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 https://huggingface.co/OWG/bert-base-uncased
.
Then you can use the model with the following code:
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)
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