Setting the model in evaluation mode

You're ready to set your language model in evaluation mode. If the model is not in evaluation mode during inference, layers like batch normalization and dropout will introduce changes in the behavior of the model, leading to inconsistent translation quality. Build the loop to evaluate the model!

Some data have been preloaded: model, eval_dataloader, accelerator, and metric.

本练习是课程的一部分

Efficient AI Model Training with PyTorch

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练习说明

Set the model in evaluation mode before looping through batches in the dataset.
Aggregate predictions and labels across devices to compute evaluation metrics with Accelerator's .gather_for_metrics() method.
Compute the evaluation metric at the end.

交互式实操练习

通过完成这段示例代码来试试这个练习。

metric = evaluate.load("glue", "mrpc")

# Set the model in evaluation mode
____.____()
for step, batch in enumerate(eval_dataloader):
    with torch.no_grad():
        outputs = model(**batch)
    predictions = outputs.logits.argmax(dim=-1)
    # Aggregate values across devices
    predictions, references = ____.____((predictions, batch["labels"]))
    metric.add_batch(predictions=predictions, references=references)
# Compute the evaluation metric
eval_metric = metric.____()
print(eval_metric)

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