I found that model predict NER at the string end less accurate than NER is surrounded by other words.
take all unit measure including values from product description string:
text: метилэтилпиридинол 10 мг/мл раствор инфузи 1мл ампул №10
ner correct: (10 мг/мл, 1мл, №10)
problem: ‘№10’ is not detected well in another similar products, sometime: ‘’, ‘№’, ‘10’
also did ner-gold
- is any special symbols to tell model to read it correctly BOS, EOS etc?
- or I have to add something like ’ thisisspecialwordtoindicateendofstring’?
- or just train more items in database?