nitz0219/bert-movie-review

BERT Movie Review Sentiment Analysis

A fine-tuned BERT model for binary sentiment classification on movie reviews.

Project Overview

This project fine-tunes bert-base-uncased on the IMDB movie reviews dataset to classify reviews as Positive or Negative.

Model Details

• Base Model: bert-base-uncased
• Task: Binary Sentiment Classification
• Dataset: IMDB Movie Reviews
• Training Samples: 2000
• Test Samples: 500
• Epochs: 2
• Accuracy: ~92%

Files

• model.safetensors — fine-tuned model weights
• config.json — model configuration
• tokenizer.json — tokenizer
• tokenizer_config.json — tokenizer configuration
• predict.py — inference script
• BERT_transformer_movie_review_project.ipynb — full training notebook

Usage

from transformers import BertTokenizer, BertForSequenceClassification
import torch

model_name = "nitz0219/bert-movie-review"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()

text = "This movie was absolutely fantastic!"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()

label = "Positive" if prediction == 1 else "Negative"
print(label)

Built By

Nitesh — AI/ML Engineer