Instructions to use rame/en_pipeline_ner_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rame/en_pipeline_ner_model_1 with spaCy:
!pip install https://huggingface.co/rame/en_pipeline_ner_model_1/resolve/main/en_pipeline_ner_model_1-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline_ner_model_1") # Importing as module. import en_pipeline_ner_model_1 nlp = en_pipeline_ner_model_1.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72752d04359edf39f6ba4af0490f76980619e2f534f875129df1f14b945ba0b6
- Size of remote file:
- 502 MB
- SHA256:
- 5f7fa00f3f90bda21bdb4f51e5c0627bb07f10226aa0ddaa960b740eec90fd91
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