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README.md

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+# DeBERTa Mental Health Classification Model
+
+A fine-tuned DeBERTa v3 small model for detecting mental health conditions from text.
+
+---
+
+tags:
+
+- text-classification
+- mental-health
+- deberta-v3
+- pytorch
+- transformers
+- sentiment-analysis
+- healthcare
+  language:
+- en
+  license: mit
+  datasets:
+- AIMH/SWMH
+  metrics:
+- accuracy
+- f1
+  pipeline_tag: text-classification
+
+---
+
+## Model Description
+
+This model is based on `microsoft/deberta-v3-small` and has been fine-tuned to classify text into 8 mental health categories.
+
+## Training Data
+
+This model was trained on the following datasets:
+
+- **SWMH (Social Media Mental Health Dataset)**: [AIMH/SWMH](https://huggingface.co/datasets/AIMH/SWMH)
+- **Sentiment Analysis for Mental Health**: [Kaggle Dataset](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)
+
+## Labels
+
+The model can classify text into the following categories:
+
+| ID  | Label                | Description                                         |
+| --- | -------------------- | --------------------------------------------------- |
+| 0   | Normal               | No mental health concerns detected                  |
+| 1   | Offmychest           | General venting/sharing                             |
+| 2   | Depression           | Depression-related content                          |
+| 3   | Anxiety              | Anxiety-related content                             |
+| 4   | Stress               | Stress-related content                              |
+| 5   | Bipolar              | Bipolar disorder-related content                    |
+| 6   | Personality disorder | Personality disorder-related content                |
+| 7   | Suicidal             | Suicidal ideation (⚠️ requires immediate attention) |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_path = "deberta-illness"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Example text
+text = "I've been feeling down lately and can't seem to enjoy anything anymore."
+
+# Tokenize and predict
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+# Get predicted label
+predicted_class = torch.argmax(predictions, dim=-1).item()
+confidence = predictions[0][predicted_class].item()
+
+print(f"Predicted: {model.config.id2label[str(predicted_class)]}")
+print(f"Confidence: {confidence:.2%}")
+```
+
+## Model Architecture
+
+- **Base Model:** microsoft/deberta-v3-small
+- **Hidden Size:** 768
+- **Attention Heads:** 12
+- **Hidden Layers:** 6
+- **Max Sequence Length:** 512 tokens
+- **Vocabulary Size:** 128,100
+
+## License
+
+Please refer to the original microsoft/deberta-v3-small license and any additional licensing terms from the fine-tuning dataset.