diff --git a/.gitignore b/.gitignore
index adbb97d..08d780c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
-data/
\ No newline at end of file
+data/
+
+venv/**
\ No newline at end of file
diff --git a/3_export_spacy.sh b/3_export_spacy.sh
index ba6dd42..0fe4515 100755
--- a/3_export_spacy.sh
+++ b/3_export_spacy.sh
@@ -14,6 +14,6 @@ python spacy_vectors.py $VECTORS_FILE $OUTPUT_FOLDER_VECTORS $OUTPUT_FOLDER_MODE
 # now create python package
 python -m spacy package $OUTPUT_FOLDER_MODEL $PACKAGE_FOLDER
 
-pushd $PACKAGE_FOLDER/it_vectors_wiki_lg-1.0.0
+pushd $PACKAGE_FOLDER/it_vectors_wiki_lg-1.0.1
 python setup.py sdist
 popd
diff --git a/README.md b/README.md
index 95efb48..9a00e33 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ Option 1: do the preceding steps to train the vectors and then load the vectors
 
 Option 2: install with pip the complete model from [there](https://github.com/MartinoMensio/it_vectors_wiki_spacy/releases/download/v1.0/it_vectors_wiki_lg-1.0.0.tar.gz) with the following command:
 ```bash
-pip install -U https://github.com/MartinoMensio/it_vectors_wiki_spacy/releases/download/v1.0/it_vectors_wiki_lg-1.0.0.tar.gz
+pip install -U https://github.com/MartinoMensio/it_vectors_wiki_spacy/releases/download/v1.0.1/it_vectors_wiki_lg-1.0.1.tar.gz
 ```
 then simply load the model in SpaCy with `nlp = spacy.load('it_vectors_wiki_lg')`
 
diff --git a/spacy_vectors.py b/spacy_vectors.py
index 2873c1b..6d5e8a7 100644
--- a/spacy_vectors.py
+++ b/spacy_vectors.py
@@ -5,7 +5,7 @@
 
 def update_progress(curr, tot):
     workdone = curr/tot
-    print("\rProgress: [{0:50s}] {1:.1f}% - {2}/{3}".format('#' * int(workdone * 50), workdone*100, curr, tot), 
+    print("\rProgress: [{0:50s}] {1:.1f}% - {2}/{3}".format('#' * int(workdone * 50), workdone*100, curr, tot),
 end="", flush=True)
 
 @plac.annotations(
@@ -37,8 +37,8 @@ def main(input_file, output_folder_vectors=None, output_folder_model=None):
         # edit package details
         nlp.meta['name'] = 'vectors_wiki_lg'
         nlp.meta['lang'] = 'it'
-        nlp.meta['version'] = '1.0.0'
-        nlp.meta['spacy_version'] = '>=2.0.0,<3.0.0'
+        nlp.meta['version'] = '1.0.1'
+        nlp.meta['spacy_version'] = '>=2.1.0,<3.0.0'
         nlp.meta['description'] = '300-dimensional word vectors trained on Wikipedia with GloVe.'
         nlp.meta['author'] = 'Martino Mensio'
         nlp.meta['email'] = 'martinomensio@outlook.it'