This model is a distilbert-base-uncased fine-tuned for sentiment analysis on the IMDb movie review dataset. The model is trained to classify movie reviews into positive or negative sentiment.

Intended Use

The model is intended for sentiment analysis tasks, specifically to classify the sentiment of English-language movie reviews. It can be used by developers or data scientists who wish to include sentiment analysis features in their applications.

Training Data

The model was fine-tuned on the IMDb movie review dataset available from the Hugging Face datasets library. The dataset consists of 50,000 movie reviews from IMDb, labeled as positive or negative.

Training Procedure

The model was fine-tuned for 2 epochs with a batch size of 8, Adam optimizer with a learning rate of 2e-5.

Ethical Considerations

This model may inherit biases present in the IMDb dataset, and its predictions should be reviewed with critical consideration, especially if used in sensitive contexts.

Sample Usage in Python

Here's how you can use this model in Python:

python

from transformers import pipeline

# Load the sentiment analysis pipeline
classifier = pipeline('sentiment-analysis', model='sarahai/movie-sentiment-analysis')

# Analyze sentiment
review = "I really enjoyed this movie from start to finish!"
result = classifier(review)

print(result)