Metadata-Version: 2.1
Name: condolence-models
Version: 1.0.0
Summary: Detecting condolence, distress, and empathy in text
Home-page: UNKNOWN
Author: Naitian Zhou
Author-email: naitian@umich.edu
License: UNKNOWN
Description: # Condolence Models
        
        ## Intro
        `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`. 
        
        
        ## Install 
        
        ### Use pip
        If `pip` is installed, question-intimacy could be installed directly from it:
        
            pip3 install condolence-models
        
        ### Dependencies
            python>=3.6.0
            torch>=1.6.0
            pytorch-transformers
            markdown
            beautifulsoup4
            numpy
            tqdm
            simpletransformers
            pandas
            numpy
            
        ## Usage and Example
        
        See `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.
        
        ```py
        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]
        ```
        
        ### Classifying empathy.
        
        ```py
        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]
        ```
        
        ## Contact
        Naitian Zhou (naitian@umich.edu)
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
