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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: fa |
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dataset: |
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- NSURL-2019 |
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widget: |
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- text: >- |
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اولین نمایش این فیلمها روز دوشنبه 13 اردیبهشت و از ساعت 21 در موزه سینماست. |
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metrics: |
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- f1 |
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--- |
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## Persian NER Using Flair |
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This is the 7-class Named-entity recognition model for Persian that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **90.33** (NSURL-2019) |
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Predicts NER tags: |
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| **tag** | **meaning** | |
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|:---------------------------------:|:-----------:| |
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| PER | person name | |
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| LOC | location name | |
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| ORG | organization name | |
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| DAT | date | |
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| TIM | time | |
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| PCT | percent | |
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| MON | Money| |
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Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and Pars-Bert. |
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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("PooryaPiroozfar/Flair-Persian-NER") |
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# make example sentence |
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sentence = Sentence("اولین نمایش این فیلمها روز دوشنبه 13 اردیبهشت و از ساعت 21 در موزه سینماست.") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
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This yields the following output: |
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``` |
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Span[4:8]: "روز دوشنبه 13 اردیبهشت" → DAT (1.0) |
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Span[10:12]: "ساعت 21" → TIM (1.0) |
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Span[13:15]: "موزه سینماست" → LOC (0.9999) |
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``` |
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--- |
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### Results |
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- F-score (micro) 0.9033 |
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- F-score (macro) 0.8976 |
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- Accuracy 0.8277 |
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|
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``` |
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By class: |
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precision recall f1-score support |
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ORG 0.9016 0.8667 0.8838 1523 |
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LOC 0.9113 0.9305 0.9208 1425 |
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PER 0.9216 0.9322 0.9269 1224 |
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DAT 0.8623 0.7958 0.8277 480 |
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MON 0.9665 0.9558 0.9611 181 |
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PCT 0.9375 0.9740 0.9554 77 |
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TIM 0.8235 0.7925 0.8077 53 |
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micro avg 0.9081 0.8984 0.9033 4963 |
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macro avg 0.9035 0.8925 0.8976 4963 |
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weighted avg 0.9076 0.8984 0.9028 4963 |
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samples avg 0.8277 0.8277 0.8277 4963 |
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``` |
