readme: 
  ---
  language: ky
  datasets:
  - wikiann
  examples:
  widget:
  - text: "Бириккен Улуттар Уюму"
    example_title: "Sentence_1"
  - text: "Жусуп Мамай"
    example_title: "Sentence_2"
  ---
  
  <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
  ```py
  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
  ```
  