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README.md

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-license: mit
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+
+# BERT IMDb Sentiment Analysis Model
+
+This repository contains a fine-tuned BERT model for sentiment analysis on IMDb movie reviews. The model classifies text as either **Positive** or **Negative** sentiment.
+
+## Model Details
+- **Base Model**: `bert-base-uncased`
+- **Dataset**: IMDb Movie Reviews
+- **Task**: Sentiment Analysis (Binary Classification)
+- **Fine-tuned on**: IMDb dataset
+- **Labels**:
+  - `0`: Negative
+  - `1`: Positive
+
+## Usage
+
+### Load the Model using `transformers`
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+model_name = "philipobiorah/bert-imdb-model"
+
+# Load tokenizer and model
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+# Define function for sentiment prediction
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    return "Positive" if logits.argmax().item() == 1 else "Negative"
+
+# Test the model
+print(predict_sentiment("This movie was absolutely fantastic!"))
+print(predict_sentiment("I really disliked this movie, it was terrible."))
+```
+
+## Using the Inference API
+You can also use the Hugging Face Inference API to test the model:
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="philipobiorah/bert-imdb-model")
+print(classifier("This movie was amazing!"))
+```
+
+## Deploying as a Web App (Gradio)
+You can deploy this model using Gradio for an interactive UI:
+```python
+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="philipobiorah/bert-imdb-model")
+
+def predict(text):
+    return classifier(text)[0]['label']
+
+gr.Interface(fn=predict, inputs="text", outputs="label").launch()
+```
+
+## License
+This model is released under the **MIT License**.
+
+
+
+---
+license: mit
+---