mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-22 11:59:14 +03:00
41 lines
2.0 KiB
Plaintext
41 lines
2.0 KiB
Plaintext
//- 💫 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
|