license: apache-2.0
datasets:
- Noobie314/mental-health-posts-dataset
language:
- en
metrics:
- accuracy
- f1
- precision
base_model:
- mental/mental-roberta-base
pipeline_tag: text-classification
library_name: transformers
tags:
- RoBERTa
- mental-health-nlp
- emotion-classification
- mental-health-therapy
- fine-tuned-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.
📬 For questions or collaborations, feel free to reach out through the Hugging Face hub.