diff --git a/tests/test_docs.py b/tests/test_docs.py
new file mode 100644
index 000000000..a6a44c154
--- /dev/null
+++ b/tests/test_docs.py
@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+"""Sphinx doctest is just too hard. Manually paste doctest examples here"""
+from spacy.en.attrs import IS_LOWER
+
+def test_1():
+    import spacy.en
+    from spacy.parts_of_speech import ADV
+    # Load the pipeline, and call it with some text.
+    nlp = spacy.en.English()
+    tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’",
+                tag=True, parse=False)
+    o = u''.join(tok.string.upper() if tok.pos == ADV else tok.string for tok in tokens)
+    assert u"‘Give it BACK,’ he pleaded ABJECTLY, ‘it’s mine.’"
+
+    o = nlp.vocab[u'back'].prob
+    assert o == -7.403977394104004
+    o = nlp.vocab[u'not'].prob
+    assert o == -5.407193660736084
+    o = nlp.vocab[u'quietly'].prob
+    assert o == -11.07155704498291
+
+
+def test2():
+    import spacy.en
+    from spacy.parts_of_speech import ADV
+    nlp = spacy.en.English()
+    # Find log probability of Nth most frequent word
+    probs = [lex.prob for lex in nlp.vocab]
+    probs.sort()
+    is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
+    tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
+    o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
+    o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
+
+    nlp.vocab[u'back'].prob
+    -7.403977394104004
+    nlp.vocab[u'not'].prob
+    -5.407193660736084
+    nlp.vocab[u'quietly'].prob
+    -11.07155704498291
+
+def test3():
+    import spacy.en
+    from spacy.parts_of_speech import ADV
+    nlp = spacy.en.English()
+    # Find log probability of Nth most frequent word
+    probs = [lex.prob for lex in nlp.vocab]
+    probs.sort()
+    is_adverb = lambda tok: tok.pos == ADV and tok.prob < probs[-1000]
+    tokens = nlp(u"‘Give it back,’ he pleaded abjectly, ‘it’s mine.’")
+    o = u''.join(tok.string.upper() if is_adverb(tok) else tok.string for tok in tokens)
+    assert o == u'‘Give it back,’ he pleaded ABJECTLY, ‘it’s mine.’'
+
+    pleaded = tokens[7]
+    assert pleaded.repvec.shape == (300,)
+    o = pleaded.repvec[:5]
+    assert sum(o) != 0
+    from numpy import dot
+    from numpy.linalg import norm
+ 
+    cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))
+    words = [w for w in nlp.vocab if w.check(IS_LOWER) and w.has_repvec]
+    words.sort(key=lambda w: cosine(w.repvec, pleaded.repvec))
+    words.reverse()
+    o = [w.orth_ for w in words[0:20]]
+    assert o == [u'pleaded', u'pled', u'plead', u'confessed', u'interceded',
+                 u'pleads', u'testified', u'conspired', u'motioned', u'demurred',
+                 u'countersued', u'remonstrated', u'begged', u'apologised',
+                 u'consented', u'acquiesced', u'petitioned', u'quarreled',
+                 u'appealed', u'pleading']
+    o = [w.orth_ for w in words[50:60]]
+    assert o == [u'counselled', u'bragged', u'backtracked', u'caucused', u'refiled',
+                 u'dueled', u'mused', u'dissented', u'yearned', u'confesses']
+    o = [w.orth_ for w in words[100:110]]
+    assert o == [u'cabled', u'ducked', u'sentenced', u'perjured', u'absconded',
+                 u'bargained', u'overstayed', u'clerked', u'confided', u'sympathizes']
+    o = [w.orth_ for w in words[1000:1010]]
+    assert o == [u'scorned', u'baled', u'righted', u'requested', u'swindled',
+                 u'posited', u'firebombed', u'slimed', u'deferred', u'sagged']
+    o = [w.orth_ for w in words[50000:50010]]
+    assert o == [u'fb', u'ford', u'systems', u'puck', u'anglers', u'ik', u'tabloid',
+                 u'dirty', u'rims', u'artists']