readme: ---
  tags:
  - BERT
  - token-classification
  - sequence-tagger-model
  language:
  - ar
  - en
  license: mit
  datasets:
  - ACE2005
  ---
  # Arabic NER Model
  - [Github repo](https://github.com/edchengg/GigaBERT)
  - NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
  - ACE2005 Training data: English + Arabic
  - [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC
  
  ## Hyperparameters
  - learning_rate=2e-5
  - num_train_epochs=10
  - weight_decay=0.01
  
  ## ACE2005 Evaluation results (F1)
  | Language | Arabic | English  | 
  |:----:|:-----------:|:----:|
  |      | 89.4   | 88.8 |
  
  ## How to use
  ```python
  >>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
  
  >>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace")
  >>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace")
  >>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
  
  >>> output = ner_pip('Protests break out across the US after Supreme Court overturns.')
  >>> print(output)
  [{'entity_group': 'GPE', 'score': 0.9979881, 'word': 'us', 'start': 30, 'end': 32}, {'entity_group': 'ORG', 'score': 0.99898684, 'word': 'supreme court', 'start': 39, 'end': 52}]
  
  >>> output = ner_pip('قال وزير العدل التركي بكير بوزداغ إن أنقرة تريد 12 مشتبهاً بهم من فنلندا و 21 من السويد')
  >>> print(output)
  [{'entity_group': 'PER', 'score': 0.9996214, 'word': 'وزير', 'start': 4, 'end': 8}, {'entity_group': 'ORG', 'score': 0.9952383, 'word': 'العدل', 'start': 9, 'end': 14}, {'entity_group': 'GPE', 'score': 0.9996675, 'word': 'التركي', 'start': 15, 'end': 21}, {'entity_group': 'PER', 'score': 0.9978992, 'word': 'بكير بوزداغ', 'start': 22, 'end': 33}, {'entity_group': 'GPE', 'score': 0.9997154, 'word': 'انقرة', 'start': 37, 'end': 42}, {'entity_group': 'PER', 'score': 0.9946885, 'word': 'مشتبها بهم', 'start': 51, 'end': 62}, {'entity_group': 'GPE', 'score': 0.99967396, 'word': 'فنلندا', 'start': 66, 'end': 72}, {'entity_group': 'PER', 'score': 0.99694425, 'word': '21', 'start': 75, 'end': 77}, {'entity_group': 'GPE', 'score': 0.99963355, 'word': 'السويد', 'start': 81, 'end': 87}]
  ```
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{lan2020gigabert,
    author     = {Lan, Wuwei and Chen, Yang and Xu, Wei and Ritter, Alan},
      title      = {Giga{BERT}: Zero-shot Transfer Learning from {E}nglish to {A}rabic},
      booktitle  = {Proceedings of The 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP)},
      year       = {2020}
    } 
  ```
  