Create README.md

README.md

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+---
+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](https://huggingface.co/datasets/Noobie314/mental-health-posts-dataset)
+
+## 🛠️ How to Use
+
+```python
+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](https://www.apache.org/licenses/LICENSE-2.0).
+
+---
+
+📬 For questions or collaborations, feel free to reach out through the Hugging Face hub.
+
+---