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This documentation is aimed to help communicate key information about a machine learning model. This model card has been borrowed and adapted from Annotated Hugging Face Model Card. A model card can also be generated through huggingface_hub library which is a python interface.
Last updated : August 2023.
Provide a short summary of model.
This is intended to make it easier to navigate the different sections.
- Model details
- Intended use
- Bias, Risks, and Limitations
- Technical Specifications
- Training Details
- Evaluation
- Environmental Impact
- How to Get Started with the Model
- Citations
- More Information
This section describes important background information of the developers and the model.
Provide a longer summary of what this model is. Include details such as the input and output of the model.
- Developed by:
- Model type:
- Language(s) (NLP):
- License:
Some other optional details that can be added :
- Shared by :
- Finetuned from model:
This section is optional, and is used to provide direct sources to the users.
- Repository:
- Paper:
- Demo:
This section is optional.
This section is optional.
This section aims to describe how the model was intended to be used in various applied contexts, the intended users of the model and those that will be affected by it.
Provide the direct uses of the model, when it is not fine-tuned or any post-processing is done.
Provide a description of uses of the model after fine-tuning is done, or it is added to a larger ecosytem.
Provide a description of misuse of the model.
Provide any forseeable harm, technical and sociotechnical limitations.
This heading is optional.
Provide a description of the model specifications
- Hardware:
- Software:
This section is intended to provide information that describes the training of the model, and can allow users to replicate the training.
This section is intended to provide information that describes the evaluation protocol that was followed, what was measured and the final results obtained.
This provides a summary of the factors that contribute to the environmental impact. An estimation can be generated on ML CO2 Impact.
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
Provide the code needed to use the model.
get started code
Provide the preferred citation (usually a paper).
BibTeX
@misc{name_year_modeltype,
title={Model card title},
author={Lastname, Firstname (and Lastname, Firstname and...)},
year={year},
url={this URL}
}
APA
Author {Lastname, Initial}.(Date). Title. url
This is optional and should address any additional information that the users might require.
This is optional and provides the necessary information about the authors of the model card.
- Written by :
- Contact details :