What is the main purpose of a model card in machine learning projects?
Think about what helps users understand a model before using it.
Model cards summarize key information about a model, such as its performance, limitations, and intended use cases, helping users make informed decisions.
What output would you expect after running a typical command to generate a model card using a tool like model-card-tool?
model-card-tool generate --model-path ./models/my_model --output ./model_card.md
Focus on what the command is designed to do: generate documentation.
The command generates a model card file summarizing the model details and saves it to the specified output path.
Which configuration snippet correctly defines metadata for a model card including model name, version, and intended use?
Think about common formats used in model card YAML or simple config files.
Option C uses a simple YAML-like key-value format commonly used in model card metadata configuration.
You generated a model card but notice the 'Limitations' section is missing. What is the most likely cause?
Consider what controls the sections included in a generated document.
If the template used to generate the model card lacks a 'Limitations' section, it will not appear in the output.
What is the correct sequence of steps to update a model card after retraining a model?
Think about the logical order from retraining to publishing documentation.
First retrain the model, then evaluate its performance, update the model card with new info, and finally publish the updated card.