Create README.md

# Theme Model (Multi-Label Classifier)

This model performs **multi-label theme detection** for community knowledge submissions.
It classifies short narratives or summaries into one or more of the following themes:

- **Mentorship**
- **Entrepreneurship**
- **Startup Success**

## 🧩 Base Model
Fine-tuned from [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased).

## 📚 Training Data
Trained on a synthetic dataset (`theme_response.json`) consisting of community stories with labeled themes such as:
- “Mentorship strengthens community-based skill development.”
- “Entrepreneurship drives local economic growth and sustainability.”
- “Support programs help boost startup success rates.”

## ⚙️ Usage
```python
from transformers import pipeline

classifier = pipeline("text-classification", model="4nkh/theme_model", tokenizer="4nkh/theme_model", top_k=None)
text = "Our incubator helped women founders scale their startups."
preds = classifier(text)
print(preds)

README.md

@@ -0,0 +1,35 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- multi-label
+- theme
+- theme_detection
+- mentorship
+- entrepreneurship
+- startup_success
+- community
+- AI
+- JSON
+- Json_automation
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+library_name: transformers
+---
+        3---
+license: apache-2.0
+language:
+- en
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- multi-label
+---