README.md

---
license: mit
datasets:
- stanfordnlp/imdb
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- code
- sentiment-analysis
- bert
- imdb
- text-classification
- nlp
---
# 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.
## Live Demo: https://huggingface.co/spaces/philipobiorah/bert-sentiment-analysis
## 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("bert-base-uncased")
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."))