Instructions to use RaThorat/en_grantss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use RaThorat/en_grantss with spaCy:
!pip install https://huggingface.co/RaThorat/en_grantss/resolve/main/en_grantss-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_grantss") # Importing as module. import en_grantss nlp = en_grantss.load() - Notebooks
- Google Colab
- Kaggle
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Three variants of the model is built with Spacy3 for grant applications.
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A simple named entity recognition custom model from scratch with annotation tool prodi.gy.
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Github info: https://github.com/RaThorat/ner_model_prodigy
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The most general model is 'en_grantss'.
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| Feature | Description |
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Three variants of the model is built with Spacy3 for grant applications.
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A simple named entity recognition custom model from scratch with annotation tool prodi.gy.
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Github info: https://github.com/RaThorat/ner_model_prodigy
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The most general model is 'en_grantss'. The model en_ncv is more suitable to extract entities from narrative CV's.
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The model en_grant is the first model in the series.
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| Feature | Description |
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