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--- |
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language: |
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- ar |
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- en |
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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: أعرف كل شيء عن جيجي |
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- 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. |
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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 using Flair Embeddings |
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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. |
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## Original Datasets: |
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- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/) |
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- [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp) |
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## Results: |
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- F1-score (micro) 0.8666 |
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- F1-score (macro) 0.8488 |
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| | Named Entity Type | True Posititves | False Positives | False Negatives | Precision | Recall | class-F1 | |
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|------|-|----|----|----|-----------|--------|----------| |
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| LOC | Location| 539 | 51 | 68 | 0.9136 | 0.8880 | 0.9006 | |
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| MISC | Miscellaneous|408 | 57 | 89 | 0.8774 | 0.8209 | 0.8482 | |
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| ORG | Organisation|167 | 43 | 64 | 0.7952 | 0.7229 | 0.7574 | |
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| PER | Person (no title)|501 | 65 | 60 | 0.8852 | 0.8930 | 0.8891 | |
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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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tagger = SequenceTagger.load("julien-c/flair-ner") |
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arTagger = SequenceTagger.load('megantosh/flair-arabic-multi-ner') |
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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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```bash |
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2021-07-07 14:30:59,649 loading file /Users/mega/.flair/models/flair-ner/f22eb997f66ae2eacad974121069abaefca5fe85fce71b49e527420ff45b9283.941c7c30b38aef8d8a4eb5c1b6dd7fe8583ff723fef457382589ad6a4e859cfc |
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2021-07-07 14:31:04,654 loading file /Users/mega/.flair/models/flair-arabic-multi-ner/c7af7ddef4fdcc681fcbe1f37719348afd2862b12aa1cfd4f3b93bd2d77282c7.242d030cb106124f7f9f6a88fb9af8e390f581d42eeca013367a86d585ee6dd6 |
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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> ."]> |
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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> ."]> |
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ic| entity: <PER-span (1,2): "George Washington"> |
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ic| entity: <LOC-span (5): "Washington"> |
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ic| entity: <PER-span (1,2): "عمرو عادلي"> |
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ic| entity: <ORG-span (8,9): "الجامعة الأمريكية"> |
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ic| entity: <LOC-span (10): "بالقاهرة"> |
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ic| sentence.to_dict(tag_type='ner'): |
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{"text":"عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة .", |
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"labels":[], |
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{"entities":[{{{ |
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"text":"عمرو عادلي", |
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"start_pos":0, |
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"end_pos":10, |
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"labels":[PER (0.9826)]}, |
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{"text":"الجامعة الأمريكية", |
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"start_pos":45, |
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"end_pos":62, |
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"labels":[ORG (0.7679)]}, |
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{"text":"بالقاهرة", |
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"start_pos":64, |
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"end_pos":72, |
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"labels":[LOC (0.8079)]}]} |
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"text":"George Washington went to Washington .", |
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"labels":[], |
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"entities":[{ |
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{"text":"George Washington", |
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"start_pos":0, |
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"end_pos":17, |
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"labels":[PER (0.9968)]}, |
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{"text":"Washington""start_pos":26, |
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"end_pos":36, |
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"labels":[LOC (0.9994)]}}]} |
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``` |
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# Model Configuration |
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```python |
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SequenceTagger( |
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(embeddings): StackedEmbeddings( |
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(list_embedding_0): WordEmbeddings('glove') |
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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=4196, out_features=4196, bias=True) |
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(rnn): LSTM(4196, 256, batch_first=True, bidirectional=True) |
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(linear): Linear(in_features=512, out_features=15, 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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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) |
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# Citation |
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*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):* |
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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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year = {2021}, |
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doi = "10.13140/RG.2.2.34961.10084" |
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url = {https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects} |
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} |
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``` |