
# 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)
```

{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]}
transformers onnx bert fill-mask exbert en dataset:bookcorpus dataset:wikipedia arxiv:1810.04805 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #onnx #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# BERT base model (uncased)

## Model description

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 uncased: it does not make a difference
between english and English.

## 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:



# BERT base model (uncased) ## Model description

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 uncased: it does not make a difference
between english and English. ## 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
#transformers #onnx #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # BERT base model (uncased) ## Model description

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 uncased: it does not make a difference
between english and English. ## 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
#transformers #onnx #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# BERT base model (uncased)## Model description

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 uncased: it does not make a difference
between english and English.## 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: