Create README.md

README.md

@@ -0,0 +1,64 @@
+---
+license: apache-2.0
+tags:
+  - sentiment-analysis
+  - distillation
+  - small-model
+  - smollm
+  - nlp
+model-index:
+  - name: distilled-smollm-sentiment-analyzer
+    results:
+      - task:
+          type: sentiment-analysis
+        dataset:
+          name: Custom Distillation Dataset
+          type: text
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: ~XX.XX%
+---
+
+# Distilled SmollM Sentiment Analyzer
+
+This model is a distilled version of a larger sentiment analysis model, fine-tuned on custom datasets using the [Hugging Face Transformers](https://huggingface.co/docs/transformers) library. It is designed for **efficient, lightweight sentiment analysis** tasks in resource-constrained environments.
+
+✅ **Key Features:**
+- Compact model architecture (`SmollM`)
+- Distilled for speed and smaller size
+- Fine-tuned for sentiment classification tasks
+- Supports labels: `negative`, `neutral`, `positive`
+
+---
+
+## 🔍 Model Details
+
+| Model | Distilled SmollM Sentiment Analyzer |
+|:------|:------------------------------------|
+| Base Model | SmollM  |
+| Task | Sentiment Analysis (3-class: negative, neutral, positive) |
+| Dataset | Custom Yelp Review + Distilled Dataset |
+| Framework | Hugging Face Transformers |
+| Distillation Method | Knowledge Distillation |
+| Accuracy | ~75% (Relative Accuracy - compared with Teacher model gemma3:12b) |
+
+---
+
+## 🚀 Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("AhilanPonnusamy/distilled-smollm-sentiment-analyzer")
+model = AutoModelForSequenceClassification.from_pretrained("AhilanPonnusamy/distilled-smollm-sentiment-analyzer")
+
+inputs = tokenizer("The movie was amazing!", return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = logits.argmax().item()
+
+label_map = {0: "negative", 1: "neutral", 2: "positive"}
+print("Predicted sentiment:", label_map[predicted_class_id])