diff --git a/nlp b/nlp
index 203c922..79af9af 100755
--- a/nlp
+++ b/nlp
@@ -18,33 +18,23 @@ from pyflow import WorkflowRunner
 
 def parse_arguments():
     parser = argparse.ArgumentParser(
-        description='Performs NLP of documents utilizing spaCy. The results are served as verticalized text files.'
-    )
-
-    parser.add_argument(
-        '-i',
-        dest='input_dir',
-        required=True
+        description=('Performs NLP of documents utilizing spaCy. The results '
+                     'are served as verticalized text files.')
     )
+    parser.add_argument('-i', dest='input_dir', required=True)
     parser.add_argument(
         '-l',
         choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
         dest='lang',
         required=True
     )
-    parser.add_argument(
-        '-o',
-        dest='output_dir',
-        required=True
-    )
-    parser.add_argument(
-        '--nCores',
-        default=min(4, multiprocessing.cpu_count()),
-        dest='n_cores',
-        help='total number of cores available',
-        required=False,
-        type=int
-    )
+    parser.add_argument('-o', dest='output_dir', required=True)
+    parser.add_argument('--nCores',
+                        default=min(4, multiprocessing.cpu_count()),
+                        dest='n_cores',
+                        help='total number of cores available',
+                        required=False,
+                        type=int)
     return parser.parse_args()
 
 
@@ -65,11 +55,11 @@ class NLPWorkflow(WorkflowRunner):
         '''
         create_output_directories_jobs = []
         for index, job in enumerate(self.jobs):
-            cmd = 'mkdir -p "%s"' % (job['output_dir'])
+            cmd = 'mkdir -p "{}"'.format(job['output_dir'])
             create_output_directories_jobs.append(
                 self.addTask(
                     command=cmd,
-                    label='create_output_directories_job_-_%i' % (index)
+                    label='create_output_directories_job_-_{}'.format(index)
                 )
             )
 
@@ -84,7 +74,7 @@ class NLPWorkflow(WorkflowRunner):
             max(1, int(self.n_cores / len(self.jobs)))
         )
         for index, job in enumerate(self.jobs):
-            cmd = 'spacy_nlp -l "%s" "%s" "%s"' % (
+            cmd = 'spacy_nlp -l "{}" "{}" "{}"'.format(
                 self.lang,
                 job['path'],
                 os.path.join(job['output_dir'], job['name'] + '.vrt')
@@ -92,8 +82,10 @@ class NLPWorkflow(WorkflowRunner):
             nlp_jobs.append(
                 self.addTask(
                     command=cmd,
-                    dependencies='create_output_directories_job_-_%i' % (index),
-                    label='nlp_job_-_%i' % (index),
+                    dependencies='create_output_directories_job_-_{}'.format(
+                        index
+                    ),
+                    label='nlp_job_-_{}'.format(index),
                     nCores=nlp_job_n_cores
                 )
             )
@@ -104,19 +96,13 @@ def analyze_jobs(input_dir, output_dir):
 
     for file in os.listdir(input_dir):
         if os.path.isdir(os.path.join(input_dir, file)):
-            jobs += analyze_jobs(
-                os.path.join(input_dir, file),
-                os.path.join(output_dir, file),
-            )
+            jobs += analyze_jobs(os.path.join(input_dir, file),
+                                 os.path.join(output_dir, file))
         elif file.endswith('.txt'):
-            jobs.append(
-                {
-                    'filename': file,
-                    'name': file.rsplit('.', 1)[0],
-                    'output_dir': os.path.join(output_dir, file),
-                    'path': os.path.join(input_dir, file)
-                }
-            )
+            jobs.append({'filename': file,
+                         'name': file.rsplit('.', 1)[0],
+                         'output_dir': os.path.join(output_dir, file),
+                         'path': os.path.join(input_dir, file)})
 
     return jobs
 
diff --git a/spacy_nlp b/spacy_nlp
index a20fdf5..57904d5 100755
--- a/spacy_nlp
+++ b/spacy_nlp
@@ -7,29 +7,25 @@ import spacy
 import textwrap
 
 parser = argparse.ArgumentParser(
-    description='Tag a text file with spaCy and save it as a verticalized text file.'
-)
-parser.add_argument(
-    'i',
-    metavar='txt-sourcefile',
-)
-parser.add_argument(
-    '-l',
-    choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
-    dest='lang',
-    required=True
-)
-parser.add_argument(
-    'o',
-    metavar='vrt-destfile',
+    description=('Tag a text file with spaCy and save it as a verticalized '
+                 'text file.')
 )
+parser.add_argument('i', metavar='txt-sourcefile')
+parser.add_argument('-l',
+                    choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
+                    dest='lang',
+                    required=True)
+parser.add_argument('o', metavar='vrt-destfile')
 args = parser.parse_args()
 
-SPACY_MODELS = {
-    'de': 'de_core_news_sm', 'el': 'el_core_news_sm', 'en': 'en_core_web_sm',
-    'es': 'es_core_news_sm', 'fr': 'fr_core_news_sm', 'it': 'it_core_news_sm',
-    'nl': 'nl_core_news_sm', 'pt': 'pt_core_news_sm'
-}
+SPACY_MODELS = {'de': 'de_core_news_sm',
+                'el': 'el_core_news_sm',
+                'en': 'en_core_web_sm',
+                'es': 'es_core_news_sm',
+                'fr': 'fr_core_news_sm',
+                'it': 'it_core_news_sm',
+                'nl': 'nl_core_news_sm',
+                'pt': 'pt_core_news_sm'}
 
 # Set the language model for spacy
 nlp = spacy.load(SPACY_MODELS[args.lang])
@@ -45,9 +41,9 @@ with open(args.i) as input_file:
 output_file = open(args.o, 'w+')
 
 output_file.write(
-    '<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
-        os.path.basename(args.i).rsplit(".", 1)[0]
-    )
+    '<?xml version="1.0" encoding="UTF-8"?>\n'
+    '<corpus>\n'
+    '<text id="{}">\n'.format(os.path.basename(args.i).rsplit(".", 1)[0])
 )
 for text in texts:
     # Run spacy nlp over the text (partial string if above 1 million chars)
@@ -61,11 +57,18 @@ for text in texts:
             # Write all information in .vrt style to the output file
             # text, lemma, simple_pos, pos, ner
             output_file.write(
-                token.text + '\t' + token.lemma_ + '\t'
-                + token.pos_ + '\t' + token.tag_ + '\t'
-                + (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
+                '{}\t{}\t{}\t{}\t{}\n'.format(
+                    token.text,
+                    token.lemma_,
+                    token.pos_,
+                    token.tag_,
+                    token.ent_type_ if token.ent_type_ != '' else 'NULL'
+                )
             )
         output_file.write('</s>\n')
-output_file.write('</text>\n</corpus>')
+output_file.write(
+    '</text>\n'
+    '</corpus>'
+)
 
 output_file.close()