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--- |
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language: ar |
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license: apache-2.0 |
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datasets: |
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- AQMAR |
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- ANERcorp |
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thumbnail: https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@images/image/preview |
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tags: |
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- flair |
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- Text Classification |
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- token-classification |
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- sequence-tagger-model |
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metrics: |
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- f1 |
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widget: |
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- 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" |
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--- |
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# Arabic NER Model for AQMAR dataset |
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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. |
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## Original Dataset: |
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- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/) |
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## Results: |
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- F1-score (micro) 0.9323 |
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- F1-score (macro) 0.9272 |
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| | True Posititves | False Positives | False Negatives | Precision | Recall | class-F1 | |
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|------|-----|----|----|---------|--------|----------| |
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| LOC | 164 | 7 | 13 | 0.9591 | 0.9266 | 0.9425 | |
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| MISC | 398 | 22 | 37 | 0.9476 | 0.9149 | 0.9310 | |
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| ORG | 65 | 6 | 9 | 0.9155 | 0.8784 | 0.8966 | |
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| PER | 199 | 13 | 13 | 0.9387 | 0.9387 | 0.9387 | |
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--- |
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# Usage |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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import pyarabic.araby as araby |
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from icecream import ic |
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arTagger = SequenceTagger.load('megantosh/flair-arabic-MSA-aqmar') |
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sentence = Sentence('George Washington went to Washington .') |
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arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة .') |
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# predict NER tags |
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tagger.predict(sentence) |
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arTagger.predict(arSentence) |
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# print sentence with predicted tags |
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ic(sentence.to_tagged_string) |
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ic(arSentence.to_tagged_string) |
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``` |
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# Example |
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see an example from a [similar NER model in Flair](https://huggingface.co/megantosh/flair-arabic-multi-ner) |
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# Model Configuration |
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```python |
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(embeddings): StackedEmbeddings( |
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(list_embedding_0): WordEmbeddings('ar') |
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(list_embedding_1): FlairEmbeddings( |
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(lm): LanguageModel( |
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(drop): Dropout(p=0.1, inplace=False) |
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(encoder): Embedding(7125, 100) |
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(rnn): LSTM(100, 2048) |
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(decoder): Linear(in_features=2048, out_features=7125, bias=True) |
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) |
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) |
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(list_embedding_2): FlairEmbeddings( |
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(lm): LanguageModel( |
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(drop): Dropout(p=0.1, inplace=False) |
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(encoder): Embedding(7125, 100) |
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(rnn): LSTM(100, 2048) |
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(decoder): Linear(in_features=2048, out_features=7125, bias=True) |
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) |
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) |
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) |
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(word_dropout): WordDropout(p=0.05) |
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(locked_dropout): LockedDropout(p=0.5) |
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(embedding2nn): Linear(in_features=4396, out_features=4396, bias=True) |
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(rnn): LSTM(4396, 256, batch_first=True, bidirectional=True) |
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(linear): Linear(in_features=512, out_features=14, bias=True) |
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(beta): 1.0 |
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(weights): None |
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(weight_tensor) None |
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)" |
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2021-03-31 22:19:50,654 ---------------------------------------------------------------------------------------------------- |
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2021-03-31 22:19:50,654 Corpus: "Corpus: 3025 train + 336 dev + 373 test sentences" |
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2021-03-31 22:19:50,654 ---------------------------------------------------------------------------------------------------- |
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2021-03-31 22:19:50,654 Parameters: |
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2021-03-31 22:19:50,654 - learning_rate: "0.3" |
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2021-03-31 22:19:50,654 - mini_batch_size: "48" |
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2021-03-31 22:19:50,654 - patience: "3" |
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2021-03-31 22:19:50,654 - anneal_factor: "0.5" |
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2021-03-31 22:19:50,654 - max_epochs: "150" |
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2021-03-31 22:19:50,654 - shuffle: "True" |
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2021-03-31 22:19:50,654 - train_with_dev: "False" |
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2021-03-31 22:19:50,654 - batch_growth_annealing: "False" |
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2021-03-31 22:19:50,655 ------------------------------------ |
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``` |
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Due to some formatting errors, your code might appear like [this](https://ibb.co/ky20Lnq). |
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# Citation |
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*if you use this model in your work, please consider citing this work:* |
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```latex |
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@unpublished{MMHU21 |
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author = "M. Megahed", |
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title = "Sequence Labeling Architectures in Diglossia", |
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note = "In Review", |
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} |
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``` |
