DocPallet->DocBin in docs
@@ -118,20 +118,20 @@ classification.
</Infobox>
-### New DocPallet class to efficiently Doc collections
+### New DocBin class to efficiently serialize Doc collections
> #### Example
>
> ```python
-> from spacy.tokens import DocPallet
-> pallet = DocPallet(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=False)
+> from spacy.tokens import DocBin
+> doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=False)
> for doc in nlp.pipe(texts):
-> pallet.add(doc)
-> byte_data = pallet.to_bytes()
+> doc_bin.add(doc)
+> byte_data = docbin.to_bytes()
> # Deserialize later, e.g. in a new process
> nlp = spacy.blank("en")
-> pallet = DocPallet()
-> docs = list(pallet.get_docs(nlp.vocab))
+> doc_bin = DocBin()
+> docs = list(doc_bin.get_docs(nlp.vocab))
> ```
If you're working with lots of data, you'll probably need to pass analyses
@@ -140,7 +140,7 @@ save out work to disk. Often it's sufficient to use the doc.to_array()
functionality for this, and just serialize the numpy arrays --- but other times
you want a more general way to save and restore `Doc` objects.
-The new `DocPallet` class makes it easy to serialize and deserialize
+The new `DocBin` class makes it easy to serialize and deserialize
a collection of `Doc` objects together, and is much more efficient than
calling `doc.to_bytes()` on each individual `Doc` object. You can also control
what data gets saved, and you can merge pallets together for easy
