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	---
	language:
	- ar
	- en
	license: apache-2.0
	datasets:
	- AQMAR
	- ANERcorp
	thumbnail: https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@images/image/preview
	tags:
	- flair
	- Text Classification
	- token-classification
	- sequence-tagger-model
	metrics:
	- f1
	widget:
	- text: أعرف كل شيء عن جيجي
	- text: ترتقي شريحة M1 Pro وشريحة M1 Max ببنية شريحة M1 المذهلة إلى مستويات جديدة، إذ تأتيان للمرة الأولى ببنية نظام متكامل في شريحة (SoC) إلى جهاز نوت بوك للمحترفين.
	- text: "اختارها خيري بشارة كممثلة، دون سابقة معرفة أو تجربة تمثيلية، لتقف بجانب فاتن حمامة في فيلم «يوم مر ويوم حلو» (1988) وهي ما زالت شابة لم تتخطَ عامها الثاني"
	---
	# Arabic NER Model using Flair Embeddings
	Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, starting from 0.225 and a batch size of 32 with GloVe and Flair forward and backward embeddings.


	## Original Datasets:
	- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
	- [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp)

	## Results:
	- F1-score (micro) 0.8666
	- F1-score (macro) 0.8488

	\| \| Named Entity Type \| True Posititves \| False Positives \| False Negatives \| Precision \| Recall \| class-F1 \|
	\|------\|-\|----\|----\|----\|-----------\|--------\|----------\|
	\| LOC \| Location\| 539 \| 51 \| 68 \| 0.9136 \| 0.8880 \| 0.9006 \|
	\| MISC \| Miscellaneous\|408 \| 57 \| 89 \| 0.8774 \| 0.8209 \| 0.8482 \|
	\| ORG \| Organisation\|167 \| 43 \| 64 \| 0.7952 \| 0.7229 \| 0.7574 \|
	\| PER \| Person (no title)\|501 \| 65 \| 60 \| 0.8852 \| 0.8930 \| 0.8891 \|

	---

	# Usage
	```python
	from flair.data import Sentence
	from flair.models import SequenceTagger
	import pyarabic.araby as araby
	from icecream import ic

	tagger = SequenceTagger.load("julien-c/flair-ner")
	arTagger = SequenceTagger.load('megantosh/flair-arabic-multi-ner')

	sentence = Sentence('George Washington went to Washington .')
	arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة .')


	# predict NER tags
	tagger.predict(sentence)
	arTagger.predict(arSentence)

	# print sentence with predicted tags
	ic(sentence.to_tagged_string)
	ic(arSentence.to_tagged_string)

	```

	# Example
	```bash
	2021-07-07 14:30:59,649 loading file /Users/mega/.flair/models/flair-ner/f22eb997f66ae2eacad974121069abaefca5fe85fce71b49e527420ff45b9283.941c7c30b38aef8d8a4eb5c1b6dd7fe8583ff723fef457382589ad6a4e859cfc
	2021-07-07 14:31:04,654 loading file /Users/mega/.flair/models/flair-arabic-multi-ner/c7af7ddef4fdcc681fcbe1f37719348afd2862b12aa1cfd4f3b93bd2d77282c7.242d030cb106124f7f9f6a88fb9af8e390f581d42eeca013367a86d585ee6dd6
	ic\| sentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "George Washington went to Washington ." [− Tokens: 6 − Token-Labels: "George <B-PER> Washington <E-PER> went to Washington <S-LOC> ."]>
	ic\| arSentence.to_tagged_string: <bound method Sentence.to_tagged_string of Sentence: "عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة ." [− Tokens: 11 − Token-Labels: "عمرو <B-PER> عادلي <I-PER> أستاذ للاقتصاد السياسي المساعد في الجامعة <B-ORG> الأمريكية <I-ORG> بالقاهرة <B-LOC> ."]>
	ic\| entity: <PER-span (1,2): "George Washington">
	ic\| entity: <LOC-span (5): "Washington">
	ic\| entity: <PER-span (1,2): "عمرو عادلي">
	ic\| entity: <ORG-span (8,9): "الجامعة الأمريكية">
	ic\| entity: <LOC-span (10): "بالقاهرة">
	ic\| sentence.to_dict(tag_type='ner'):
	{"text":"عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة .",
	"labels":[],
	{"entities":[{{{
	"text":"عمرو عادلي",
	"start_pos":0,
	"end_pos":10,
	"labels":[PER (0.9826)]},
	{"text":"الجامعة الأمريكية",
	"start_pos":45,
	"end_pos":62,
	"labels":[ORG (0.7679)]},
	{"text":"بالقاهرة",
	"start_pos":64,
	"end_pos":72,
	"labels":[LOC (0.8079)]}]}
	"text":"George Washington went to Washington .",
	"labels":[],
	"entities":[{
	{"text":"George Washington",
	"start_pos":0,
	"end_pos":17,
	"labels":[PER (0.9968)]},
	{"text":"Washington""start_pos":26,
	"end_pos":36,
	"labels":[LOC (0.9994)]}}]}
	```

	# Model Configuration
	```python
	SequenceTagger(
	(embeddings): StackedEmbeddings(
	(list_embedding_0): WordEmbeddings('glove')
	(list_embedding_1): FlairEmbeddings(
	(lm): LanguageModel(
	(drop): Dropout(p=0.1, inplace=False)
	(encoder): Embedding(7125, 100)
	(rnn): LSTM(100, 2048)
	(decoder): Linear(in_features=2048, out_features=7125, bias=True)
	)
	)
	(list_embedding_2): FlairEmbeddings(
	(lm): LanguageModel(
	(drop): Dropout(p=0.1, inplace=False)
	(encoder): Embedding(7125, 100)
	(rnn): LSTM(100, 2048)
	(decoder): Linear(in_features=2048, out_features=7125, bias=True)
	)
	)
	)
	(word_dropout): WordDropout(p=0.05)
	(locked_dropout): LockedDropout(p=0.5)
	(embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
	(rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
	(linear): Linear(in_features=512, out_features=15, bias=True)
	(beta): 1.0
	(weights): None
	(weight_tensor) None
	```
	Due to the right-to-left in left-to-right context, some formatting errors might occur. and your code might appear like [this](https://ibb.co/ky20Lnq), (link accessed on 2020-10-27)

	# Citation
	if you use this model, please consider citing [this work](https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects):
	```latex
	@unpublished{MMHU21
	author = "M. Megahed",
	title = "Sequence Labeling Architectures in Diglossia",
	year = {2021},
	doi = "10.13140/RG.2.2.34961.10084"
	url = {https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects}
	}
	```