Sequence models on the How to Make AI Models track. Sequences have order: words, sensor readings, clicks. The model must know position or it treats the input as a bag. Truncate long sequences on purpose and document the cutoff.
This lesson assumes you already worked through Convolutional models.
The idea in practice
Choose a maximum length. Say what happens to overflow. Pad and mask so pads are not treated as real tokens.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Sequence models',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A model that attends to padding and learns nothing about the text. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Define max length and the mask rule for one text task.
Self-check
- Say Sequence models in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.