flypythoncom
diff --git a/‎modules/data/bert_data.py
Lines changed: 5 additions & 0 deletions b/‎modules/data/bert_data.py
Lines changed: 5 additions & 0 deletions
diff --git a/‎modules/train/train.py
Lines changed: 9 additions & 1 deletion b/‎modules/train/train.py
Lines changed: 9 additions & 1 deletion
diff --git a/‎modules/utils/utils.py
Lines changed: 6 additions & 0 deletions b/‎modules/utils/utils.py
Lines changed: 6 additions & 0 deletions
@@ -1,9 +1,11 @@
 from torch.utils.data import DataLoader
 from modules.data import tokenization
+from modules.utils.utils import ipython_info
 import torch
 import pandas as pd
 import numpy as np
 from tqdm._tqdm_notebook import tqdm_notebook
+from tqdm import tqdm
 
 
 class InputFeatures(object):
@@ -276,6 +278,9 @@ def __init__(self, train_dl, valid_dl, tokenizer, label2idx, max_seq_len=424,
     @classmethod
     def create(cls,
                train_path, valid_path, vocab_file, batch_size=16, cuda=True, is_cls=False, data_type="bert_cased", max_seq_len=424):
+        if ipython_info():
+            global tqdm_notebook
+            tqdm_notebook = tqdm
         if data_type == "bert_cased":
             do_lower_case = False
             fn = get_bert_data_loaders
 
@@ -1,15 +1,20 @@
 from tqdm._tqdm_notebook import tqdm_notebook
+from tqdm import tqdm
 from sklearn_crfsuite.metrics import flat_classificati
8000
on_report
 import logging
 import torch
 from modules.utils.plot_metrics import get_mean_max_metric
-from modules.train.clr import CyclicLR
+from modules.utils.utils import ipython_info
 from torch.optim import Adam
 from .optimization import BertAdam
 
 
 logging.basicConfig(level=logging.INFO)
 
+if __name__ == '__main__':
+    print(1)
+    tqdm_notebook = tqdm
+
 
 def train_step(dl, model, optimizer, lr_scheduler=None, clip=None, num_epoch=1):
     model.train()
@@ -132,6 +137,9 @@ def predict(dl, model, id2label, id2cls=None):
 class NerLearner(object):
     def __init__(self, model, data, best_model_path, lr=0.001, betas=list([0.8, 0.9]), clip=5,
                  verbose=True, sup_labels=None, t_total=-1, warmup=0.1, weight_decay=0.01):
+        if ipython_info():
+            global tqdm_notebook
+            tqdm_notebook = tqdm
         self.model = model
         self.optimizer = BertAdam(model, lr, t_total=t_total, b1=betas[0], b2=betas[1], max_grad_norm=clip)
         self.optimizer_defaults = dict(model=model, lr=lr, warmup=warmup, t_total=t_total, schedule='warmup_linear',
 
@@ -1,3 +1,9 @@
+import sys
+import __main__ as main
+
+
+def ipython_info():
+    return hasattr(main, '__file__')
 
 
 def bert_labels2tokens(dl, labels):