DistilBERT Sentiment Analysis Model

This model is a fine-tuned version of DistilBERT for sentiment analysis on the IMDb dataset. It classifies movie reviews as either positive or negative based on the text content.

Model Details

Model Type: DistilBERT (a smaller and faster variant of BERT)
Task: Sentiment Analysis
Dataset: IMDb dataset containing movie reviews with labels (positive/negative)
Fine-Tuned On: IMDb dataset

Model Performance

This model was fine-tuned on the IMDb dataset for sentiment classification, achieving good performance for binary sentiment classification tasks (positive/negative).

Usage

To use this model, you can load it from the Hugging Face Model Hub using the transformers library:

from transformers import pipeline

# Load the model
classifier = pipeline('sentiment-analysis', model='dorukan/distilbert-base-uncased-bert-finetuned-imdb')

# Example usage
result = classifier("This movie was amazing!")
print(result)

This will output a sentiment prediction for the given text.

License

This model is licensed under the MIT License. For more information, see the LICENSE file.

Acknowledgments

DistilBERT: A smaller version of BERT, created by the Hugging Face team.
IMDb Dataset: A collection of movie reviews used for sentiment classification, widely used in NLP tasks.

You can find more details about the model at the Hugging Face model page.