wandb/run-20240924_172630-x9iddikd/files/output.log

[]
[]
dados_tokenizados:
 DatasetDict({
    train: Dataset({
        features: ['rotulo', 'rotulo_simples', 'text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 4000
    })
    validation: Dataset({
        features: ['rotulo', 'rotulo_simples', 'text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 1000
    })
    test: Dataset({
        features: ['rotulo', 'rotulo_simples', 'text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 1000
    })
})
/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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{'loss': 0.2031, 'grad_norm': 1.1480563879013062, 'learning_rate': 1.2e-05, 'epoch': 2.0}
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{'train_runtime': 8026.8642, 'train_samples_per_second': 2.492, 'train_steps_per_second': 0.156, 'train_loss': 0.11480112991333008, 'epoch': 5.0}
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
{'eval_loss': 0.17208045721054077, 'eval_accuracy': 0.939, 'eval_runtime': 40.1609, 'eval_samples_per_second': 24.9, 'eval_steps_per_second': 0.797, 'epoch': 1.0}
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wandb: Network error (SSLError), entering retry loop.
{'loss': 0.0549, 'grad_norm': 0.032662052661180496, 'learning_rate': 5.2e-06, 'epoch': 24.0}
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