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onionLad
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ner-tutorial-2024

Token Classification
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deberta-v2
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="onionLad/ner-tutorial-2024")
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("onionLad/ner-tutorial-2024")
model = AutoModelForTokenClassification.from_pretrained("onionLad/ner-tutorial-2024", device_map="auto")
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ner-tutorial-2024

This model is a fine-tuned version of microsoft/deberta-v3-base on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.016900
  • Precision: 0.955075
  • Recall: 0.966005
  • F1: 0.960509
  • Accuracy: 0.992351
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Token Classification
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Dataset used to train onionLad/ner-tutorial-2024