Create README.md

README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: fa
+dataset:
+- NSURL-2019
+widget:
+- text: >-
+    کارنامه نشر، وابسته به موسسه خانه کتاب و زیر نظر احمد مسجدی جامعی معاون امور
+    فرهنگی وزارت فرهنگ و ارشاد اسلامی است.
+metrics:
+- f1
+---
+
+## Persian NER Using Flair
+
+This is the 7-class Named-entity recognition model for Persian that ships with [Flair](https://github.com/flairNLP/flair/).
+
+F1-Score: **90.33** (NSURL-2019)
+
+Predicts NER tags:
+
+| **tag**                        | **meaning** |
+|:---------------------------------:|:-----------:|
+| PER         | person name | 
+| LOC         | location name | 
+| ORG         | organization name | 
+| DAT         | date |
+| TIM         | time |
+| PCT         | percent |
+| MON         | Money|
+
+Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and Pars-Bert.
+
+---
+
+### Demo: How to use in Flair
+
+Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
+
+```python
+from flair.data import Sentence
+from flair.models import SequenceTagger
+# load tagger
+tagger = SequenceTagger.load("PooryaPiroozfar/Flair_Persian_NER")
+# make example sentence
+sentence = Sentence("کارنامه نشر، وابسته به موسسه خانه کتاب و زیر نظر احمد مسجدی جامعی معاون امور فرهنگی وزارت فرهنگ و ارشاد اسلامی است.")
+tagger.predict(sentence)
+#print result
+print(sentence.to_tagged_string())
+```
+
+This yields the following output:
+```
+
+```
+
+---
+
+### Results
+- F-score (micro) 0.9033
+- F-score (macro) 0.8976
+- Accuracy 0.8277
+
+```
+By class:
+              precision    recall  f1-score   support
+
+         ORG     0.9016    0.8667    0.8838      1523
+         LOC     0.9113    0.9305    0.9208      1425
+         PER     0.9216    0.9322    0.9269      1224
+         DAT     0.8623    0.7958    0.8277       480
+         MON     0.9665    0.9558    0.9611       181
+         PCT     0.9375    0.9740    0.9554        77
+         TIM     0.8235    0.7925    0.8077        53
+
+   micro avg     0.9081    0.8984    0.9033      4963
+   macro avg     0.9035    0.8925    0.8976      4963
+weighted avg     0.9076    0.8984    0.9028      4963
+ samples avg     0.8277    0.8277    0.8277      4963
+              
+```