philipobiorah/bert-imdb-model

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

Evaluation

Model	SST-2 Accuracy	Yelp Accuracy	Amazon Accuracy	IMDB Accuracy
philipobiorah/bert-imdb-model	0.89	0.89	0.89	0.96
DistilBERT-SST-2	0.94	0.85	0.85	0.89
RoBERTa-Sentiment	0.40	0.42	0.47	0.79
Logistic Regression	0.83	0.91	0.86	0.85
Naive Bayes	0.77	0.86	0.84	0.85

Usage

Load the Model in 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 with confidence score
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
    
    # Convert logits to probabilities
    probabilities = torch.nn.functional.softmax(logits, dim=1)[0]
    
    # Get predicted class (0 = Negative, 1 = Positive)
    sentiment_idx = probabilities.argmax().item()
    confidence = probabilities[sentiment_idx].item() * 100  # Convert to percentage
    
    sentiment_label = "Positive" if sentiment_idx == 1 else "Negative"
    
    return {"sentiment": sentiment_label, "confidence": round(confidence, 2)}

# Test the model
result1 = predict_sentiment("This movie was absolutely fantastic!")
result2 = predict_sentiment("I really disliked this movie, it was terrible.")

print(f"Sentiment: {result1['sentiment']}, Confidence: {result1['confidence']}%")
print(f"Sentiment: {result2['sentiment']}, Confidence: {result2['confidence']}%")



from transformers import pipeline

pipe = pipeline("text-classification", model="philipobiorah/bert-imdb-model")

text_to_classify = "This movie was fantastic! I loved every minute of it."
result = pipe(text_to_classify)
print(result)

text_to_classify_2 = "The acting was terrible and the plot made no sense."
result_2 = pipe(text_to_classify_2)
print(result_2)