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
File size: 1,746 Bytes
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"lang":"en",
"name":"pipeline",
"version":"0.0.0",
"spacy_version":">=3.6.1,<3.7.0",
"description":"",
"author":"",
"email":"",
"url":"",
"license":"",
"spacy_git_version":"458bc5f45",
"vectors":{
"width":0,
"vectors":0,
"keys":0,
"name":null,
"mode":"default"
},
"labels":{
"tok2vec":[
],
"ner":[
"CASUALTY",
"HOSPITAL",
"INJURED",
"LANDMARK",
"LGA",
"ROAD",
"STATE",
"SUBURB",
"TOWN"
]
},
"pipeline":[
"tok2vec",
"ner"
],
"components":[
"tok2vec",
"ner"
],
"disabled":[
],
"performance":{
"ents_f":0.9392883802,
"ents_p":0.9381212724,
"ents_r":0.9404583956,
"ents_per_type":{
"CASUALTY":{
"p":0.9034482759,
"r":0.9018932874,
"f":0.902670112
},
"TOWN":{
"p":0.913107511,
"r":0.9295352324,
"f":0.9212481426
},
"LGA":{
"p":0.9545454545,
"r":0.9692307692,
"f":0.9618320611
},
"STATE":{
"p":0.947008547,
"r":0.9502572899,
"f":0.948630137
},
"ROAD":{
"p":0.9763313609,
"r":0.9786476868,
"f":0.9774881517
},
"INJURED":{
"p":0.9057377049,
"r":0.9170124481,
"f":0.9113402062
},
"LANDMARK":{
"p":0.9272727273,
"r":0.8571428571,
"f":0.8908296943
},
"HOSPITAL":{
"p":0.9525483304,
"r":0.9592920354,
"f":0.9559082892
},
"SUBURB":{
"p":0.8684210526,
"r":0.9166666667,
"f":0.8918918919
}
},
"tok2vec_loss":4200.0542435646,
"ner_loss":6021.9171480695
}
} |