readme: ---
  language: ar
  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: "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 for AQMAR dataset
  Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings.
  
  
  ## Original Dataset:
  - [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
  
  ## Results:
  - F1-score (micro) 0.9323
  - F1-score (macro) 0.9272
  
  |      | True Posititves  | False Positives | False Negatives | Precision | Recall | class-F1 |
  |------|-----|----|----|---------|--------|----------|
  | LOC  | 164 | 7  | 13 | 0.9591  | 0.9266 | 0.9425   |
  | MISC | 398 | 22 | 37 |  0.9476 | 0.9149 | 0.9310   |
  | ORG  | 65  | 6  | 9  | 0.9155  | 0.8784 | 0.8966   |
  | PER  | 199 | 13 | 13 | 0.9387  | 0.9387 | 0.9387   |
  
  ---
  
  # Usage
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  import pyarabic.araby as araby
  from icecream import ic
  
  arTagger = SequenceTagger.load('megantosh/flair-arabic-MSA-aqmar')
  
  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
  see an example from a [similar NER model in Flair](https://huggingface.co/megantosh/flair-arabic-multi-ner)
  
  # Model Configuration
  ```python
    (embeddings): StackedEmbeddings(
      (list_embedding_0): WordEmbeddings('ar')
      (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=4396, out_features=4396, bias=True)
    (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True)
    (linear): Linear(in_features=512, out_features=14, bias=True)
    (beta): 1.0
    (weights): None
    (weight_tensor) None
  )"
  2021-03-31 22:19:50,654 ----------------------------------------------------------------------------------------------------
  2021-03-31 22:19:50,654 Corpus: "Corpus: 3025 train + 336 dev + 373 test sentences"
  2021-03-31 22:19:50,654 ----------------------------------------------------------------------------------------------------
  2021-03-31 22:19:50,654 Parameters:
  2021-03-31 22:19:50,654  - learning_rate: "0.3"
  2021-03-31 22:19:50,654  - mini_batch_size: "48"
  2021-03-31 22:19:50,654  - patience: "3"
  2021-03-31 22:19:50,654  - anneal_factor: "0.5"
  2021-03-31 22:19:50,654  - max_epochs: "150"
  2021-03-31 22:19:50,654  - shuffle: "True"
  2021-03-31 22:19:50,654  - train_with_dev: "False"
  2021-03-31 22:19:50,654  - batch_growth_annealing: "False"
  2021-03-31 22:19:50,655 ------------------------------------
   ```
  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}
  }
  ```