Metadata-Version: 2.1
Name: smaberta
Version: 0.0.2
Summary: a wrapper for the huggingface transformer libraries
Home-page: https://github.com/SMAPPNYU/SMaBERTa.git
Author: Vishakh Padmakumar, Zhanna Terechshenko
License: MIT
Description: # SMaBERTa
        This repository contains the code for SMaBERTa, a wrapper for the huggingface transformer libraries.
        It was developed by Zhanna Terechshenko and Vishakh Padmakumar through research at the Center for 
        Social Media and Politics at NYU.
        
        ## Setup
        
        To install using pip, run
        ```
        pip install smaberta
        ```
        
        To install from the source, first download the repository by running 
        
        ```
        git clone https://github.com/SMAPPNYU/SMaBERTa.git
        ```
        
        Then, install the dependencies for this repo and setup by running
        ```
        cd SMaBERTa
        pip install -r requirements.txt
        python setup.py install
        ```
        
        ## Using the package
        
        Basic use:
        
        ```
        from smaberta import TransformerModel
        
        epochs = 3
        lr = 4e-6
        
        training_sample = ['Today is a great day', 'Today is a terrible day']
        training_labels = [1, 0]
        
        model = TransformerModel('roberta', 'roberta-base', num_labels=25, 'reprocess_input_data': True, "num_train_epochs":epochs, "learning_rate":lr,    
                                 'output_dir':'./saved_model/', 'overwrite_output_dir': True, 'fp16':False)
        
        model.train_model(training_sample, training_labels)
        
        ```
        
        For further details, see `Tutorial.ipynb` in the (examples)[https://github.com/SMAPPNYU/SMaBERTa/tree/master/examples] directory.
        
        # Acknowledgements 
        
        Code for this project was adapted from version 0.6 of https://github.com/ThilinaRajapakse/simpletransformers
        
        Vishakh Padmakumar and Zhanna Terechshenko contributed to the software writing, implementation, and testing.
        
        Megan Brown contributed to documentation and publication.
Keywords: nlp transformers classification text-classification fine-tuning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
