spacy pipeline component for sentiment analysis using onnx
Project description
Sentimental Onix
Sentiment Analysis using onnx for python with a focus on being spacy compatible and EEEEEASY to use.
Features
- English sentiment analysis
- Spacy pipeline component
- Sentiment model downloading from github
Install
$ pip install sentimental_onix
# download english sentiment model
$ python -m sentimental_onix download en
Usage
import spacy
from sentimental_onix import pipeline
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("sentencizer")
nlp.add_pipe("sentimental_onix", after="sentencizer")
sentences = [
(sent.text, sent._.sentiment)
for doc in nlp.pipe(
[
"i hate pasta on tuesdays",
"i like movies on wednesdays",
"i find your argument ridiculous",
"soda with straws are my favorite",
]
)
for sent in doc.sents
]
assert sentences == [
("i hate pasta on tuesdays", "Negative"),
("i like movies on wednesdays", "Positive"),
("i find your argument ridiculous", "Negative"),
("soda with straws are my favorite", "Positive"),
]
Dev setup / testing
expand
Install
install the dev package and pyenv versions
$ pip install -e ".[dev]"
$ python -m spacy download en_core_web_sm
$ python -m sentimental_onix download en
Run tests
$ black .
$ pytest -vvl
Packaging and publishing
python3 -m pip install --upgrade build twine
python3 -m build
python3 -m twine upload dist/*
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
Close
Hashes for sentimental_onix-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 972ef06cd18d25a41d96f5f1f3b2029c1780ad1ba503153372679d89c46dfb5f |
|
MD5 | 21ff907bede0e6406c1a63da95516693 |
|
BLAKE2b-256 | 52b124ecac3b4a5f3071ba1a2662a203eecb9f79312136e0abaec48ed8b7f3f8 |