Quantifying the Carbon Emissions of Machine Learning
Paper
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1910.09700
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Published
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24
ModernBERT Fine-tuned for IMDB Sentiment Analysis
This model is a fine-tuned version of answerdotai/ModernBERT-base for sentiment analysis on the IMDB movie review dataset.
Dataset
The model was fine-tuned on a subset of the IMDB dataset.
- Training Split Size: 5000 samples
- Validation Split Size: 12500 samples
- Test Split Size: 1000 samples
Evaluation Results
The model was evaluated on the validation set after training. The key metrics are:
- Validation Loss: 0.1587
- Validation Accuracy: 0.9478
- Validation F1-score (macro): 0.9488
The best model checkpoint was saved at step 300.
Usage
You can use this model for text classification (sentiment analysis) using the Hugging Face pipeline:
from transformers import pipeline
model_id = "Hugging-GK/ModerBert-MultiClass-IMDB-imdb_complete_data"
sentiment_pipeline = pipeline("text-classification", model=model_id)
text = "This movie was fantastic!"
result = sentiment_pipeline(text)
print(result)
text = "This movie was terrible."
result = sentiment_pipeline(text)
print(result)
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Model Details
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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