Noobie314/finetuned-roberta-mental-health

🧠 Finetuned RoBERTa for Mental Health Text Classification

This model is a fine-tuned version of roberta-base for detecting mental health-related categories from textual content. It classifies user-generated posts into five categories:

• 🟦 Depression
• 🟨 Anxiety
• 🔴 Suicidal
• 🟩 Addiction
• 🟪 Eating Disorder

It is designed to support research, digital therapy tools, and emotion-aware systems.

📁 Model Details

• Base model: mental-roberta-base
• Fine-tuned on: Custom Kaggle-aggregated dataset of mental health-related posts
• Output: Single-label classification (one of the five categories)
• Loss function: Cross-entropy
• Format: PyTorch model with Hugging Face Transformers compatibility

🧪 Dataset

The dataset used for training and testing was compiled from multiple Kaggle sources involving real-world discussions related to mental health. It contains posts categorized into the five emotion/mental-health topics.

• Training samples were selected from five original CSV files and combined into a single file.
• Testing data was kept separate and sourced similarly.

📦 You can find the dataset here: Noobie314/mental-health-posts-dataset

🛠️ How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = "Noobie314/finetuned-roberta-mental-health"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "I'm feeling hopeless and tired of everything..."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

# Get predicted label
predicted_class = outputs.logits.argmax(dim=1).item()

📊 Evaluation

The model was evaluated on a held-out test set with standard metrics:

• Accuracy: 78.32%
• F1 Score (macro): 82.22%
• Precision & Recall: Reported per class

Category	Precision	Recall	F1-Score	Support
Addiction	94.62%	91.40%	92.98%	1000
Anxiety	88.19%	82.31%	85.15%	1996
Depression	77.13%	72.86%	74.93%	3990
Eating Disorder	92.77%	93.60%	93.18%	1000
Suicidal	59.67%	71.01%	64.85%	1994

✅ Intended Uses

This model is intended for:

• Research on mental health-related NLP
• Emotion-aware content moderation
• Digital therapy assistants

⚠️ Disclaimer: This model is not intended for medical diagnosis or treatment. It should not be used as a substitute for professional mental health support.

📜 License

This project is licensed under the Apache 2.0 License.

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📬 For questions or collaborations, feel free to reach out through the Hugging Face hub.

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