readme: ---
  language: en
  tags:
  - exbert
  license: apache-2.0
  datasets:
  - bookcorpus
  - wikipedia
  ---
  
  # BERT base model (uncased)
  
  ## Model description
  
  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.
  
  ## Original implementation
  
  Follow [this link](https://huggingface.co/bert-base-uncased) to see the original implementation.
  
  ## How to use
  
  Download the model by cloning the repository via `git clone https://huggingface.co/OWG/bert-base-uncased`.
  
  Then you can use the model with the following code:
  
  ```python
  from onnxruntime import InferenceSession, SessionOptions, GraphOptimizationLevel
  from transformers import BertTokenizer
  
  
  tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
  
  options = SessionOptions()
  options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
  
  session = InferenceSession("path/to/model.onnx", sess_options=options)
  session.disable_fallback()
  
  text = "Replace me by any text you want to encode."
  input_ids = tokenizer(text, return_tensors="pt", return_attention_mask=True)
  
  inputs = {k: v.cpu().detach().numpy() for k, v in input_ids.items()}
  outputs_name = session.get_outputs()[0].name
  
  outputs = session.run(output_names=[outputs_name], input_feed=inputs)
  ```
  