Token Classification
spaCy
English
named-entity-recognition
ner
nlp
nigeria
road-traffic-crash
road-safety
information-extraction
data-anonymization
privacy
Instructions to use pidakwo/rtc-ner-extended with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use pidakwo/rtc-ner-extended with spaCy:
!pip install https://huggingface.co/pidakwo/rtc-ner-extended/resolve/main/rtc-ner-extended-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("rtc-ner-extended") # Importing as module. import rtc-ner-extended nlp = rtc-ner-extended.load() - Notebooks
- Google Colab
- Kaggle
Download tok2vec/model from pidakwo/rtc-ner-extended: direct link, hf CLI and curl.
- Browser
- Download file 6.01 MB
-
https://huggingface.co/pidakwo/rtc-ner-extended/resolve/main/tok2vec/model
- Command line
-
hf download hf://pidakwo/rtc-ner-extended/tok2vec/model
-
curl -L -o model https://huggingface.co/pidakwo/rtc-ner-extended/resolve/main/tok2vec/model
6.01 MB
- Xet hash:
- a220d160f8bbc2b24a77e3d8f568b24ea8a23942013e77c3ee1cd38d1e16c4b0
- Size of remote file:
- 6.01 MB
- SHA256:
- d59f0da9841b526d066240d89d7e920a99ea795391228930f23d47e118ce4cfc
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