README.md

---
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
```