Yes, BERT (Bidirectional Encoder Representations from Transformers) requires extensive computational resources during both pre-training and fine-tuning stages.

  During pre-training, BERT uses unsupervised learning on massive amounts of unlabeled text. The model is trained on large-scale parallel hardware, such as GPUs or TPUs, to leverage their computing power and parallel processing capabilities. The pre-training process involves training a transformer-based deep neural network model with hundreds of millions or even billions of parameters. This requires significant computational resources to process and train the model efficiently.

  Once the pre-training is completed, the fine-tuning process is performed on specific downstream tasks. Fine-tuning BERT typically involves training a relatively smaller network on task-specific labeled data. Although the fine-tuning process is less resource-intensive compared to pre-training, it still requires considerable computational resources due to the size and complexity of the BERT model.

  The computational requirements for using BERT can vary depending on the specific task, model configuration, and available hardware. Training a BERT model from scratch can be computationally intensive and time-consuming, especially when dealing with large-scale datasets. However, the use of pre-trained BERT models, such as those provided by the Hugging Face Transformers library, can significantly reduce the computational requirements for downstream tasks.

  In summary, BERT does require extensive computational resources, especially during the pre-training stage, but the availability of pre-trained models and optimization techniques can help mitigate the resource demands for fine-tuning and inference tasks.