Token Classification
spaCy
English
named-entity-recognition
ner
nlp
road-traffic-crash
road-safety
information-extraction
geospatial
nigeria
Instructions to use pidakwo/rtc-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use pidakwo/rtc-ner with spaCy:
!pip install https://huggingface.co/pidakwo/rtc-ner/resolve/main/rtc-ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("rtc-ner") # Importing as module. import rtc-ner nlp = rtc-ner.load() - Notebooks
- Google Colab
- Kaggle
Download ner/model from pidakwo/rtc-ner: direct link, hf CLI and curl.
- Browser
- Download file 136 kB
-
https://huggingface.co/pidakwo/rtc-ner/resolve/main/ner/model
- Command line
-
hf download hf://pidakwo/rtc-ner/ner/model
-
curl -L -o model https://huggingface.co/pidakwo/rtc-ner/resolve/main/ner/model
136 kB
- Xet hash:
- eb4c3b7bc203b5df32f3fc1c311c328961251e7cc9128b6b0959e28f052df5fd
- Size of remote file:
- 136 kB
- SHA256:
- 8897448b9cd5baa05ae10289731c759e55f51ff96d3de3063ed6906351207151
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