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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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--- |
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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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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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``` |
