metadata
license: apache-2.0
datasets:
- stanfordnlp/imdb
language:
- en
metrics:
- accuracy
- recall
- precision
base_model:
- google-bert/bert-base-uncased
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.