NLP / ML
Text classification at scale
Classifying text across large datasets.
- Tools
- [Models, libraries, infrastructure]
- Year
- [Year]
- Role
- [Your role]
Summary
[Lead with the result for a non-specialist reader: what the model made possible, against what baseline.]
Results
[Start with a table a founder can read in seconds, then precision/recall for engineers. For example:]
| Top-ranked share | Relevant items captured | Baseline |
|---|---|---|
| [Top 10%] | [–%] | [–%] |
| [Top 20%] | [–%] | [–%] |
Context
[The dataset (size, source, labels) and why classifying it mattered.]
Approach
[Models and features you tried, how you evaluated them, and the trade-offs that decided it.]
Implementation
[Pipeline, training, and serving details worth showing.]
Lessons
[What surprised you, what you'd change.]