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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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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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``` |
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2020-10-27 12:05:47,801 Model: "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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``` |
