README.md

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 using transformers

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
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
    return "Positive" if logits.argmax().item() == 1 else "Negative"

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