Instructions to use hamedkhaledi/persian-flair-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use hamedkhaledi/persian-flair-pos with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("hamedkhaledi/persian-flair-pos") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d108b16
Create README.md
Browse files
README.md
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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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datasets:
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- UPC-2017
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widget:
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- text: "تمام ایران یک تابستان تنوری را تجربه میکند ."
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---
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## Persian Part-of-Speech Tagging in Flair
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This is the part-of-speech tagging model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
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F1-Score: **??** (UPC-2017)
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List of Tags in UPC:
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| **tag** | **meaning** |
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|:--------:|:-----------------------:|
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| ADJ | adjective |
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| ADJ_CMPR | Comparative adjective |
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| ADJ_INO | Participle adjective |
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| ADJ_SUP | Superlative adjective |
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| ADJ_VOC | Vocative adjective |
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| ADV | Adverb |
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| ADV_COMP | Adverb of comparison |
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| ADV_I | Adverb of interrogation |
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| ADV_LOC | Adverb of location |
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| ADV_NEG | Adverb of negation |
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| ADV_TIME | Adverb of time |
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| CLITIC | Accusative marker |
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| CON | Conjunction |
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| DELM | Delimiter |
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| DET | Determiner |
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| FW | Foreign Word |
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| INT | Interjection |
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| N_PL | Plural noun |
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| N_SING | Singular noun |
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| NUM | Numeral |
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| N_VOC | Vocative noun |
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| P | Preposition |
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| PREV | Preverbal particle |
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| PRO | Pronoun |
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| SYM | Symbol |
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| V_AUX | Auxiliary verb |
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| V_PA | Past tense verb |
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| V_PP | Past participle verb |
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| V_PRS | Present tense verb |
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| V_SUB | Subjunctive verb |
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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-pos")
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# make example sentence
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sentence = Sentence("تمام ایران یک تابستان تنوری را تجربه میکند .")
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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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تمام <DET> ایران <N_SING> یک <NUM> تابستان <N_SING> تنوری <ADJ> را <CLITIC> تجربه <N_SING> میکند <V_PRS> . <DELM>
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```
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