Instructions to use rame/en_pipeline_ner_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rame/en_pipeline_ner_model_2 with spaCy:
!pip install https://huggingface.co/rame/en_pipeline_ner_model_2/resolve/main/en_pipeline_ner_model_2-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline_ner_model_2") # Importing as module. import en_pipeline_ner_model_2 nlp = en_pipeline_ner_model_2.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16a16585440fa4a1a906b774da3087f1e4e4b7c282395bb079cb0ddece7a31b6
- Size of remote file:
- 266 MB
- SHA256:
- d4e34350dea0a77cc38cc12d1d6593e9409599abc5c8d31e2c3b9ded7dc588a5
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