diff --git a/doc2vec.py b/doc2vec.py
deleted file mode 100644
index e3f09a0..0000000
--- a/doc2vec.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from gensim.models.doc2vec import Doc2Vec, TaggedDocument
-from chemdataextractor.doc.text import Text
-from chemdataextractor.nlp.tokenize import ChemWordTokenizer
-import os
-import time  
-import numpy as np
-
-class DocumentVector:
-    def __init__(self, vector_size=200, alpha=0.025, min_alpha=0.00025, min_count=1, workers=4, dm=1, epochs=10):
-
-        self.vector_size = vector_size
-        self.alpha = alpha
-        self.min_count = min_count
-        self.min_alpha = min_alpha
-        self.workers = workers
-        self.dm = dm
-        self.epochs = epochs
-
-    # Lowercase words that are not chemical formulas using chemdataextractor
-    def normalize(self, no_chem_word):
-        no_chem_word = no_chem_word.lower()
-        return no_chem_word   
-    
-    # Tokenize the corpus using chemdataextractor's tokenizer
-    def tokenize(self, corpus):
-        corpus = Text(corpus)
-
-        total_token_list_loc = []
-        total_token_loc = []
-        total_token = []
-
-        for word_token in corpus.tokens:
-            
-            for word in word_token:
-                total_token_loc.append((word.start, word.end))
-                total_token.append(word.text)
-
-
-        for pair in zip(total_token_loc, total_token):
-            total_token_list_loc.append(list(pair))
-
-        return total_token
-
-    # Create a list of preprocessed tokens using the above two functions
-    def preprocess(self, corpus):
-
-        total_token = self.tokenize(corpus)
-
-        cwt = ChemWordTokenizer()
-
-        for i in range(0, len(total_token)):
-            chemicals_token_list = []
-            normalized_word_list = []
-
-            entity_word = Text(total_token[i])
-        
-            if entity_word.cems != [] :
-                    for chemicals in entity_word.cems :
-                        chemicals = chemicals.text 
-                        chemicals_token_list.append(chemicals)
-                        
-            if str(entity_word) not in chemicals_token_list : 
-                entity_word = entity_word.text
-                normalized_word = entity_word.lower()
-                normalized_word_list.append(normalized_word)
-                total_token[i] = normalized_word
-
-        return total_token
-
-    # Train a doc2vec model and save the trained model
-    def doc2vec(self, tag_data) :
-
-        model = Doc2Vec(vector_size = self.vector_size ,
-                        alpha = self.alpha,
-                        min_count = self.min_count,
-                        min_alpha = self.min_alpha,
-                        workers = self.workers,
-                        dm = self.dm)
-
-        model.build_vocab(tag_data)
-
-
-        model.train (tag_data,
-                    total_examples=model.corpus_count,
-                    epochs=self.epochs)
-        model.save("doc2vec_document_20211020.model")
-        
-        return model
-
-    # Run function
-    def run(self, path_dir) :
-        tag_data = []
-        file_list = os.listdir(path_dir)
-
-        for file in file_list:
-            
-            with open(path_dir+'/'+file, 'r', encoding='UTF8') as f:
-                corpus = f.read()
-
-            total_token = self.preprocess(corpus)
-
-            tag_data.append(TaggedDocument(words=total_token, tags=[str(file[:-4])]))
-            
-            print('tokenization finished' +'\t'+ str(file[:-4]))
-
-
-        model = self.doc2vec(tag_data)
-
-
-if __name__ == '__main__':
-    print(time.strftime('%c', time.localtime(time.time())))
-
-    path_dir = 'D:/corpus/corpus_20211013'
-
-    documentvector = DocumentVector()
-    documentvector.run(path_dir)
-
-    print(time.strftime('%c', time.localtime(time.time())))