GitHub - google-research/bert: TensorFlow code and pre-trained models for BERT

🤖🚀 Embrace the power of BERT - the #AI tool that revolutionizes language processing! With its bidirectional, contextual training and pre-trained models, BERT delivers state-of-the-art results in NLP tasks. Fine-tune it for tailored solutions and unleash its full potential! #GitHub #tensorflow

BERT is a method for pre-training language representations with state-of-the-art results across NLP tasks.
BERT uses unsupervised training on plain text data to generate contextual representations.
BERT is bidirectional and contextual compared to previous models that were unidirectional or shallowly bidirectional.
BERT tasks include masked LM (Masked Language Model) and next sentence prediction.
BERT's pre-training data can be generated from plain text files using specific scripts.
BERT offers pre-trained models such as BERT-Base (12-layer) and BERT-Large (24-layer) with specific configurations.
BERT models are released with associated vocab files, config files, and checkpoints under Apache 2.0 license.
Fine-tuning BERT models is adaptable to various NLP tasks with minor modifications for state-of-the-art results.
BERT pre-training requires specific data preprocessing steps and considerations for effective training.
BERT can be fine-tuned on GPUs or Cloud TPUs with hyperparameters like batch size, learning rate, and sequence length.