README.md

metadata

language: ar
license: apache-2.0
datasets:
  - AQMAR

Arabic NER Model using Flair Embeddings

Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, and a total batch size of 32. 11 12

Results:

• F1-score (micro) 0.8666
• F1-score (macro) 0.8488

By class: LOC tp: 539 - fp: 51 - fn: 68 - precision: 0.9136 - recall: 0.8880 - f1-score: 0.9006 MISC tp: 408 - fp: 57 - fn: 89 - precision: 0.8774 - recall: 0.8209 - f1-score: 0.8482 ORG tp: 167 - fp: 43 - fn: 64 - precision: 0.7952 - recall: 0.7229 - f1-score: 0.7574 PER tp: 501 - fp: 65 - fn: 60 - precision: 0.8852 - recall: 0.8930 - f1-score: 0.8891

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2020-10-27 12:05:47,801 Model: "SequenceTagger(
  (embeddings): StackedEmbeddings(
    (list_embedding_0): WordEmbeddings('glove')
    (list_embedding_1): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
    (list_embedding_2): FlairEmbeddings(
      (lm): LanguageModel(
        (drop): Dropout(p=0.1, inplace=False)
        (encoder): Embedding(7125, 100)
        (rnn): LSTM(100, 2048)
        (decoder): Linear(in_features=2048, out_features=7125, bias=True)
      )
    )
  )
  (word_dropout): WordDropout(p=0.05)
  (locked_dropout): LockedDropout(p=0.5)
  (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)
  (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)
  (linear): Linear(in_features=512, out_features=15, bias=True)
  (beta): 1.0
  (weights): None
  (weight_tensor) None