Understand it before memorizing it.
A term matters when it is important locally but not common everywhere.
Where this model earns its place
Text similarity
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Search baseline
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Classical NLP
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Trade-offs matter more than popularity.
Strengths
✓ Simple
✓ Interpretable
✓ Cheap
Limitations
△ Sparse
△ Weak semantics
Metrics to watch
Cosine similarityInterpret with the product objective and error cost.
Retrieval precisionInterpret with the product objective and error cost.
Classifier F1Interpret with the product objective and error cost.
Before it reaches real users
- 01
Normalize text consistently
Document the assumption and instrument the condition so regressions can be detected.
- 02
Watch vocabulary growth
Document the assumption and instrument the condition so regressions can be detected.
- 03
Use sparse matrices
Document the assumption and instrument the condition so regressions can be detected.