
# 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)
```
{"language": "en", "license": "apache-2.0", "datasets": ["bookcorpus", "wikipedia", "cc_news"]}
onnx en dataset:bookcorpus dataset:wikipedia dataset:cc_news arxiv:2007.14062 license:apache-2.0 region:us
TAGS
#onnx #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #region-us 


# 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 and first released in this repository.

## 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 to see the original implementation.

## How to use

Download the model by cloning the repository via 'git clone URL

Then you can use the model with the following code:


# 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 and first released in this repository. ## 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 to see the original implementation. ## How to use

Download the model by cloning the repository via 'git clone URL

Then you can use the model with the following code:
TAGS
#onnx #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #region-us 
 # 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 and first released in this repository. ## 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 to see the original implementation. ## How to use

Download the model by cloning the repository via 'git clone URL

Then you can use the model with the following code:
passage: TAGS
#onnx #en #dataset-bookcorpus #dataset-wikipedia #dataset-cc_news #arxiv-2007.14062 #license-apache-2.0 #region-us 
# 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 and first released in this repository.## 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 to see the original implementation.## How to use

Download the model by cloning the repository via 'git clone URL

Then you can use the model with the following code: