Update README.md

README.md

@@ -1,5 +1,56 @@
+# Create a README.md file with proper metadata
+model_card_content = """---
+language: en
+license: mit
+tags:
+- sentiment-analysis
+- text-classification
+- distilbert
+datasets:
+- imdb
+metrics:
+- accuracy
+model-index:
+- name: product-review-sentiment-analyzer
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Yelp
+      type: Yelp
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.92  # Replace with your actual accuracy
+---
+
 # Product Review Sentiment Analyzer
 
+This model classifies product reviews as positive, negative, or neutral. It was fine-tuned on the IMDB dataset using DistilBERT as the base model.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("yourusername/product-review-sentiment-analyzer")
+model = AutoModelForSequenceClassification.from_pretrained("arpitk/product-review-sentiment-analyzer")
+
+text = "This product exceeded my expectations!"
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    
+probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+prediction = torch.argmax(probabilities, dim=-1).item()
+
+labels = ["Negative", "Positive", "Neutral"]  # Adjust based on your model's output order
+print(f"Sentiment: {labels[prediction]}")
+```
+
 This project demonstrates how to build and deploy a sentiment analysis model for product reviews using free resources.
 
 ## Project Overview