# Model Card for Model ID

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



# Model Details

## Model Description

<!-- Provide a longer summary of what this model is. -->



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

## Model Sources [optional]

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

- **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. -->

## 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. -->

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# 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.

# Training Details

## Training Data

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[More Information Needed]

## Training Procedure [optional]

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

### Preprocessing

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### Speeds, Sizes, Times

<!-- 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. -->

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### Metrics

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

[More Information Needed]

## Results

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### 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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

[More Information Needed]

</details>
{"tags": ["conversational"]}
transformers pytorch gpt2 text-generation conversational arxiv:1910.09700 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Model Card for Model ID





# Model Details

## Model Description





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

## Model Sources [optional]



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

# Uses



## 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.

# Training Details

## Training Data





## Training Procedure [optional]



### Preprocessing



### Speeds, Sizes, Times





# 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



# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
# Model Card for Model ID # Model Details ## Model Description





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



- Repository: 
- Paper [optional]: 
- Demo [optional]: # Uses ## 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. # Training Details ## Training Data ## Training Procedure [optional] ### Preprocessing ### Speeds, Sizes, Times # 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 # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for Model ID # Model Details ## Model Description





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



- Repository: 
- Paper [optional]: 
- Demo [optional]: # Uses ## 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. # Training Details ## Training Data ## Training Procedure [optional] ### Preprocessing ### Speeds, Sizes, Times # 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 # How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for Model ID# Model Details## Model Description





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



- Repository: 
- Paper [optional]: 
- Demo [optional]:# Uses## 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.# Training Details## Training Data## Training Procedure [optional]### Preprocessing### Speeds, Sizes, Times# 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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>



</details>