Update README.md

README.md

@@ -33,4 +33,59 @@ model-index:
       value: 0.8979
 library_name: transformers
 pipeline_tag: text-classification
----
+---
+# Facebook Post Classifier (RoBERTa Base, fine-tuned)
+
+This model classifies short Facebook posts into **one** of the following **three mutually exclusive categories**:
+- `Appreciation`
+- `Complaint`
+- `Feedback`
+
+It is fine-tuned on ~8k manually labeled posts from business pages (e.g. Target, Walmart), based on the `cardiffnlp/twitter-roberta-base` model, which is pretrained on 58M tweets.
+
+## 🧠 Intended Use
+
+- Customer support automation
+- Sentiment analysis on social media
+- CRM pipelines or chatbot classification
+
+## 📊 Performance
+
+| Class        | Precision | Recall | F1 Score |
+|--------------|-----------|--------|----------|
+| Appreciation | 0.906     | 0.936  | 0.921    |
+| Complaint    | 0.931     | 0.902  | 0.916    |
+| Feedback     | 0.840     | 0.874  | 0.857    |
+| **Average**  | –         | –      | **0.898** |
+
+> Evaluated on 2039 unseen posts with held-out labels using macro-averaged F1.
+
+## 🛠️ How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from torch.nn.functional import softmax
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained("your-username/fb-post-classifier-roberta")
+tokenizer = AutoTokenizer.from_pretrained("your-username/fb-post-classifier-roberta")
+
+inputs = tokenizer("I love the fast delivery!", return_tensors="pt")
+outputs = model(**inputs)
+probs = softmax(outputs.logits, dim=1)
+
+label = torch.argmax(probs).item()
+classes = ["Appreciation", "Complaint", "Feedback"]
+print("Predicted:", classes[label])
+```
+
+## 🧾 License
+MIT License
+
+## 🙋‍♀️ Author
+This model was fine-tuned by @harshithan.
+
+## 📚 Academic Disclaimer
+This model was developed as part of an academic experimentation project. It is intended solely for educational and research purposes.
+The model has not been validated for production use and may not generalize to real-world Facebook or customer support data beyond the scope of the assignment.
+