'microsoft/deberta-v3-base'
training_args = TrainingArguments( output_dir='ECO_DEBERTA', evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=32, weight_decay=0.01, save_strategy="epoch", load_best_model_at_end=True, push_to_hub=True )
Epoch Training Loss Validation Loss Precision Recall F1 Accuracy 1 No log 0.079086 0.264007 0.159103 0.198550 0.982315 2 0.153400 0.055790 0.402147 0.354133 0.376616 0.985904 3 0.153400 0.055938 0.377627 0.436791 0.405060 0.985536 4 0.037400 0.059241 0.424993 0.426256 0.425624 0.986040 5 0.037400 0.066712 0.436903 0.457320 0.446879 0.986067 6 0.021100 0.064148 0.422239 0.465694 0.442903 0.986155 7 0.021100 0.069515 0.460089 0.474878 0.467367 0.986865 8 0.012900 0.073564 0.458955 0.465154 0.462034 0.986700 9 0.012900 0.081422 0.452289 0.472447 0.462148 0.986066 10 0.008500 0.082762 0.452456 0.467855 0.460027 0.986476 11 0.008500 0.085812 0.458534 0.462993 0.460753 0.986490 12 0.005900 0.086245 0.470666 0.481091 0.475822 0.986883 13 0.005900 0.089477 0.479507 0.483522 0.481506 0.986921 14 0.004300 0.093831 0.474394 0.465424 0.469866 0.986814 15 0.004300 0.096122 0.487333 0.483252 0.485284 0.987021 16 0.003300 0.096951 0.492196 0.494057 0.493125 0.987023 17 0.003300 0.093057 0.480755 0.509454 0.494689 0.987118 18 0.002700 0.099559 0.507381 0.501351 0.504348 0.987200 19 0.002700 0.102917 0.498771 0.493247 0.495993 0.986986 20 0.002200 0.099864 0.503277 0.497839 0.500543 0.987309 21 0.002200 0.101206 0.500547 0.494327 0.497418 0.987205 22 0.001900 0.103037 0.490170 0.491626 0.490897 0.987013 23 0.001900 0.103360 0.493261 0.494327 0.493794 0.987143 24 0.001600 0.107981 0.505051 0.499730 0.502376 0.987058 25 0.001600 0.108147 0.511440 0.495138 0.503157 0.987289 26 0.001400 0.111687 0.507705 0.498379 0.502999 0.987246 27 0.001400 0.111873 0.502892 0.493247 0.498023 0.986916 28 0.001200 0.111417 0.506169 0.498649 0.502381 0.987219 29 0.001200 0.111508 0.509287 0.496218 0.502668 0.987453 30 0.001100 0.112689 0.514325 0.499460 0.506784 0.987268 31 0.001100 0.113233 0.508647 0.500540 0.504561 0.987196 32 0.001000 0.113873 0.510779 0.499190 0.504918 0.987244
