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
| 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 |