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This model is fine tunned on GPT2 to generate text following the writings of W. E. Burghardt Du Bois

Model Details

Model Description

The model is designed to be finned tunning with writting from Historical black black writers who wrote on freedom and emancipation. This first version has GPT2 fintunned with the writings of W. E. Burghardt Du Bois.

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  • Language(s) (NLP): [More Information Needed] English
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Model Sources [optional]

https://www.gutenberg.org/files/15210/15210-h/15210-h.htm

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Uses

The models can be used as a resource to the study of Black writers on freedom and emancipation.

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

The data used in the training consist of the writings of W. E. Burghardt Du Bois. The DarkWater obtained from project Gutenberg was used. Specifiically, the chapters used are below THE SHADOW OF the YEAR, Litany at Atlanta, THE SOULS OF WHITE FOLK, The Riddle of the Sphinx, THE HANDS OF ETHIOPIA, The Princess of the Hither Isles OF WORK AND WEALTH, Second Coming, THE SERVANT IN THE HOUSE, Jesus Christ in Texas, OF THE RULING OF MEN, The Call and THE DAMNATION OF WOMEN. About 50,000 word token was used in the training.

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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  • Num examples = 1005 Num Epochs = 3 Instantaneous batch size per device = 8 Total train batch size (w. parallel, distributed & accumulation) = 8 Gradient Accumulation steps = 1 Total optimization steps = 378 Number of trainable parameters = 124439808

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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