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@@ -3,22 +3,24 @@ language: ar
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  license: apache-2.0
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  datasets:
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  - AQMAR
 
 
 
 
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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, and a total batch size of 32.
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- 11
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- 12
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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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- By class:
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- LOC tp: 539 - fp: 51 - fn: 68 - precision: 0.9136 - recall: 0.8880 - f1-score: 0.9006
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- MISC tp: 408 - fp: 57 - fn: 89 - precision: 0.8774 - recall: 0.8209 - f1-score: 0.8482
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- ORG tp: 167 - fp: 43 - fn: 64 - precision: 0.7952 - recall: 0.7229 - f1-score: 0.7574
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- PER tp: 501 - fp: 65 - fn: 60 - precision: 0.8852 - recall: 0.8930 - f1-score: 0.8891
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-
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  ---
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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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+ embeddings:
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+ - GloVe
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+ - Flair
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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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  Results:
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  - F1-score (micro) 0.8666
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  - F1-score (macro) 0.8488
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+ | | tp | fp | fn | precision | recall | class-F1 |
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+ |------|-----|----|----|-----------|--------|----------|
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+ | LOC | 539 | 51 | 68 | 0.9136 | 0.8880 | 0.9006 |
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+ | MISC | 408 | 57 | 89 | 0.8774 | 0.8209 | 0.8482 |
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+ | ORG | 167 | 43 | 64 | 0.7952 | 0.7229 | 0.7574 |
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+ | PER | 501 | 65 | 60 | 0.8852 | 0.8930 | 0.8891 |
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  ---
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