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

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)

Base model

google-bert/bert-base-uncased

(6775)