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How to benchmark your model on CoLA against GPT-1 with Test Model Thing

Run the included CoLA benchmark on your trained model and compare the score to GPT-1's 45.4.

  • Research
  • Advanced
  • 1 hour 30 minutes
  • From the README
  • Any agent

Who it is forA researcher or student who wants a measurable score for a small experimental model and a known reference point.

The problemA proof of concept needs a concrete number to show whether training changes are helping.

The outcomeYou get a CoLA score for your model that you can compare with the README's GPT-1 figure of 45.4.

What you need first

  • Python with a virtual environment (the README says to initialize a venv)
  • Apple Silicon Mac, or Linux with a GPU or CPU, or WSL (the README says it trains on WSL)
  • A training dataset, since model weights are not provided and dataset mode must be configured by you
  • Time for training, since the README's examples used 5 minutes to about 12 hours

Steps

  1. Train a model first so that weights exist, using python main.py train.
  2. Run the benchmark with python benchmark.py.
  3. Record the CoLA score and the training time it took to reach that score.
  4. Compare the score with the README's GPT-1 score of 45.4 and note the difference.

Prompt for your agent

In the jrz97619761/test-model-thing repo, with trained weights present, run `python benchmark.py` and report the CoLA score. Verify that the score is reported next to the README's GPT-1 figure of 45.4.

Caveat. The README reports a 4.5M-parameter model trained for about 30 minutes on CoLA, and it is a small experiment, not a finished model, so scores are not directly comparable to large models.

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