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optim = AdamW(model.parameters(), lr=5e-5, eps=1e-8) #tasa de aprendizaje
# Se inicializa el cargador de datos para los datos de entrenamiento
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
for epoch in range(10):
{'score': 0.7284889221191406, 'start': 14, 'end': 29, 'answer': 'serology tests,'}
Precisión del modelo ajustado: 0.8211654387139986
Epoch 0: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=1.87]
Epoch 1: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.211]
Epoch 2: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=1.95]
Epoch 3: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.0322]
Epoch 4: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.0229]
Epoch 5: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.0271]
Epoch 6: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.59]
Epoch 7: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.0233]
Epoch 8: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.00257]
Epoch 9: 100%|██████████| 94/94 [00:57<00:00, 1.62it/s, loss=0.00663]
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