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

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']}%")