Define evaluation metrics

You're developing a real-time language translation service in a video conferencing application. To monitor training, you'll define evaluation metrics for accuracy and F1 score, which measure overall model performance.

The evaluate and numpy (np) libraries have been pre-imported.

本练习是课程的一部分

Efficient AI Model Training with PyTorch

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练习说明

Load the f1 score using the evaluate library; accuracy has been loaded for you.
Extract logits and labels by unpacking eval_predictions into two variables.
Convert logits to predictions.
Compute the f1 score based on the predictions and labels.

交互式实操练习

通过完成这段示例代码来试试这个练习。

def compute_metrics(eval_predictions):
    load_accuracy = evaluate.load("accuracy")
  	# Load the F1 score
    load_f1 = evaluate.load("____")
    # Extract logits and labels from eval_predictions
    logits, ____ = eval_predictions
    # Convert logits to predictions
    predictions = np.____(logits, axis=-1)
    accuracy = load_accuracy.compute(predictions=predictions, references=labels)["accuracy"]
    # Compute the F1 score
    f1 = ____.____(predictions=predictions, references=labels)["f1"]
    return {"accuracy": accuracy, "f1": f1}

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