Understanding Trainer Logs and Saved Model Checkpoints in Hugging Face

Learn how to configure logging in Hugging Face’s Trainer and understand which model checkpoint is utilized after training.
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Understanding Trainer Logs and Saved Model Checkpoints in Hugging Face

When using the Trainer class from Hugging Face's Transformers library, you might find yourself puzzled by the absence of logs during certain epochs of your model training. Specifically, if you see “No log” for the first six epochs, you might wonder why this is happening and which model checkpoint is being used at the end of the training. In this guide, we will break down these issues thoroughly to clarify your concerns.

Why Are There No Logs for Epochs 0-5?

The first question we need to address is regarding the absence of logs for the early epochs of training. This often comes down to the default settings in the TrainingArguments class.

Default Logging Behavior

The logging_steps parameter is set to a default value of 500. This means that the trainer will only log the loss once every 500 training steps.

Given that your output indicates that the first epoch consists of 100 steps, the first log will only appear after the 500th step. Therefore, it is expected that you see "No log" for epochs 0-5.

Explanation of Epochs to Steps

Epoch 1 = 0-100 steps: No log generated.

Epoch 2 = 100-200 steps: No log generated.

Epoch 3 = 200-300 steps: No log generated.

Epoch 4 = 300-400 steps: No log generated.

Epoch 5 = 400-500 steps: No log generated.

Epoch 6 = 500-600 steps: Logs appear, and you start seeing outputs.

If you want to see logs for all epochs, you have to adjust the logging_steps parameter in your training configuration.

Which Model Checkpoint Will Be Used for Predictions?

After addressing the logging issue, the second point of confusion often arises regarding the model checkpoint that will be utilized when you perform predictions. In your case, it's noted that the fifth epoch achieved the best accuracy.

Understanding Model Checkpoints

You need to consider the following settings in TrainingArguments to clarify which model will be saved and used:

Saving Strategy: You can save checkpoints based on certain metrics.

Loading Best Model at End: The trainer can be configured to load the best model based on the specified metric.

Configuring TrainingArguments

Here is an example of how to configure these parameters effectively:

[[See Video to Reveal this Text or Code Snippet]]

In this configuration:

eval_steps specifies that every 100 steps, the model will be evaluated on the validation set with accuracy as the optimizing metric.

save_total_limit ensures that only the two best models will be kept to save storage space.

By using load_best_model_at_end, the model with the highest accuracy will be preserved and loaded for predictions, meaning it will be the model trained after epoch 5 (assuming it is the one with best metrics up to that point).

Conclusion

By understanding the settings of your Trainer and how epochs translate into steps, you can tailor your training process to suit your needs. Configuring your Logging and TrainingArguments allows you to effectively monitor your training process and ensure the best model is used for predictions. Always remember, fine-tuning these parameters can make a significant difference in your training experience and the outcome of your machine learning models.

If you have any further questions or could use a helping hand with Hugging Face, feel free to leave a comment below!