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Trial timestamp (UTC): 2025-10-08 10:59:46
| Hyperparameter | Value |
|---|---|
| LR | 2e-05 |
| EPOCHS | 3 |
| MAX_LENGTH | 256 |
| USE_BIO | True |
| USE_LANG_TOKEN | False |
| GATED_BIO | True |
| FOCAL_LOSS | True |
| FOCAL_GAMMA | 1.5 |
| USE_SAMPLER | True |
| R_DROP | True |
| R_KL_ALPHA | 1.0 |
| Metric | Value |
|---|---|
| trial | 0 |
| cfg | {'LR': 2e-05, 'EPOCHS': 3.0, 'MAX_LENGTH': 256.0, 'USE_BIO': 1.0, 'USE_LANG_TOKEN': 0.0, 'GATED_BIO': 1.0, 'FOCAL_LOSS': 1.0, 'FOCAL_GAMMA': 1.5, 'USE_SAMPLER': 1.0, 'R_DROP': 1.0, 'R_KL_ALPHA': 1.0} |
| f1_macro_dev | 0.83319335572975 |
| best_threshold_dev | 0.7000000000000001 |
| precision_macro_dev | 0.8451690821256039 |
| recall_macro_dev | 0.8226024923243633 |
train_en.csv, train_it.csv, train_es.csv with 15% stratified Dev).1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2tok = AutoTokenizer.from_pretrained("SimoneAstarita/Pride-large-try-sweep-20251008-105946-t00")
3model = AutoModelForSequenceClassification.from_pretrained("SimoneAstarita/Pride-large-try-sweep-20251008-105946-t00")