Instructions to use DanielHellebust/cycLingoNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use DanielHellebust/cycLingoNER with spaCy:
!pip install https://huggingface.co/DanielHellebust/cycLingoNER/resolve/main/cycLingoNER-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("cycLingoNER") # Importing as module. import cycLingoNER nlp = cycLingoNER.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from DanielHellebust/cycLingoNER: direct link, hf CLI and curl.
- Browser
- Download file 344 Bytes
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https://huggingface.co/DanielHellebust/cycLingoNER/resolve/main/README.md
- Command line
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hf download hf://DanielHellebust/cycLingoNER/README.md
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curl -L -o README.md https://huggingface.co/DanielHellebust/cycLingoNER/resolve/main/README.md
344 Bytes
| license: mit | |
| language: | |
| - en | |
| library_name: spacy | |
| tags: | |
| - customNER | |
| - bicycle | |
| - ner | |
| pipeline_tag: token-classification | |
| # Model Card cycLingoNER model | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| A custom NER model trained on custom dataset to detect bicycle specific terminology as entity. Trained on a blank spaCy model. |