kyrgyz_language_NER

murat/kyrgyz_language_NER

kyrgyz_language_NER is a token classification model from murat. Use it when you need labels on individual words, such as names. It is set up for transformers.

<h1Kyrgyz Named Entity Recognition</h1 Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Kyrgyz language.

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: Oct 5, 2022
Updated: Oct 5, 2022
SHA: 1f3ff1b5

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Datasets

wikiann

Model card

From the Hugging Face model README

<h1>Kyrgyz Named Entity Recognition</h1> Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Kyrgyz language.

WARNING: this model is not usable (see metrics below) and is built just as a proof of concept. I'll update the model after cleaning up the Wikiann dataset (ky part of it which contains only 100 train/test/valid items) or coming up with a completely new dataset.

Label ID and its corresponding label name

Label ID	Label Name
0	O
1	B-PER
2	I-PER
3	B-ORG
4	I-ORG
5	B-LOC
6	I-LOC

<h1>Results</h1>

Name	Overall F1	LOC F1	ORG F1	PER F1
Train set	0.595683	0.570312	0.687179	0.549180
Validation set	0.461333	0.551181	0.401913	0.425087
Test set	0.442622	0.456852	0.469565	0.413114

Example

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("murat/kyrgyz_language_NER")
model = AutoModelForTokenClassification.from_pretrained("murat/kyrgyz_language_NER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Жусуп Мамай"
ner_results = nlp(example)
ner_results

Task: Token Classification
Library: transformers
Type: bert
Languages: ky
Created: Oct 5, 2022
Updated: Oct 5, 2022