https://github.com/kootenpv/spacy_api

Server/Client around Spacy to load spacy only once
https://github.com/kootenpv/spacy_api

api machine-learning nlp spacy

Last synced: 3 months ago
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Server/Client around Spacy to load spacy only once

• Host: GitHub
• URL: https://github.com/kootenpv/spacy_api
• Owner: kootenpv
• Created: 2017-03-19T09:31:11.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-17T11:27:51.000Z (almost 7 years ago)
• Last Synced: 2024-10-01T14:50:04.295Z (3 months ago)
• Topics: api, machine-learning, nlp, spacy
• Language: Python
• Size: 37.1 KB
• Stars: 46
• Watchers: 5
• Forks: 7
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
## spacy_api

Helps with loading models in a separate, dedicated process.

Caching happens on unique arguments.

#### Features

- ✓ Serve models separately

- ✓ Client- and Server-side caching

- ✓ CLI interface

### Install

Should work with py2 and py3.

Assumes you have installed `spacy`.

Install:

    pip install spacy_api[all]

### Example

Run the server:

    spacy serve

Then open a python process and run code in the next section.

#### Single document

```python

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)

```

#### Switch to running spacy within the process

Instead of

    from spacy_api import Client

use

    from spacy_api import LocalClient

#### Arguments

LocalClient/Client:

```python

# language/model

spacy_client = Client(model="en")

# Using google pretrained vectors

spacy_client = Client(embeddings_path="en_google")

```

To make a call:

```python

# 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.

#### Bulk of documents

```python

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_)

```