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--- |
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language: ky |
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datasets: |
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- wikiann |
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examples: |
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widget: |
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- text: "Бириккен Улуттар Уюму" |
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example_title: "Sentence_1" |
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- text: "Жусуп Мамай" |
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example_title: "Sentence_2" |
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--- |
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<h1>Kyrgyz Named Entity Recognition</h1> |
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Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Kyrgyz language. |
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WARNING: this model is not usable (see metrics below) and is built just as a proof of concept. |
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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. |
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## Label ID and its corresponding label name |
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| Label ID | Label Name| |
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| -------- | ----- | |
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| 0 | O | |
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| 1 | B-PER | |
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| 2 | I-PER | |
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| 3 | B-ORG| |
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| 4 | I-ORG | |
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| 5 | B-LOC | |
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| 6 | I-LOC | |
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<h1>Results</h1> |
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| Name | Overall F1 | LOC F1 | ORG F1 | PER F1 | |
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| ---- | -------- | ----- | ---- | ---- | |
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| Train set | 0.595683 | 0.570312 | 0.687179 | 0.549180 | |
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| Validation set | 0.461333 | 0.551181 | 0.401913 | 0.425087 | |
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| Test set | 0.442622 | 0.456852 | 0.469565 | 0.413114 | |
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Example |
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```py |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("murat/kyrgyz_language_NER") |
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model = AutoModelForTokenClassification.from_pretrained("murat/kyrgyz_language_NER") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Жусуп Мамай" |
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ner_results = nlp(example) |
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ner_results |
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``` |
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