Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
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BERT-Based Mesasages Spam Detection ModelModel Description This model is a BERT-based sequence classification model trained to classify email messages as either "ham" (non-spam) or "spam." The model leverages the pre-trained bert-base-uncased model from the Hugging Face Transformers library and fine-tunes it on a labeled dataset of email messages. This model is designed to help identify unwanted or malicious email messages, which is a common task in text classification.
Training Data The model was trained on a dataset of email messages, where each message is labeled as either "ham" or "spam." The data was split into a training set (80%) and a validation set (20%). The email messages were tokenized using the BERT tokenizer with a maximum sequence length of 128 tokens.
Training Procedure Optimizer: AdamW with a learning rate of 2e-5 and epsilon of 1e-8. Learning Rate Scheduler: Linear scheduler with warmup. Batch Size: 32 Epochs: 6 Loss Function: CrossEntropyLoss (handled internally by the BertForSequenceClassification model). Device: The model was trained on a CPU.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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