Instructions to use onionLad/ner-tutorial-2024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onionLad/ner-tutorial-2024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="onionLad/ner-tutorial-2024")# 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") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- eriktks/conll2003
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language:
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- en
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pipeline_tag: token-classification
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---
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# ner-tutorial-2024
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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:
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- Loss: 0.016900
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- Precision: 0.955075
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- Recall: 0.966005
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- F1: 0.960509
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- Accuracy: 0.992351
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