Add README with model description

README.md

@@ -1,15 +1,50 @@
+---
+language: en
+datasets:
+  - imdb
+metrics:
+  - accuracy
+model-index:
+  - name: BERT IMDB Sentiment Classifier
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          name: IMDB
+          type: imdb
+        metrics:
+          - type: accuracy
+            value: 0.93
+tags:
+  - sentiment
+  - imdb
+  - text-classification
+  - bert
+license: apache-2.0
+---
+
 # BERT IMDB Sentiment Classifier
 
 This model is a fine-tuned version of `bert-base-uncased` on the IMDB movie reviews dataset.
 
 ## Task
+
 Binary Sentiment Classification:
 - `0` → Negative
 - `1` → Positive
 
 ## Usage
+
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 model = AutoModelForSequenceClassification.from_pretrained("dina1/bert-imdb-sentiment")
 tokenizer = AutoTokenizer.from_pretrained("dina1/bert-imdb-sentiment")
+
+text = "This movie was absolutely wonderful!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+outputs = model(**inputs)
+
+predicted_class = outputs.logits.argmax().item()
+print("Predicted Sentiment:", predicted_class)