ONNX-converted models
Collection
Models converted to ONNX for faster CPU inference on LLM Guard. • 20 items • Updated • 2
ONNX version of dslim/bert-base-NER
This model is a conversion of dslim/bert-base-NER to ONNX format using the 🤗 Optimum library.
bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
Usage
Loading the model requires the 🤗 Optimum library installed.
from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("laiyer/bert-base-NER-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-base-NER-onnx")
ner = pipeline(
task="ner",
model=model,
tokenizer=tokenizer,
)
ner_output = ner("My name is John Doe.")
print(ner_output)
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Model tree for protectai/bert-base-NER-onnx
Base model
dslim/bert-base-NER