From 271d1c9ed1f86fa1fce0c6e409590a8184a9f43a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=B7=D1=83=D1=80=20=D0=93=D1=80=D0=B5=D1=82?= =?UTF-8?q?=D0=B0=20=D0=95=D0=B2=D0=B3=D0=B5=D0=BD=D1=8C=D0=B5=D0=B2=D0=BD?= =?UTF-8?q?=D0=B0?= <gemazur_1@edu.hse.ru> Date: Thu, 27 Mar 2025 13:13:27 +0300 Subject: [PATCH] supermega --- .ipynb_checkpoints/ULTRAMegaOB-checkpoint.py | 12 ++++++------ ULTRAMegaOB.py | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/.ipynb_checkpoints/ULTRAMegaOB-checkpoint.py b/.ipynb_checkpoints/ULTRAMegaOB-checkpoint.py index e6d7bd7..1d00d46 100644 --- a/.ipynb_checkpoints/ULTRAMegaOB-checkpoint.py +++ b/.ipynb_checkpoints/ULTRAMegaOB-checkpoint.py @@ -141,10 +141,10 @@ def compute_metrics(p): "support": attack_report[attack_type]["support"] } metrics = { - 'accuracy': safety_report["accuracy"], - 'f1': safety_report["weighted avg"]["f1-score"], - 'unsafe_recall': safety_report["unsafe"]["recall"], # Без eval_ префикса - 'safe_precision': safety_report["safe"]["precision"], + 'eval_accuracy': safety_report["accuracy"], + 'eval_f1': safety_report["weighted avg"]["f1-score"], + 'eval_unsafe_recall': safety_report["unsafe"]["recall"], # Добавляем eval_ префикс + 'eval_safe_precision': safety_report["safe"]["precision"], } # Добавляем метрики для атак (только если есть unsafe примеры) @@ -553,7 +553,7 @@ def train_model(): logging_steps=100, save_total_limit=2, # load_best_model_at_end=True, - metric_for_best_model="unsafe_recall", + # metric_for_best_model="unsafe_recall", # greater_is_better=True, fp16=True, # Принудительное использование mixed precision fp16_full_eval=True, @@ -561,7 +561,7 @@ def train_model(): report_to="none", seed=Config.SEED, max_grad_norm=1.0, - # metric_for_best_model="eval_unsafe_recall", + metric_for_best_model="eval_unsafe_recall", greater_is_better=True, load_best_model_at_end=True, ) diff --git a/ULTRAMegaOB.py b/ULTRAMegaOB.py index e6d7bd7..1d00d46 100644 --- a/ULTRAMegaOB.py +++ b/ULTRAMegaOB.py @@ -141,10 +141,10 @@ def compute_metrics(p): "support": attack_report[attack_type]["support"] } metrics = { - 'accuracy': safety_report["accuracy"], - 'f1': safety_report["weighted avg"]["f1-score"], - 'unsafe_recall': safety_report["unsafe"]["recall"], # Без eval_ префикса - 'safe_precision': safety_report["safe"]["precision"], + 'eval_accuracy': safety_report["accuracy"], + 'eval_f1': safety_report["weighted avg"]["f1-score"], + 'eval_unsafe_recall': safety_report["unsafe"]["recall"], # Добавляем eval_ префикс + 'eval_safe_precision': safety_report["safe"]["precision"], } # Добавляем метрики для атак (только если есть unsafe примеры) @@ -553,7 +553,7 @@ def train_model(): logging_steps=100, save_total_limit=2, # load_best_model_at_end=True, - metric_for_best_model="unsafe_recall", + # metric_for_best_model="unsafe_recall", # greater_is_better=True, fp16=True, # Принудительное использование mixed precision fp16_full_eval=True, @@ -561,7 +561,7 @@ def train_model(): report_to="none", seed=Config.SEED, max_grad_norm=1.0, - # metric_for_best_model="eval_unsafe_recall", + metric_for_best_model="eval_unsafe_recall", greater_is_better=True, load_best_model_at_end=True, ) -- GitLab