Jev vs a 310M encoder I trained myself: 750 rows, three tasks, two different winners
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TL;DR : I ran three Japanese classification tasks (250 rows each, same gold labels) through six systems. A 310M encoder I fine-tuned on 250 labels beat TypeSafe's Jev on topic classification by +12.0 points (McNemar p=0.00007) and was 4–20× faster — but on the two polarity tasks it only tied Jev. T…
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- 2026-09-21 02:20 · DEV Community — Machine Learning
Jev vs a 310M encoder I trained myself: 750 rows, three tasks, two different winners