eriktks/conll2003
Updated • 23.3k • 175
How to use protectai/bert-large-cased-finetuned-conll03-english-onnx with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="protectai/bert-large-cased-finetuned-conll03-english-onnx") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("protectai/bert-large-cased-finetuned-conll03-english-onnx")
model = AutoModelForTokenClassification.from_pretrained("protectai/bert-large-cased-finetuned-conll03-english-onnx", device_map="auto")THIS PROJECT HAS BEEN ARCHIVED.
This project and its associated code on GitHub are no longer under active development or maintained.
This model is a conversion of dbmdz/bert-large-cased-finetuned-conll03-english to ONNX format using the 🤗 Optimum library.
dbmdz/bert-large-cased-finetuned-conll03-english is designed for named-entity recognition (NER), capable of finding person, organization, and other entities in the text.
Loading the model requires the 🤗 Optimum library installed.
from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("laiyer/bert-large-cased-finetuned-conll03-english-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-large-cased-finetuned-conll03-english-onnx")
ner = pipeline(
task="ner",
model=model,
tokenizer=tokenizer,
)
ner_output = ner("My name is John Doe.")
print(ner_output)
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