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Arabic NER Model

  • Github repo
  • NER BIO tagging model based on GigaBERTv4.
  • ACE2005 Training data: English + Arabic
  • NER tags 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

>>> 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('ู‚ุงู„ ูˆุฒูŠุฑ ุงู„ุนุฏู„ ุงู„ุชุฑูƒูŠ ุจูƒูŠุฑ ุจูˆุฒุฏุงุบ ุฅู† ุฃู†ู‚ุฑุฉ ุชุฑูŠุฏ 12 ู…ุดุชุจู‡ุงู‹ ุจู‡ู… ู…ู† ูู†ู„ู†ุฏุง ูˆ 21 ู…ู† ุงู„ุณูˆูŠุฏ')
>>> print(output)
[{'entity_group': 'PER', 'score': 0.9996214, 'word': 'ูˆุฒูŠุฑ', 'start': 4, 'end': 8}, {'entity_group': 'ORG', 'score': 0.9952383, 'word': 'ุงู„ุนุฏู„', 'start': 9, 'end': 14}, {'entity_group': 'GPE', 'score': 0.9996675, 'word': 'ุงู„ุชุฑูƒูŠ', 'start': 15, 'end': 21}, {'entity_group': 'PER', 'score': 0.9978992, 'word': 'ุจูƒูŠุฑ ุจูˆุฒุฏุงุบ', 'start': 22, 'end': 33}, {'entity_group': 'GPE', 'score': 0.9997154, 'word': 'ุงู†ู‚ุฑุฉ', 'start': 37, 'end': 42}, {'entity_group': 'PER', 'score': 0.9946885, 'word': 'ู…ุดุชุจู‡ุง ุจู‡ู…', 'start': 51, 'end': 62}, {'entity_group': 'GPE', 'score': 0.99967396, 'word': 'ูู†ู„ู†ุฏุง', 'start': 66, 'end': 72}, {'entity_group': 'PER', 'score': 0.99694425, 'word': '21', 'start': 75, 'end': 77}, {'entity_group': 'GPE', 'score': 0.99963355, 'word': 'ุงู„ุณูˆูŠุฏ', 'start': 81, 'end': 87}]

BibTeX entry and citation info

@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}
  } 
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