BERT base model (uncased)¶
Transformers model pretrained on a large corpus of English data in a self-supervised fashion
|Hugging Face team||Unknown||Unknown||Unknown|
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. It was introduced in a paper and first released in a repository. It is uncased, meaning it does not differentiate between English and english. It can be used for masked language modeling or next sentence prediction, but is primarily intended to be fine-tuned on downstream tasks such as sequence classification, token classification or question answering. It was trained on 4 cloud TPUs for one million steps with a batch size of 256 and a sequence length of 128 tokens for 90% of the steps and 512 for the remaining 10%. When fine-tuned on downstream tasks, it achieves good results on Glue test results.
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Use Cases: Sequence classification, token classification or question answering
Training corpus: BookCorpus, English Wikipedia
Training method: Adam optimizer, learning rate of 1e-4, β1=0.9, β2=0.999, weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after
Evaluation method: Glue test results
Compute: 4 cloud TPUs in Pod configuration (16 TPU chips total)
Features: Lowercased and tokenized using WordPiece and a vocabulary size of 30,000
Limitations: Primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions
Strengths: Can use lots of publicly available data