Instructions to use rame/en_pipeline_ner_model_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rame/en_pipeline_ner_model_4 with spaCy:
!pip install https://huggingface.co/rame/en_pipeline_ner_model_4/resolve/main/en_pipeline_ner_model_4-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline_ner_model_4") # Importing as module. import en_pipeline_ner_model_4 nlp = en_pipeline_ner_model_4.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54c11b417400e4d4875b7b9c194db9b7cad3b3e6d3ac4514c394dac893d93629
- Size of remote file:
- 266 MB
- SHA256:
- 40928e33024f62d4378cdd7931eeed297c1140d4c12c1b3b977b58644640f5d7
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