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--- |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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language: |
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- fa |
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datasets: |
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- ontonotes |
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widget: |
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- text: "مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند ، در حالی که این کشور در طول ۱۶ سال گذشته تنها هشت سال آنرا بدون اعلام وضعیت اضطراری سپری کرده است ." |
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--- |
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## Persian Universal Part-of-Speech Tagging in Flair |
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This is the universal part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **97,73** (UD_PERSIAN) |
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Predicts Universal POS tags: |
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| **tag** | **meaning** | |
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|:---------------------------------:|:-----------:| |
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|ADJ | adjective | |
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| ADP | adposition | |
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| ADV | adverb | |
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| AUX | auxiliary | |
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| CCONJ | coordinating conjunction | |
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| DET | determiner | |
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| INTJ | interjection | |
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| NOUN | noun | |
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| NUM | numeral | |
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| PART | particle | |
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| PRON | pronoun | |
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| PUNCT | punctuation | |
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| SCONJ | subordinating conjunction | |
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| VERB | verb | |
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| X | other | |
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--- |
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### Demo: How to use in Flair |
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Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("hamedkhaledi/persain-flair-upos") |
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# make example sentence |
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sentence = Sentence("مقامات مصری به خاطر حفظ ثبات کشور در منطقهای پرآشوب بر خود میبالند .") |
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tagger.predict(sentence) |
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#print result |
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print(sentence.to_tagged_string()) |
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``` |
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This yields the following output: |
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``` |
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مقامات <NOUN> مصری <ADJ> به <ADP> خاطر <NOUN> حفظ <NOUN> ثبات <NOUN> کشور <NOUN> در <ADP> منطقهای <NOUN> پرآشوب <ADJ> بر <ADP> خود <PRON> میبالند <VERB> . <PUNCT> |
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``` |
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--- |
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### Results |
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- F-score (micro) 0.9773 |
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- F-score (macro) 0.9461 |
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- Accuracy 0.9773 |
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``` |
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By class: |
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precision recall f1-score support |
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NOUN 0.9770 0.9849 0.9809 6420 |
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ADP 0.9947 0.9916 0.9932 1909 |
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ADJ 0.9342 0.9128 0.9234 1525 |
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PUNCT 1.0000 1.0000 1.0000 1365 |
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VERB 0.9840 0.9711 0.9775 1141 |
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CCONJ 0.9912 0.9937 0.9925 794 |
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AUX 0.9622 0.9799 0.9710 546 |
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PRON 0.9751 0.9865 0.9808 517 |
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SCONJ 0.9797 0.9757 0.9777 494 |
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NUM 0.9948 1.0000 0.9974 385 |
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ADV 0.9343 0.9033 0.9185 362 |
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DET 0.9773 0.9711 0.9742 311 |
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PART 0.9916 1.0000 0.9958 237 |
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INTJ 0.8889 0.8000 0.8421 10 |
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X 0.7143 0.6250 0.6667 8 |
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micro avg 0.9773 0.9773 0.9773 16024 |
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macro avg 0.9533 0.9397 0.9461 16024 |
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weighted avg 0.9772 0.9773 0.9772 16024 |
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samples avg 0.9773 0.9773 0.9773 16024 |
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Loss: 0.12471389770507812 |
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``` |
