Instructions to use appfire/en_NER_Features with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use appfire/en_NER_Features with spaCy:
!pip install https://huggingface.co/appfire/en_NER_Features/resolve/main/en_NER_Features-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_NER_Features") # Importing as module. import en_NER_Features nlp = en_NER_Features.load() - Notebooks
- Google Colab
- Kaggle
Ramsha Ali commited on
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English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
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English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.Named Entity Recognition model trained on Google Play Store app descriptions to automatically identify app features from app description.
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