readme: ---
  language: en
  inference: false
  tags:
  - onnx
  - exbert
  license: apache-2.0
  datasets:
  - bookcorpus
  - wikipedia
  ---
  
  # ONNX export of bert-base-uncased
  
  Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
  [this paper](https://arxiv.org/abs/1810.04805) and first released in
  [this repository](https://github.com/google-research/bert). This model is uncased: it does not make 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.
  
  ## Model description
  
  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 labeling 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:
  
  - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
    the entire masked sentence through the model and has to predict the masked words. This is different from traditional
    recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
    GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
    sentence.
  - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
    they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
    predict if the two sentences were following each other or not.
  
  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.
  
  ## Model variations
  
  BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.  
  Chinese and multilingual uncased and cased versions followed shortly after.  
  Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.  
  Other 24 smaller models are released afterward.  
  
  The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
  
  | Model | #params | Language |
  |------------------------|--------------------------------|-------|
  | [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M   | English |
  | [`bert-large-uncased`](https://huggingface.co/bert-large-uncased)              | 340M    | English | sub 
  | [`bert-base-cased`](https://huggingface.co/bert-base-cased)        | 110M    | English |
  | [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M    |  English |
  | [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M    | Chinese |
  | [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
  | [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
  | [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
  
  ## Intended uses & limitations
  
  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](https://huggingface.co/models?filter=bert) to look for
  fine-tuned versions of 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.
  
  ### How to use
  
  You can use this model directly with a pipeline for masked language modeling:
  
  ```python
  >>> from transformers import pipeline
  >>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
  >>> unmasker("Hello I'm a [MASK] model.")
  
  [{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
    'score': 0.1073106899857521,
    'token': 4827,
    'token_str': 'fashion'},
   {'sequence': "[CLS] hello i'm a role model. [SEP]",
    'score': 0.08774490654468536,
    'token': 2535,
    'token_str': 'role'},
   {'sequence': "[CLS] hello i'm a new model. [SEP]",
    'score': 0.05338378623127937,
    'token': 2047,
    'token_str': 'new'},
   {'sequence': "[CLS] hello i'm a super model. [SEP]",
    'score': 0.04667217284440994,
    'token': 3565,
    'token_str': 'super'},
   {'sequence': "[CLS] hello i'm a fin