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This produces novel color names and hexadecimal values. It was fine tuned using https://www.kaggle.com/datasets/avi1023/color-names

Model 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]

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  • 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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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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Model Card Authors [optional]

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