Instructions to use jcorrie/en_extract_defined_terms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use jcorrie/en_extract_defined_terms with spaCy:
!pip install https://huggingface.co/jcorrie/en_extract_defined_terms/resolve/main/en_extract_defined_terms-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_extract_defined_terms") # Importing as module. import en_extract_defined_terms nlp = en_extract_defined_terms.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from jcorrie/en_extract_defined_terms: direct link, hf CLI and curl.
- Browser
- Download file 401 Bytes
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https://huggingface.co/jcorrie/en_extract_defined_terms/resolve/main/README.md
- Command line
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hf download hf://jcorrie/en_extract_defined_terms/README.md
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curl -L -o README.md https://huggingface.co/jcorrie/en_extract_defined_terms/resolve/main/README.md
401 Bytes
| tags: | |
| - spacy | |
| - token-classification | |
| language: | |
| - en | |
| model-index: | |
| - name: en_extract_defined_terms | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.9911504425 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.9911504425 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.9911504425 | |