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spaCy finally supports another language, in addition to English. We're lucky to have Wolfgang Seeker on the team, and the new German model is just the beginning.
Now that there are multiple languages, you should consider loading spaCy via the load() function. This function also makes it easier to load extra word vector data for English:

.. code:: python

    import spacy
    en_nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
    de_nlp = spacy.load('de')

To support use of the load function, there are also two new helper functions: spacy.get_lang_class and spacy.set_lang_class.
Once the German model is loaded, you can use it just like the English model:

.. code:: python

    doc = nlp(u'''Wikipedia ist ein Projekt zum Aufbau einer Enzyklopädie aus freien Inhalten, zu dem du mit deinem Wissen beitragen kannst. Seit Mai 2001 sind 1.936.257 Artikel in deutscher Sprache entstanden.''')

    for sent in doc.sents:
        print(sent.root.text, sent.root.n_lefts, sent.root.n_rights)

    # (u'ist', 1, 2)
    # (u'sind', 1, 3)

The German model provides tokenization, POS tagging, sentence boundary detection, syntactic dependency parsing, recognition of organisation, location and person entities, and word vector representations trained on a mix of open subtitles and Wikipedia data. It doesn't yet provide lemmatisation or morphological analysis, and it doesn't yet recognise numeric entities such as numbers and dates.

Bugfixes
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* spaCy < 0.100.7 had a bug in the semantics of the Token.__str__ and Token.__unicode__ built-ins: they included a trailing space.
* Improve handling of "infixed" hyphens. Previously the tokenizer struggled with multiple hyphens, such as "well-to-do".

* Improve handling of periods after mixed-case tokens

* Improve lemmatization for English special-case tokens

* Fix bug that allowed spaces to be treated as heads in the syntactic parse

* Fix bug that led to inconsistent sentence boundaries before and after serialisation.

* Fix bug from deserialising untagged documents.