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 tok2vec/model from pidakwo/rtc-ner: direct link, hf CLI and curl.
- Browser
- Download file 6.01 MB
-
https://huggingface.co/pidakwo/rtc-ner/resolve/main/tok2vec/model
- Command line
-
hf download hf://pidakwo/rtc-ner/tok2vec/model
-
curl -L -o model https://huggingface.co/pidakwo/rtc-ner/resolve/main/tok2vec/model
6.01 MB
- Xet hash:
- 761559ae797ff4734bc6a81cc8bafbafb47a802b5eb82f7c038aa74bd3571f23
- Size of remote file:
- 6.01 MB
- SHA256:
- f3c22f3cc7dbef93a5a4402e8df8b9861b8000521848abce6180af123f8fae8d
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