wandb/run-20240924_172029-478j3exr/files/output.log

[]
[]
[{"variableName": "ds_dados", "type": "dictionary", "supportedEngines": ["pandas"], "isLocalVariable": false}]
[{"variableName": "ds_dados", "type": "dictionary", "supportedEngines": ["pandas"], "isLocalVariable": false}]
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.