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
-
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
metadata
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