Update README.md

README.md

@@ -1,10 +1,46 @@
 ---
-license: apache-2.0
-datasets:
-- stanfordnlp/imdb
-language:
-- en
-base_model:
-- google-bert/bert-base-uncased
-pipeline_tag: text-classification
----
+language: en
+license: mit
+tags:
+  - sentiment-analysis
+  - imdb
+  - bert
+  - transformers
+  - text-classification
+model-index:
+  - name: sentiment-bert-imdb
+    results:
+      - task:
+          type: text-classification
+          name: Sentiment Analysis
+        dataset:
+          name: IMDB Movie Reviews
+          type: imdb
+        metrics:
+          - type: accuracy
+            value: 0.93  # Replace with actual score if available
+---
+
+# Sentiment-BERT-IMDB
+
+A BERT-based model fine-tuned on the IMDB movie reviews dataset for **binary sentiment classification** (positive/negative). This model is intended for quick deployment and practical use in applications like review analysis, recommendation systems, and content moderation.
+
+## Model Details
+
+- **Architecture**: `bert-base-uncased`
+- **Task**: Sentiment classification (positive vs. negative)
+- **Dataset**: [IMDB](https://ai.stanford.edu/~amaas/data/sentiment/)
+- **Classes**: `positive`, `negative`
+- **Tokenizer**: `bert-base-uncased`
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+model = AutoModelForSequenceClassification.from_pretrained("HrishikeshDeore/sentiment-bert-imdb")
+tokenizer = AutoTokenizer.from_pretrained("HrishikeshDeore/sentiment-bert-imdb")
+
+nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+result = nlp("This movie was absolutely fantastic!")
+print(result)