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Kyungjin-Kim/
embc25_finetuned_30000_fr_it-ipa

Name: embc25_finetuned_30000_fr_it-ipa
Availability: InStock

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embc25_finetuned_30000_fr_it-ipa

This model is a fine-tuned version of Kyungjin-Kim/mmc_roberta_500000_fr_it-ipa on the None dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.5767	0.5926	500	0.5604	0.7012	0.7010	0.7017	0.7013
0.4796	1.1849	1000	0.5236	0.7397	0.7168	0.7923	0.7527
0.4469	1.7775	1500	0.4888	0.7645	0.7745	0.7463	0.7601
0.3834	2.3698	2000	0.5044	0.7668	0.8163	0.6887	0.7471
0.3875	2.9624	2500	0.4979	0.7733	0.7732	0.7737	0.7734
0.3362	3.5547	3000	0.5508	0.7662	0.7423	0.8153	0.7771
0.26	4.1470	3500	0.5949	0.7625	0.7422	0.8043	0.7720
0.2575	4.7396	4000	0.6632	0.7495	0.7067	0.853	0.7730
0.2026	5.3319	4500	0.7058	0.7632	0.7330	0.828	0.7776
0.2032	5.9244	5000	0.6879	0.7643	0.7489	0.7953	0.7714
0.1713	6.5167	5500	0.7654	0.7578	0.7328	0.8117	0.7702
0.1301	7.1090	6000	0.8759	0.7632	0.7321	0.83	0.7780
0.1292	7.7016	6500	0.8705	0.7667	0.7517	0.7963	0.7734
0.1087	8.2939	7000	0.9325	0.765	0.7461	0.8033	0.7737
0.1035	8.8865	7500	0.9521	0.765	0.7578	0.779	0.7682
0.0913	9.4788	8000	0.9820	0.7653	0.7568	0.782	0.7692