metadata

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 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']}%")