kparkhade/Fine-tuned-BERT-Imdb

Fine-Tuned BERT for IMDB Sentiment Classification

Model Description

This is a fine-tuned version of BERT-Base-Uncased for binary sentiment classification on the IMDB dataset. The model is trained to classify movie reviews as either positive or negative.

Model Details

• Base Model: BERT-Base-Uncased
• Dataset: IMDB Movie Reviews
• Languages: English (en)
• Fine-tuning Epochs: 3
• Batch Size: 8
• Evaluation Metrics: Accuracy, Precision, Recall
• License: Apache 2.0

Usage

Load the Model

from transformers import BertForSequenceClassification, BertTokenizer

model_name = "kparkhade/Fine-tuned-BERT-Imdb"

model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)

Inference Example

from transformers import pipeline

sentiment_pipeline = pipeline("text-classification", model=model_name)
result = sentiment_pipeline("The movie was absolutely fantastic! I loved it.")
print(result)

Citation

If you use this model, please cite: @article{devlin2019bert, title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, journal={arXiv preprint arXiv:1810.04805}, year={2019} }

License

This model is released under the Apache 2.0 License.