Model Card for Model ID
This produces novel color names and hexadecimal values. It was fine tuned using
https://www.kaggle.com/datasets/avi1023/color-namesModel Details
The model is great for beginners learning PyTorch, fine tuning, and training a simple model.
Model Description
- Developed by: Seth Hammock
- Funded by [optional]: Seth Hammock
- Shared by [optional]: [More Information Needed]
- Model type: Transformer
- Language(s) (NLP): English
- License: MIT
- Finetuned from model [optional]: GPT2
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
This is a model was created as an exercise in autoregressive language models. Use it as a beginner and train it on a larger datasert to improve its output. The idea is that
you can train it easily using free resources on Google Colab, or train it on a laptop.
Direct Use
Evaluating the model without additional tuning will produce color names with color codes, RGB values, hue degrees, HSL and HSV.
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
The color names don't always align with the colors and at times will produce improperly formed hexadecimal values.
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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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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- Hours used: [More Information Needed]
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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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Glossary [optional]
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