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| from sklearn.utils.class_weight import compute_class_weight | |
| import numpy as np | |
| import evaluate | |
| metric = evaluate.load('accuracy') | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| predictions = np.argmax(logits, axis=-1) | |
| return metric.compute(predictions=predictions, references=labels) | |
| def get_class_weights(df): | |
| class_weights = compute_class_weight("balanced", | |
| classes = sorted(df['label'].unique().tolist()), | |
| y = df['label'].tolist() | |
| ) | |
| return class_weights |