A transformer-based language model pre-trained on a large corpus of English data using a masked language modeling objective. It was introduced in a research paper by Google researchers and achieved state-of-the-art results on various natural language processing tasks. The model is cased, meaning it differentiates between English and english, and has a configuration of 24 layers, 1024 hidden dimensions, 16 attention heads, and 336M parameters.
A transformer-based language model pre-trained on a large corpus of English data using a masked language modeling objective. It was introduced in a research paper by Google researchers and achieved state-of-the-art results on various natural language processing tasks. The model is cased, meaning it differentiates between English and english, and has a configuration of 24 layers, 1024 hidden dimensions, 16 attention heads, and 336M parameters.
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where is my father? (0.09)
where is my mother? (0.08)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
This model has the following configuration:
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.
The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).
When fine-tuned on downstream tasks, this model achieves the following results:
Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy |
---|---|---|
BERT-Large, Cased (Original) | 91.5/84.8 | 86.09 |
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}