between Depression and Anxiety categories
4. Language bias: Trained primarily on English text from social media
5. Cultural context: May not generalize well across different cultural expressions of mental health
6. 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

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Model Card Authors

Priyangshu Mukherjee

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Model Card Contact

For questions or concerns about this model:

• Email: priyangshumukherjeebtech24@rvu.edu.in
• Issues: [Create an issue in the model repository]

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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}
}

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Acknowledgments

• Base Model: mental/mental-bert-base-uncased by Shaoxiong Ji et al.[18]
• Frameworks: Hugging Face Transformers, PyTorch
• Datasets: Suicide Detection Dataset, Reddit Mental Health Posts
• Compute: Google Colab GPU resources

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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.

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Additional Resources

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

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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

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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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Last updated: October 2025