Helps with loading models in a separate, dedicated process.
Caching happens on unique arguments.
- ✓ Serve models separately
- ✓ Client- and Server-side caching
- ✓ CLI interface
Should work with py2 and py3.
Assumes you have installed spacy.
Install:
pip install spacy_api[all]
Run the server:
spacy serve
Then open a python process and run code in the next section.
from spacy_api import Client
spacy_client = Client() # default args host/port
doc = spacy_client.single("How are you")
doc
# [[How, are, you]]
# iterate over sentences
for sentence in doc.sents:
for token in sentence:
print(token.text, token.pos_, token.lemma_)
# iterate over a whole document
for token in doc:
print(token)Instead of
from spacy_api import Client
use
from spacy_api import LocalClient
LocalClient/Client:
# language/model
spacy_client = Client(model="en")
# Using google pretrained vectors
spacy_client = Client(embeddings_path="en_google")To make a call:
# Tell spacy which attributes to give back, comma separated
spacy_client.single("How are you", attributes="text,lemma_,pos,vector")Naturally, you can use any combination of these.
docs = spacy_client.bulk(["How are you"]*100)
for doc in docs:
for sentence in doc.sents:
for token in sentence:
print(token.text, token.pos_, token.lemma_)