Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Use OptimizerNames.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_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss	F1	Precision	Recall
0.6461	0.2375	100	0.7873	0.5155	0.7772	0.7647	0.7900
0.4988	0.4751	200	0.8014	0.4455	0.7839	0.8012	0.7673
0.4784	0.7126	300	0.7944	0.4508	0.7914	0.7554	0.8310
0.4827	0.9501	400	0.7793	0.4632	0.7836	0.7259	0.8512
0.4628	1.1876	500	0.7944	0.4459	0.7819	0.7787	0.7851
0.4622	1.4252	600	0.7907	0.4591	0.7890	0.7488	0.8338
0.4508	1.6627	700	0.7927	0.4550	0.7875	0.7590	0.8181
0.4572	1.9002	800	0.7903	0.4516	0.7639	0.8104	0.7224
0.4359	2.1378	900	0.7917	0.4772	0.7874	0.7558	0.8217
0.3967	2.3753	1000	0.7984	0.4567	0.7797	0.8003	0.7601
0.415	2.6128	1100	0.7852	0.4792	0.7847	0.7408	0.8342
0.4097	2.8504	1200	0.7965	0.4661	0.7847	0.7795	0.7900
0.3962	3.0879	1300	0.7972	0.4655	0.7738	0.8120	0.7391
0.3738	3.3254	1400	0.7887	0.4740	0.7806	0.7612	0.8011
0.3618	3.5629	1500	0.7935	0.4706	0.7720	0.8015	0.7445
0.3604	3.8005	1600	0.7942	0.4779	0.78	0.7828	0.7772
0.3533	4.0380	1700	0.7892	0.4899	0.7752	0.7760	0.7744
0.3194	4.2755	1800	0.7902	0.5034	0.7785	0.7717	0.7854
0.3285	4.5131	1900	0.7893	0.4958	0.7767	0.7730	0.7804
0.3256	4.7506	2000	0.7908	0.4952	0.7720	0.7905	0.7544
0.321	4.9881	2100	0.7873	0.5050	0.7760	0.7675	0.7847
0.2915	5.2257	2200	0.7872	0.5167	0.7722	0.7761	0.7683
0.2819	5.4632	2300	0.7828	0.5344	0.7745	0.7554	0.7947
0.2839	5.7007	2400	0.7812	0.5529	0.7761	0.7465	0.8082
0.2836	5.9382	2500	0.7771	0.5433	0.7741	0.7384	0.8135
0.2686	6.1758	2600	0.7832	0.5545	0.7692	0.7687	0.7698
0.2559	6.4133	2700	0.7820	0.5578	0.7728	0.7564	0.7900
0.2525	6.6508	2800	0.7837	0.5647	0.7703	0.7678	0.7730
0.251	6.8884	2900	0.7867	0.5588	0.7713	0.7764	0.7662
0.2463	7.1259	3000	0.7877	0.5754	0.7738	0.7739	0.7737
0.2284	7.3634	3100	0.7842	0.5907	0.7758	0.7571	0.7954
0.2295	7.6010	3200	0.7835	0.5832	0.7654	0.7789	0.7523
0.234	7.8385	3300	0.7807	0.5821	0.7670	0.7650	0.7690
0.2296	8.0760	3400	0.7850	0.5823	0.7667	0.7813	0.7527
0.2161	8.3135	3500	0.7837	0.5908	0.7694	0.7699	0.7690
0.2253	8.5511	3600	0.7857	0.5907	0.7648	0.7887	0.7423
0.21	8.7886	3700	0.7835	0.6021	0.7719	0.7636	0.7804
0.2123	9.0261	3800	0.7840	0.6025	0.7691	0.7720	0.7662
0.1977	9.2637	3900	0.7827	0.6081	0.7655	0.7755	0.7559
0.2061	9.5012	4000	0.7838	0.6090	0.7715	0.7656	0.7776
0.2032	9.7387	4100	0.7850	0.6081	0.7718	0.7690	0.7747
0.2077	9.9762	4200	0.7838	0.6084	0.7699	0.7694	0.7705

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1