Detecting condolence, distress, and empathy in text
condolence-models is a package used to detect condolence and distress
expressions, as well as empathetic comments. It is released with the
EMNLP 2020 paper
Condolence and Empathy in Online Commmunities.
pip is installed, question-intimacy could be installed directly from it:
pip3 install condolence-models
python>=3.6.0 torch>=1.6.0 pytorch-transformers markdown beautifulsoup4 numpy tqdm simpletransformers pandas numpy
Usage and Example
example.py for an example of how to use the classifiers.
Note: The first time you run the code, the model parameters will need to be downloaded, which could take up significant space. The condolence and distress classifiers are about 500MB each, and the empathy classifier is about 1GB.
The interface for condolence and distress are the same. The interface for empathy is slightly different, to align with the simpletransformers interface more closely.
Classifying condolence or distress.
from condolence_models.condolence_classifier import CondolenceClassifier cc = CondolenceClassifier() # single string gets turned into a length-1 list # outputs probabilities print("I like ice cream") print(cc.predict("I like ice cream")) # [0.11919236] # multiple strings print(["I'm so sorry for your loss.", "F", "Tuesday is a good day of the week."]) print(cc.predict(["I'm so sorry for your loss.", "F", "Tuesday is a good day of the week."])) # [0.9999901 0.8716224 0.20647633]
from condolence_models.empathy_classifier import EmpathyClassifier ec = EmpathyClassifier(use_cuda=True, cuda_device=2) # list of lists # first item is target, second is observer # regression output on scale of 1 to 5 print([["", "Yes, but wouldn't that block the screen?"]]) print(ec.predict([["", "Yes, but wouldn't that block the screen?"]])) # [1.098]
Naitian Zhou (firstname.lastname@example.org)
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Hashes for condolence_models-1.0.0-py3-none-any.whl