---
license: odc-by
language:
- en
library_name: transformers
---

# Model Card for Model ID

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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]

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

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# Uses

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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

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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

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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 

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### Preprocessing [optional]

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### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-   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

### Speeds, Sizes, Times [optional]

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# Evaluation

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## Testing Data, Factors & Metrics

### Testing Data

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### Factors

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### Metrics

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## Results

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### Summary



# Model Examination [optional]

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# Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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# Technical Specifications [optional]

## Model Architecture and Objective

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## Compute Infrastructure

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### Hardware

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### Software

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# Citation [optional]

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**BibTeX:**

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**APA:**

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# Glossary [optional]

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# More Information [optional]

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# Model Card Authors [optional]

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# Model Card Contact

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