
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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. 



- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed] English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

## Model Sources [optional]

<!-- Provide the basic links for the model. -->

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

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The models can be used as a resource to the study of Black writers on freedom and emancipation. 

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

## Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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. 


[More Information Needed]

## Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->


### Preprocessing [optional]

[More Information Needed]


### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

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[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

## Results

[More Information Needed]

### Summary



# Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

# Technical Specifications [optional]

## Model Architecture and Objective

[More Information Needed]

## Compute Infrastructure

[More Information Needed]

### Hardware

[More Information Needed]

### Software

[More Information Needed]

# Citation [optional]

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**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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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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{"language": ["en"], "license": "odc-by", "library_name": "transformers"}
transformers en arxiv:1910.09700 license:odc-by endpoints_compatible region:us
TAGS
#transformers #en #arxiv-1910.09700 #license-odc-by #endpoints_compatible #region-us 


# Model Card for Model ID



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. 



- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP):  English
- License: 
- Finetuned from model [optional]: 

## Model Sources [optional]



URL

- Repository: 
- Paper [optional]: 
- Demo [optional]: 

# Uses


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

## Direct Use





## Downstream Use [optional]





## Out-of-Scope Use





# Bias, Risks, and Limitations





## 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.



# 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. 




## Training Procedure 




### Preprocessing [optional]




### Training Hyperparameters

- Training regime:  
-   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]





# Evaluation



## Testing Data, Factors & Metrics

### Testing Data





### Factors





### Metrics





## Results



### Summary



# Model Examination [optional]





# Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

# Technical Specifications [optional]

## Model Architecture and Objective



## Compute Infrastructure



### Hardware



### Software



[optional]



BibTeX:



APA:



# Glossary [optional]





# More Information [optional]



# Model Card Authors [optional]



# Model Card Contact


# Model Card for Model ID



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. 



- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP):  English
- License: 
- Finetuned from model [optional]: ## Model Sources [optional]



URL

- Repository: 
- Paper [optional]: 
- Demo [optional]: # Uses


The models can be used as a resource to the study of Black writers on freedom and emancipation. ## Direct Use ## Downstream Use [optional] ## Out-of-Scope Use # Bias, Risks, and Limitations ## 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. # 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. ## Training Procedure ### Preprocessing [optional] ### Training Hyperparameters

- Training regime:  
-   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] # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #en #arxiv-1910.09700 #license-odc-by #endpoints_compatible #region-us 
 # Model Card for Model ID



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. 



- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP):  English
- License: 
- Finetuned from model [optional]: ## Model Sources [optional]



URL

- Repository: 
- Paper [optional]: 
- Demo [optional]: # Uses


The models can be used as a resource to the study of Black writers on freedom and emancipation. ## Direct Use ## Downstream Use [optional] ## Out-of-Scope Use # Bias, Risks, and Limitations ## 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. # 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. ## Training Procedure ### Preprocessing [optional] ### Training Hyperparameters

- Training regime:  
-   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] # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # More Information [optional] # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #en #arxiv-1910.09700 #license-odc-by #endpoints_compatible #region-us 
# Model Card for Model ID



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. 



- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP):  English
- License: 
- Finetuned from model [optional]:## Model Sources [optional]



URL

- Repository: 
- Paper [optional]: 
- Demo [optional]:# Uses


The models can be used as a resource to the study of Black writers on freedom and emancipation.## Direct Use## Downstream Use [optional]## Out-of-Scope Use# Bias, Risks, and Limitations## 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.# 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.## Training Procedure### Preprocessing [optional]### Training Hyperparameters

- Training regime:  
-   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]# Evaluation## Testing Data, Factors & Metrics### Testing Data### Factors### Metrics## Results### Summary# Model Examination [optional]# Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:# Technical Specifications [optional]## Model Architecture and Objective## Compute Infrastructure### Hardware### Software



[optional]



BibTeX:



APA:# Glossary [optional]# More Information [optional]# Model Card Authors [optional]# Model Card Contact