diff --git a/VERSION.txt b/VERSION.txt
index 7168dea..46d9844 100644
--- a/VERSION.txt
+++ b/VERSION.txt
@@ -1 +1 @@
-2.0.25
\ No newline at end of file
+2.0.26
\ No newline at end of file
diff --git a/fast_bert/data_lm.py b/fast_bert/data_lm.py
index 4a1665e..c9c93d4 100644
--- a/fast_bert/data_lm.py
+++ b/fast_bert/data_lm.py
@@ -21,7 +21,7 @@
     Dataset,
 )
 from torch.utils.data.distributed import DistributedSampler
-import spacy
+
 from tqdm import tqdm, trange
 from fastprogress.fastprogress import master_bar, progress_bar
 
@@ -73,8 +73,6 @@
 
 def create_corpus(text_list, target_path, logger=None):
 
-    #     nlp = spacy.load("en_core_web_sm", disable=["tagger", "ner", "textcat"])
-
     with open(target_path, "w") as f:
         #  Split sentences for each document
         logger.info("Formatting corpus for {}".format(target_path))
@@ -317,7 +315,7 @@ def __init__(
     # Mask tokens
 
     def mask_tokens(self, inputs, mlm_probability=0.15):
-        """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
+        """Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original."""
         labels = inputs.clone()
         # We sample a few tokens in each sequence for masked-LM training (with probability mlm_probability defaults to 0.15 in Bert/RoBERTa)
 
diff --git a/requirements.txt b/requirements.txt
index b6e5b99..983a94a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,6 @@ pytorch-lamb
 tensorboardX
 fastprogress
 scikit-learn
-spacy
 seqeval
 transformers==4.22.*
 pandas