Psychedelic-Trip-Report-Generator

Model Card for Model ID

<!-- This model is trained to produce psychedelic trip reports.-->

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

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• Developed by: [Jordan Loewen-Colón in partnership with Queen's University and McGill University]
• Shared by [optional]: [More Information Needed]
• Model type: [GPT-2]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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• Repository: [https://github.com/jbloewencolon/Psychedelic-Trip-Generator/tree/main]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [https://huggingface.co/spaces/Legacy7070/Psychedelic-Trip-Report-Generator]

Uses

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Direct Use

<!-- This model can be used to create data-augmented psychedelic trip reports. -->

[More Information Needed]

Downstream Use [optional]

<!-- This model can be used to produce reports that will supplement psychedelic science research, specifically focusing on the qualitative and experiential elements involved in taking psychedelic substances. -->

[More Information Needed]

Out-of-Scope Use

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[More Information Needed]

Bias, Risks, and Limitations

<!-- Limited processing power meant the full dataset was not utilized. -->

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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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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 -->

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 presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
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• Carbon Emitted: [More Information Needed]

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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