politenessr
Project description
Politenessr
Intro
Politenessr is a package used to predict the value of politeness of texts.
It is based on a fine tuned BERT model.
Install
Use pip
If pip
is installed, politenessr could be installed directly from it:
pip install politenessr
Dependencies
python>=3.6.0
torch>=0.4.1
numpy
pandas
unidecode
pytorch-pretrained-bert
pytorch-transformers
Usage and Example
Notes: During your first usage, the package will download a model file automatically, which is about 400MB.
predict
predict
is the core method of this package,
which takes a single text of a list of texts, and returns a list of raw values in [1,5]
(higher means more politeness, while lower means less).
Simplest usage
You may directly import politenessr
and use the default predict method, e.g.:
>>> import politenessr
>>> politenessr.predict(["I am totally agree with you"])
[4.3568916]
Construct from class
Alternatively, you may also construct the object from class, where you could customize the model path and device:
>>> from politenessr import Politenessr
>>> pr = Politenessr()
# Predict a single text
>>> pr.predict(["I am totally agree with you"])
[3.5638056]
# Predict a list of texts
>>> preds = pr.predict(['I am totally agree with you','I hate you'])
>>> f"Raw values are {preds}"
[3.5638053 2.2007465]
More detail on how to construct the object is available in docstrings.
Model using multiprocessing when preprocessing a large dataset into BERT input features
If you want to use several cpu cores via multiprocessing while preprocessing a large dataset, you may construct the object via
>>> pr = Politenessr(CPU_COUNT=cpu_cpunt, CHUNKSIZE=chunksize)
If you want to faster the code through multi gpus, you may construct the object via
>>> pr = Politenessr(is_paralleled=True, BATCH_SIZE = batch_size)
Contact
Junjie Wu (wujj38@mail2.sysu.edu.cn)
Project details
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