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")# 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") - Notebooks
- Google Colab
- Kaggle
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Download README.md from onionLad/ner-tutorial-2024: direct link, hf CLI and curl.
- Browser
- Download file 368 Bytes
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https://huggingface.co/onionLad/ner-tutorial-2024/resolve/main/README.md
- Command line
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hf download hf://onionLad/ner-tutorial-2024/README.md
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curl -L -o README.md https://huggingface.co/onionLad/ner-tutorial-2024/resolve/main/README.md
368 Bytes
metadata
license: apache-2.0
datasets:
- eriktks/conll2003
language:
- en
pipeline_tag: token-classification
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