/sequence-labeling

Sequence Labeling implemented by BiLSTM using Tensorflow

Primary LanguagePython

Sequence-labeling

Sequence Labeling implemented by Bi-LSTM using Tensorflow.

Benchmark for building a (Bidirectional) LSTM model.

The hyperparameters used in the model:

  • learning_rate - the initial value of the learning rate
  • max_lr_epoch - after max_lr_epoch, the learning rate will be decreased
  • num_layers - the number of (Bi)LSTM layers
  • num_steps - the number of unrolled steps of (Bi)LSTM
  • hidden_size - the number of (Bi)LSTM units
  • num_epochs - the total number of epochs for training
  • keep_prob - the probability of keeping weights in the dropout layer
  • lr_decay - the decay of the learning rate
  • batch_size - number of inputs

Evaluation

  • Accuracy
  • F1 Score

Todo

  • BiLSTM + CRF
  • BiLSTM + CRF + CNN