//- 💫 DOCS > USAGE > SPACY 101 > SERIALIZATION p | If you've been modifying the pipeline, vocabulary vectors and entities, or made | updates to the model, you'll eventually want | to #[strong save your progress] – for example, everything that's in your #[code nlp] | object. This means you'll have to translate its contents and structure | into a format that can be saved, like a file or a byte string. This | process is called serialization. spaCy comes with | #[strong built-in serialization methods] and supports the | #[+a("http://www.diveintopython3.net/serializing.html#dump") Pickle protocol]. +aside("What's pickle?") | Pickle is Python's built-in object persistance system. It lets you | transfer arbitrary Python objects between processes. This is usually used | to load an object to and from disk, but it's also used for distributed | computing, e.g. with | #[+a("https://spark.apache.org/docs/0.9.0/python-programming-guide.html") PySpark] | or #[+a("http://dask.pydata.org/en/latest/") Dask]. When you unpickle an | object, you're agreeing to execute whatever code it contains. It's like | calling #[code eval()] on a string – so don't unpickle objects from | untrusted sources. p | All container classes and pipeline components, i.e. for cls in ["Doc", "Language", "Tokenizer", "Tagger", "DependencyParser", "EntityRecognizer", "Vocab", "StringStore"] | #[+api(cls.toLowerCase()) #[code=cls]], | have the following methods available: +table(["Method", "Returns", "Example"]) - style = [1, 0, 1] +annotation-row(["to_bytes", "bytes", "nlp.to_bytes()"], style) +annotation-row(["from_bytes", "object", "nlp.from_bytes(bytes)"], style) +annotation-row(["to_disk", "-", "nlp.to_disk('/path')"], style) +annotation-row(["from_disk", "object", "nlp.from_disk('/path')"], style) +code. moby_dick = open('moby_dick.txt', 'r') # open a large document doc = nlp(moby_dick) # process it doc.to_disk('/moby_dick.bin') # save the processed Doc