readme: ---
  language: en
  license: apache-2.0
  datasets:
  - bookcorpus
  - wikipedia
  - cc_news
  ---
  
  # BigBird base model
  
  BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.
  
  It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird).
  
  ## Model description
  
  BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts.
  
  ## Original implementation
  
  Follow [this link](https://huggingface.co/google/bigbird-roberta-base) to see the original implementation.
  
  ## How to use
  
  Download the model by cloning the repository via `git clone https://huggingface.co/OWG/bigbird-roberta-base`.
  
  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("google/bigbird-roberta-base")
  
  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)
  ```