license: apache-2.0
datasets:
- stanfordnlp/imdb
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- sentiment-analysis
- movie-analysis
- sentiment
- distilbert
- movie-reviews
Model Description
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:
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)