diff --git a/spacy/cli/init_model.py b/spacy/cli/init_model.py
index e5a17d230..8a603fbcb 100644
--- a/spacy/cli/init_model.py
+++ b/spacy/cli/init_model.py
@@ -10,17 +10,12 @@ from pathlib import Path
 from preshed.counter import PreshCounter
 import tarfile
 import gzip
+import zipfile
 
-from ._messages import Messages
+from ..compat import fix_text
 from ..vectors import Vectors
-from ..errors import Warnings, user_warning
 from ..util import prints, ensure_path, get_lang_class
 
-try:
-    import ftfy
-except ImportError:
-    ftfy = None
-
 
 @plac.annotations(
     lang=("model language", "positional", None, str),
@@ -39,13 +34,16 @@ def init_model(lang, output_dir, freqs_loc=None, clusters_loc=None, vectors_loc=
     and word vectors.
     """
     if freqs_loc is not None and not freqs_loc.exists():
-        prints(freqs_loc, title=Messages.M037, exits=1)
+        prints(freqs_loc, title="Can't find words frequencies file", exits=1)
     clusters_loc = ensure_path(clusters_loc)
     vectors_loc = ensure_path(vectors_loc)
+
     probs, oov_prob = read_freqs(freqs_loc) if freqs_loc is not None else ({}, -20)
     vectors_data, vector_keys = read_vectors(vectors_loc) if vectors_loc else (None, None)
     clusters = read_clusters(clusters_loc) if clusters_loc else {}
+
     nlp = create_model(lang, probs, oov_prob, clusters, vectors_data, vector_keys, prune_vectors)
+
     if not output_dir.exists():
         output_dir.mkdir()
     nlp.to_disk(output_dir)
@@ -54,20 +52,26 @@ def init_model(lang, output_dir, freqs_loc=None, clusters_loc=None, vectors_loc=
 def open_file(loc):
     '''Handle .gz, .tar.gz or unzipped files'''
     loc = ensure_path(loc)
+    print("Open loc")
     if tarfile.is_tarfile(str(loc)):
         return tarfile.open(str(loc), 'r:gz')
     elif loc.parts[-1].endswith('gz'):
         return (line.decode('utf8') for line in gzip.open(str(loc), 'r'))
+    elif loc.parts[-1].endswith('zip'):
+        zip_file = zipfile.ZipFile(str(loc))
+        names = zip_file.namelist()
+        file_ = zip_file.open(names[0])
+        return (line.decode('utf8') for line in file_)
     else:
         return loc.open('r', encoding='utf8')
 
-
 def create_model(lang, probs, oov_prob, clusters, vectors_data, vector_keys, prune_vectors):
     print("Creating model...")
     lang_class = get_lang_class(lang)
     nlp = lang_class()
     for lexeme in nlp.vocab:
         lexeme.rank = 0
+
     lex_added = 0
     for i, (word, prob) in enumerate(tqdm(sorted(probs.items(), key=lambda item: item[1], reverse=True))):
         lexeme = nlp.vocab[word]
@@ -87,13 +91,15 @@ def create_model(lang, probs, oov_prob, clusters, vectors_data, vector_keys, pru
             lexeme = nlp.vocab[word]
             lexeme.is_oov = False
             lex_added += 1
+
     if len(vectors_data):
         nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
     if prune_vectors >= 1:
         nlp.vocab.prune_vectors(prune_vectors)
     vec_added = len(nlp.vocab.vectors)
-    prints(Messages.M039.format(entries=lex_added, vectors=vec_added),
-           title=Messages.M038)
+
+    prints("{} entries, {} vectors".format(lex_added, vec_added),
+           title="Sucessfully compiled vocab")
     return nlp
 
 
@@ -104,8 +110,12 @@ def read_vectors(vectors_loc):
     vectors_data = numpy.zeros(shape=shape, dtype='f')
     vectors_keys = []
     for i, line in enumerate(tqdm(f)):
-        pieces = line.split()
+        line = line.rstrip()
+        pieces = line.rsplit(' ', vectors_data.shape[1]+1)
         word = pieces.pop(0)
+        if len(pieces) != vectors_data.shape[1]:
+            print(word, repr(line))
+            raise ValueError("Bad line in file")
         vectors_data[i] = numpy.asarray(pieces, dtype='f')
         vectors_keys.append(word)
     return vectors_data, vectors_keys
@@ -140,14 +150,11 @@ def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
 def read_clusters(clusters_loc):
     print("Reading clusters...")
     clusters = {}
-    if ftfy is None:
-        user_warning(Warnings.W004)
     with clusters_loc.open() as f:
         for line in tqdm(f):
             try:
                 cluster, word, freq = line.split()
-                if ftfy is not None:
-                    word = ftfy.fix_text(word)
+                word = fix_text(word)
             except ValueError:
                 continue
             # If the clusterer has only seen the word a few times, its