We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language ...
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language ...
The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using EncoderDecoderModel as proposed in Leveraging Pre-trained ...
It's a bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence prediction on a large corpus comprising ...
In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it ...
The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, ...
The documentation is organized into five sections: GET STARTED provides a quick tour of the library and installation instructions to get up and running.
DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than google-bert/bert-base-uncased, ...
The BERT models trained on Japanese text. There are models with two different tokenization methods: ... To use MecabTokenizer, you should pip install transformers ...
The bare MegatronBert Model transformer outputting raw hidden-states without any specific head on top. This model inherits from PreTrainedModel. Check the ...