BERT (Bidirectional Encoder Representations from Transformers) is a foundational model for natural language processing. Here are popular variants and their applications:

  • BERT-base
    A standard version with 110 million parameters. Ideal for general tasks like text classification and NER.

    bert_model
    [Download BERT-base](/en/resources/models/bert-models/download)

  • BERT-large
    A larger version with 340 million parameters. Better performance for complex tasks but requires more resources.

    bert_large

  • RoBERTa
    An optimized version of BERT trained with dynamic masking. Outperforms BERT in many benchmarks.

    roberta_model
    [Read more about RoBERTa](/en/resources/models/roberta)

  • ALBERT
    A lightweight alternative using parameter sharing. Reduces model size without sacrificing performance.

    albert_architecture

For detailed documentation on BERT implementations, visit BERT Technical Guide. 📚