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Towards Transfer Learning Techniques-BERT, DistilBERT, BERTimbau, and DistilBERTimbau for Automatic Text

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DistilBERT, a smaller language model, offers significant advantages in text classification. It trains faster and is more compact than BERT, while retaining high language comprehension for English and Brazilian Portuguese datasets.

Keywords:
BERTBERTimbauDistilBERTDistilBERTimbaubig datapre-trained modeltransformer-based machine learning

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Area of Science:

  • Natural Language Processing
  • Machine Learning

Background:

  • The Internet of Things and Web 2.0 generate vast textual data, necessitating efficient text classification.
  • Traditional methods struggle with the scale and complexity of this data.

Purpose of the Study:

  • To evaluate the effectiveness of BERT and DistilBERT for text classification in English and Brazilian Portuguese.
  • To compare the performance, training time, and model size of DistilBERT against BERT.

Main Methods:

  • Utilized Bidirectional Encoder Representations from Transformers (BERT) and its smaller counterpart, DistilBERT.
  • Conducted a case study on text classification tasks using distinct datasets in English and Brazilian Portuguese.

Main Results:

  • DistilBERT demonstrated a 45% reduction in training time compared to BERT for both languages.
  • DistilBERT models were 40% smaller in size.
  • DistilBERT preserved approximately 96% of language comprehension capabilities on balanced datasets.

Conclusions:

  • DistilBERT presents a compelling alternative for text classification, offering substantial efficiency gains.
  • The model's reduced size and faster training make it suitable for resource-constrained environments.
  • DistilBERT effectively balances performance and efficiency for multilingual text classification tasks.