ner net

run/entity_extraction/augmentedNER/data.py

@@ -73,7 +73,7 @@ class Data:
         self.HP_char_hidden_dim = 50
         self.HP_hidden_dim = 128
         self.HP_dropout = 0.5
-        self.HP_lstm_layer = 1
+        self.HP_lstm_layer = 2
         self.HP_bilstm = True
         self.HP_use_char = False
         self.HP_gpu = True

run/entity_extraction/augmentedNER/main.py

@@ -1,5 +1,7 @@
 # -*- coding: utf-8 -*-
+import warnings
 
+warnings.filterwarnings("ignore")
 
 import argparse
 import copy
@@ -537,6 +539,8 @@ if __name__ == '__main__':
             with open(save_data_name, 'wb') as f:
                 pickle.dump(data, f)
             set_seed(seed_num)
+
+        data.show_data_summary()
         print('data.use_biword=', data.use_bigram)
         print('data.HP_batch_size=', data.HP_batch_size)
         train(data, save_model_dir, seg,debug=False)

run/entity_extraction/lstm_crf/main.py

@@ -62,8 +62,8 @@ def get_args():
     parser.add_argument("--max_len", default=1000, type=int)
     parser.add_argument('--word_emb_dim', type=int, default=300)
     parser.add_argument('--char_emb_dim', type=int, default=300)
-    parser.add_argument('--hidden_size', type=int, default=300)
-    parser.add_argument('--num_layers', type=int, default=1)
+    parser.add_argument('--hidden_size', type=int, default=150)
+    parser.add_argument('--num_layers', type=int, default=2)
     parser.add_argument('--dropout', type=int, default=0.5)
     parser.add_argument('--rnn_encoder', type=str, default='lstm', help="must choose in blow: lstm or gru")
     parser.add_argument('--bidirectional', type=bool, default=True)

run/entity_extraction/lstm_crf/train.py

@@ -9,7 +9,7 @@ import torch
 from torch import nn
 from tqdm import tqdm
 
-import models.ner_net.lstm_crf_v2 as ner
+import models.ner_net.lstm_crf as ner
 from models.ner_net.tener import TENER
 from utils.metrics import SpanFPreRecMetric
 from utils.optimizer_util import set_optimizer
@@ -73,7 +73,7 @@ class Trainer(object):
         step_gap = int(int(len(self.eval_file_choice['train']) / args.train_batch_size) / 20)
         for epoch in range(int(args.epoch_num)):
 
-            self.optimizer = lr_decay(self.optimizer, epoch, 0.05, args.learning_rate)
+            # self.optimizer = lr_decay(self.optimizer, epoch, 0.05, args.learning_rate)
 
             global_loss = 0.0