@@ -64,10 +64,10 @@ A similar tagger called `20230129` has been proposed to moderate this issue.
This tagger shares the same characteristics as `20230111` and differs in three important aspects:
* the number of training epochs was brought from 1,000 to 10,000 to let the original features be largely forgotten,
* the training stage involved more data: 1,200,235 time segments were used instead of 100,000; these data were unbalanced and training was performed with the newly introduced balancing strategy `auto` (see https://gitlab.pasteur.fr/nyx/larvatagger.jl/-/issues/92),
* the training stage involved more data: 1,200,235 time segments were used instead of 100,000; these data were unbalanced and training was performed with class weighting as per the newly introduced balancing strategy `auto` (see https://gitlab.pasteur.fr/nyx/larvatagger.jl/-/issues/92),
* pretraining and training data were drawn from t15 only (as opposed to previous taggers that were pretrained and trained with data from t15 and t5).
Note the last difference was not meant to improve performance, at all. The `20230129` was trained this way to study its performance on t5, and was kept as is after it showed better performance on t5 data, compared to previous taggers trained with t5 data in addition to t15 data.
Note the last difference was not meant to improve performance. The `20230129` was trained this way to study its performance on t5, and was kept as is after it showed better properties (less temporal leakage, fewer hunches and rolls except on stimulus onset) on t5 data.
