2022-04-03 20:45:35,951 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:35,958 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
)"
2022-04-03 20:45:35,962 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:35,967 Corpus: "Corpus: 23060 train + 4070 dev + 4150 test sentences"
2022-04-03 20:45:35,971 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:35,973 Parameters:
2022-04-03 20:45:35,975  - learning_rate: "0.05"
2022-04-03 20:45:35,977  - mini_batch_size: "4"
2022-04-03 20:45:35,980  - patience: "3"
2022-04-03 20:45:35,982  - anneal_factor: "0.5"
2022-04-03 20:45:35,985  - max_epochs: "40"
2022-04-03 20:45:35,988  - shuffle: "True"
2022-04-03 20:45:35,991  - train_with_dev: "False"
2022-04-03 20:45:35,996  - batch_growth_annealing: "False"
2022-04-03 20:45:35,998 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:36,001 Model training base path: "/content/gdrive/MyDrive/project/data/ner/model2"
2022-04-03 20:45:36,004 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:36,006 Device: cuda:0
2022-04-03 20:45:36,007 ----------------------------------------------------------------------------------------------------
2022-04-03 20:45:36,009 Embeddings storage mode: none
2022-04-03 20:45:36,559 ----------------------------------------------------------------------------------------------------
2022-04-03 20:49:55,248 epoch 40 - iter 576/5765 - loss 0.05129424 - samples/sec: 8.91 - lr: 0.050000
2022-04-03 20:54:12,817 epoch 40 - iter 1152/5765 - loss 0.05045109 - samples/sec: 8.98 - lr: 0.050000
2022-04-03 20:58:35,265 epoch 40 - iter 1728/5765 - loss 0.05189116 - samples/sec: 8.81 - lr: 0.050000
2022-04-03 21:03:11,325 epoch 40 - iter 2304/5765 - loss 0.05151945 - samples/sec: 8.38 - lr: 0.050000
2022-04-03 21:07:46,802 epoch 40 - iter 2880/5765 - loss 0.05105861 - samples/sec: 8.40 - lr: 0.050000
2022-04-03 21:12:16,061 epoch 40 - iter 3456/5765 - loss 0.05160696 - samples/sec: 8.59 - lr: 0.050000
2022-04-03 21:16:46,997 epoch 40 - iter 4032/5765 - loss 0.05158343 - samples/sec: 8.54 - lr: 0.050000
2022-04-03 21:21:12,246 epoch 40 - iter 4608/5765 - loss 0.05160290 - samples/sec: 8.72 - lr: 0.050000
2022-04-03 21:25:34,335 epoch 40 - iter 5184/5765 - loss 0.05188003 - samples/sec: 8.83 - lr: 0.050000
2022-04-03 21:30:00,227 epoch 40 - iter 5760/5765 - loss 0.05183257 - samples/sec: 8.70 - lr: 0.050000
2022-04-03 21:30:03,367 ----------------------------------------------------------------------------------------------------
2022-04-03 21:30:03,370 EPOCH 40 done: loss 0.0519 - lr 0.0500000
2022-04-03 21:36:15,762 DEV : loss 0.05283118411898613 - f1-score (micro avg)  0.828
2022-04-03 21:36:15,836 BAD EPOCHS (no improvement): 0
2022-04-03 21:36:18,064 saving best model
2022-04-03 21:36:29,253 ----------------------------------------------------------------------------------------------------
2022-04-03 21:36:29,271 loading file /content/gdrive/MyDrive/project/data/ner/model2/best-model.pt
2022-04-03 21:43:00,026 0.8616	0.82	0.8403	0.7357
2022-04-03 21:43:00,030 
Results:
- F-score (micro) 0.8403
- F-score (macro) 0.8656
- Accuracy 0.7357

By class:
              precision    recall  f1-score   support

         LOC     0.8789    0.8589    0.8688      4083
         ORG     0.8390    0.7653    0.8005      3166
         PER     0.8395    0.8169    0.8280      2741
         DAT     0.8648    0.7957    0.8288      1150
         MON     0.9758    0.9020    0.9374       357
         TIM     0.8500    0.8193    0.8344       166
         PCT     0.9615    0.9615    0.9615       156

   micro avg     0.8616    0.8200    0.8403     11819
   macro avg     0.8871    0.8456    0.8656     11819
weighted avg     0.8613    0.8200    0.8400     11819
 samples avg     0.7357    0.7357    0.7357     11819

2022-04-03 21:43:00,035 ----------------------------------------------------------------------------------------------------