This patch takes a step towards #1487 by introducing the
doc.retokenize() context manager, to handle merging spans, and soon
splitting tokens.
The idea is to do merging and splitting like this:
with doc.retokenize() as retokenizer:
for start, end, label in matches:
retokenizer.merge(doc[start : end], attrs={'ent_type': label})
The retokenizer accumulates the merge requests, and applies them
together at the end of the block. This will allow retokenization to be
more efficient, and much less error prone.
A retokenizer.split() function will then be added, to handle splitting a
single token into multiple tokens. These methods take `Span` and `Token`
objects; if the user wants to go directly from offsets, they can append
to the .merges and .splits lists on the retokenizer.
The doc.merge() method's behaviour remains unchanged, so this patch
should be 100% backwards incompatible (modulo bugs). Internally,
doc.merge() fixes up the arguments (to handle the various deprecated styles),
opens the retokenizer, and makes the single merge.
We can later start making deprecation warnings on direct calls to doc.merge(),
to migrate people to use of the retokenize context manager.
Changed python set to cpp stl set #2032
## Description
Changed python set to cpp stl set. CPP stl set works better due to the logarithmic run time of its methods. Finding minimum in the cpp set is done in constant time as opposed to the worst case linear runtime of python set. Operations such as find,count,insert,delete are also done in either constant and logarithmic time thus making cpp set a better option to manage vectors.
Reference : http://www.cplusplus.com/reference/set/set/
### Types of change
Enhancement for `Vectors` for faster initialising of word vectors(fasttext)
This patch addresses #1660, which was caused by keying all pre-trained
vectors with the same ID when telling Thinc how to refer to them. This
meant that if multiple models were loaded that had pre-trained vectors,
errors or incorrect behaviour resulted.
The vectors class now includes a .name attribute, which defaults to:
{nlp.meta['lang']_nlp.meta['name']}.vectors
The vectors name is set in the cfg of the pipeline components under the
key pretrained_vectors. This replaces the previous cfg key
pretrained_dims.
In order to make existing models compatible with this change, we check
for the pretrained_dims key when loading models in from_disk and
from_bytes, and add the cfg key pretrained_vectors if we find it.
