metadata
language:
- en
inference: false
pipeline_tag: token-classification
tags:
- ner
- bert
license: mit
datasets:
- conll2003
base_model: dslim/bert-large-NER
model-index:
- name: dslim/bert-large-NER
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9031688753722759
verified: true
- name: Precision
type: precision
value: 0.920025068328604
verified: true
- name: Recall
type: recall
value: 0.9193688678588825
verified: true
- name: F1
type: f1
value: 0.9196968510445761
verified: true
- name: loss
type: loss
value: 0.5085050463676453
verified: true
ONNX version of dslim/bert-large-NER
This model is a conversion of dslim/bert-large-NER to ONNX format using the 🤗 Optimum library.
bert-large-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-large-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-large-NER-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-large-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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