google-bert/bert-base-cased

Fill-Mask • Updated Feb 19 • 4.49M • 274

Note Base BERT model, smaller variant. Trained on the "cased" dataset, meaning that it wasn't lowercase and all accents were kept. 12-layer, 768-hidden, 12-heads , 110M parameters

google-bert/bert-base-uncased

Fill-Mask • Updated Feb 19 • 58M • • 2.02k

Note Base BERT model, smaller variant. Trained on the "uncased" dataset, meaning that it was lowercase and all accents were removed. 12-layer, 768-hidden, 12-heads , 110M parameters

google-bert/bert-large-cased

Fill-Mask • Updated Feb 19 • 92.2k • 31

Note Large BERT model, larger variant. Trained on the "cased" dataset, meaning that it wasn't lowercase and all accents were kept. 24-layer, 1024-hidden, 16-heads, 340M parameters

google-bert/bert-large-uncased

Fill-Mask • Updated Feb 19 • 2.66M • 124

Note Large BERT model, larger variant. Trained on the "uncased" dataset, meaning that it was lowercase and all accents were removed. 24-layer, 1024-hidden, 16-heads, 340M parameters

google-bert/bert-base-multilingual-cased

Fill-Mask • Updated Feb 19 • 6.11M • 465

Note Base BERT model, smaller variant. The list of supported languages is available here: https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters

google-bert/bert-base-chinese

Fill-Mask • Updated Feb 19 • 1.65M • 1.05k

Note Base BERT model, smaller variant. Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters

google-bert/bert-large-cased-whole-word-masking

Fill-Mask • Updated Apr 10 • 4.65k • 15

Note Large BERT model, larger variant. Trained on the "cased" dataset, meaning that it wasn't lowercase and all accents were kept. Whole word masking indicates a different preprocessing where entire words are masked rather than subwords. The BERT team reports better metrics with the wwm models. 24-layer, 1024-hidden, 16-heads, 340M parameters

google-bert/bert-large-uncased-whole-word-masking

Fill-Mask • Updated Feb 19 • 20.5k • 19

Note Large BERT model, larger variant. Trained on the "uncased" dataset, meaning that it was lowercase and all accents were removed. Whole word masking indicates a different preprocessing where entire words are masked rather than subwords. The BERT team reports better metrics with the wwm models. 24-layer, 1024-hidden, 16-heads, 340M parameters