2022-03-27 02:06:28,163 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,170 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): WordEmbeddings('fa') (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) (list_embedding_2): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) ) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (embedding2nn): Linear(in_features=4396, out_features=4396, bias=True) (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True) (linear): Linear(in_features=512, out_features=17, bias=True) (beta): 1.0 (weights): None (weight_tensor) None )" 2022-03-27 02:06:28,173 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,178 Corpus: "Corpus: 23060 train + 4070 dev + 4150 test sentences" 2022-03-27 02:06:28,184 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,189 Parameters: 2022-03-27 02:06:28,194 - learning_rate: "0.1" 2022-03-27 02:06:28,198 - mini_batch_size: "4" 2022-03-27 02:06:28,203 - patience: "3" 2022-03-27 02:06:28,207 - anneal_factor: "0.5" 2022-03-27 02:06:28,212 - max_epochs: "2" 2022-03-27 02:06:28,217 - shuffle: "True" 2022-03-27 02:06:28,219 - train_with_dev: "False" 2022-03-27 02:06:28,225 - batch_growth_annealing: "False" 2022-03-27 02:06:28,227 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,231 Model training base path: "/content/gdrive/MyDrive/project/data/ner/model2" 2022-03-27 02:06:28,238 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,242 Device: cuda:0 2022-03-27 02:06:28,244 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,247 Embeddings storage mode: none 2022-03-27 02:06:30,459 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:30,469 Testing using last state of model ... 2022-03-27 02:12:42,501 0.8475 0.7185 0.7777 0.647 2022-03-27 02:12:42,506 Results: - F-score (micro) 0.7777 - F-score (macro) 0.7833 - Accuracy 0.647 By class: precision recall f1-score support LOC 0.8821 0.7877 0.8322 4083 ORG 0.8088 0.6105 0.6958 3166 PER 0.8381 0.7443 0.7884 2741 DAT 0.8298 0.6487 0.7282 1150 MON 0.9377 0.8852 0.9107 357 TIM 0.5741 0.5602 0.5671 166 PCT 0.9737 0.9487 0.9610 156 micro avg 0.8475 0.7185 0.7777 11819 macro avg 0.8349 0.7408 0.7833 11819 weighted avg 0.8457 0.7185 0.7757 11819 samples avg 0.6470 0.6470 0.6470 11819 2022-03-27 02:12:42,508 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,163 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,170 Model: "SequenceTagger( (embeddings): StackedEmbeddings( (list_embedding_0): WordEmbeddings('fa') (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) (list_embedding_2): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(5105, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=5105, bias=True) ) ) ) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (embedding2nn): Linear(in_features=4396, out_features=4396, bias=True) (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True) (linear): Linear(in_features=512, out_features=17, bias=True) (beta): 1.0 (weights): None (weight_tensor) None )" 2022-03-27 02:06:28,173 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,178 Corpus: "Corpus: 23060 train + 4070 dev + 4150 test sentences" 2022-03-27 02:06:28,184 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,189 Parameters: 2022-03-27 02:06:28,194 - learning_rate: "0.1" 2022-03-27 02:06:28,198 - mini_batch_size: "4" 2022-03-27 02:06:28,203 - patience: "3" 2022-03-27 02:06:28,207 - anneal_factor: "0.5" 2022-03-27 02:06:28,212 - max_epochs: "2" 2022-03-27 02:06:28,217 - shuffle: "True" 2022-03-27 02:06:28,219 - train_with_dev: "False" 2022-03-27 02:06:28,225 - batch_growth_annealing: "False" 2022-03-27 02:06:28,227 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,231 Model training base path: "/content/gdrive/MyDrive/project/data/ner/model2" 2022-03-27 02:06:28,238 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,242 Device: cuda:0 2022-03-27 02:06:28,244 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:28,247 Embeddings storage mode: none 2022-03-27 02:06:30,459 ---------------------------------------------------------------------------------------------------- 2022-03-27 02:06:30,469 Testing using last state of model ... 2022-03-27 02:12:42,501 0.8475 0.7185 0.7777 0.647 2022-03-27 02:12:42,506 Results: - F-score (micro) 0.7777 - F-score (macro) 0.7833 - Accuracy 0.647 By class: precision recall f1-score support LOC 0.8821 0.7877 0.8322 4083 ORG 0.8088 0.6105 0.6958 3166 PER 0.8381 0.7443 0.7884 2741 DAT 0.8298 0.6487 0.7282 1150 MON 0.9377 0.8852 0.9107 357 TIM 0.5741 0.5602 0.5671 166 PCT 0.9737 0.9487 0.9610 156 micro avg 0.8475 0.7185 0.7777 11819 macro avg 0.8349 0.7408 0.7833 11819 weighted avg 0.8457 0.7185 0.7757 11819 samples avg 0.6470 0.6470 0.6470 11819 2022-03-27 02:12:42,508 ----------------------------------------------------------------------------------------------------