learning_rate: 5e-05
train_batch_size: 36
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss	F1
2.0397	1.0	128	1.1359	0.6661
0.9617	2.0	256	0.9647	0.7399
0.6183	3.0	384	1.0818	0.7369
0.2399	4.0	512	1.2012	0.7504
0.144	5.0	640	1.4676	0.7292
0.103	6.0	768	1.5944	0.7329
0.0938	7.0	896	1.7955	0.7481
0.0424	8.0	1024	1.9380	0.7243
0.046	9.0	1152	2.0580	0.7499
0.0339	10.0	1280	1.8773	0.7423
0.0229	11.0	1408	2.0289	0.7640
0.0144	12.0	1536	1.8883	0.7340
0.0211	13.0	1664	2.0626	0.7414
0.036	14.0	1792	2.2439	0.7518
0.0109	15.0	1920	2.3987	0.7413
0.0138	16.0	2048	2.3014	0.7466
0.0198	17.0	2176	2.4039	0.7489
0.0156	18.0	2304	2.8102	0.7382
0.0248	19.0	2432	2.5557	0.7529
0.0277	20.0	2560	2.5543	0.7493
0.0172	21.0	2688	2.4589	0.7461
0.0138	22.0	2816	2.5041	0.7487
0.0053	23.0	2944	2.5957	0.7572
0.0095	24.0	3072	2.7520	0.7450
0.0078	25.0	3200	2.7041	0.7524
0.0148	26.0	3328	2.7179	0.7455
0.0235	27.0	3456	2.9401	0.7517
0.0228	28.0	3584	2.7662	0.7514
0.0093	29.0	3712	2.8684	0.7486
0.0292	30.0	3840	2.8222	0.7553
0.0144	31.0	3968	2.6264	0.7770
0.0074	32.0	4096	2.6289	0.7607
0.0227	33.0	4224	2.8246	0.7636
0.0047	34.0	4352	2.4470	0.7664
0.0125	35.0	4480	2.5086	0.7420
0.0337	36.0	4608	2.6435	0.7424
0.0191	37.0	4736	2.7223	0.7637
0.0225	38.0	4864	2.6102	0.7693
0.0095	39.0	4992	2.6686	0.7816
0.0013	40.0	5120	2.7064	0.7771
0.0012	41.0	5248	2.7012	0.7787
0.0017	42.0	5376	2.7380	0.7797
0.0011	43.0	5504	2.7412	0.7790
0.0007	44.0	5632	2.7403	0.7791
0.001	45.0	5760	2.6960	0.7782
0.001	46.0	5888	2.7245	0.7739
0.0013	47.0	6016	2.7277	0.7795
0.0007	48.0	6144	2.7371	0.7791
0.0004	49.0	6272	2.7430	0.7755
0.0011	50.0	6400	2.7259	0.7815
0.001	51.0	6528	2.7587	0.7786
0.0009	52.0	6656	2.7626	0.7803
0.0009	53.0	6784	2.7580	0.7806
0.0005	54.0	6912	2.7569	0.7804
0.0016	55.0	7040	2.7532	0.7782
0.0006	56.0	7168	2.7573	0.7817
0.0009	57.0	7296	2.7386	0.7790
0.0004	58.0	7424	2.7708	0.7806
0.0015	59.0	7552	2.7834	0.7806
0.0003	60.0	7680	2.7810	0.7809
0.0012	61.0	7808	2.7807	0.7785
0.0006	62.0	7936	2.7704	0.7827
0.0009	63.0	8064	2.7908	0.7827
0.0009	64.0	8192	2.7821	0.7781
0.0006	65.0	8320	2.7978	0.7827
0.0012	66.0	8448	2.7880	0.7804
0.0004	67.0	8576	2.8033	0.7825
0.0005	68.0	8704	2.8128	0.7749
0.0017	69.0	8832	2.8196	0.7821
0.0011	70.0	8960	2.8229	0.7800
0.0003	71.0	9088	2.8190	0.7800
0.001	72.0	9216	2.8253	0.7800
0.0002	73.0	9344	2.8128	0.7811
0.0013	74.0	9472	2.8295	0.7800
0.0009	75.0	9600	2.8273	0.7822
0.0007	76.0	9728	2.8397	0.7787
0.0008	77.0	9856	2.8416	0.7787
0.0008	78.0	9984	2.8112	0.7714
0.0005	79.0	10112	2.8341	0.7715
0.0013	80.0	10240	2.8288	0.7776
0.0011	81.0	10368	2.8894	0.7759
0.0009	82.0	10496	2.8632	0.7751
0.0011	83.0	10624	2.8610	0.7754
0.0005	84.0	10752	2.8867	0.7729
0.0006	85.0	10880	2.8607	0.7836
0.0008	86.0	11008	2.8683	0.7758
0.001	87.0	11136	2.8716	0.7782
0.0004	88.0	11264	2.8688	0.7782
0.0012	89.0	11392	2.8807	0.7764
0.0009	90.0	11520	2.8823	0.7749
0.001	91.0	11648	2.8813	0.7765
0.0	92.0	11776	2.8886	0.7782
0.0008	93.0	11904	2.8883	0.7727
0.0009	94.0	12032	2.8953	0.7710
0.0007	95.0	12160	2.8998	0.7764
0.0003	96.0	12288	2.9062	0.7756
0.0009	97.0	12416	2.9045	0.7748
0.0004	98.0	12544	2.9242	0.7749
0.0008	99.0	12672	2.8354	0.7785
0.0796	100.0	12800	2.5102	0.7457
0.1145	101.0	12928	2.6841	0.7522
0.0296	102.0	13056	2.8246	0.7323
0.0159	103.0	13184	2.7918	0.7340
0.0047	104.0	13312	2.8134	0.7407
0.0006	105.0	13440	2.8223	0.7396
0.001	106.0	13568	2.9223	0.7427
0.0021	107.0	13696	2.9052	0.7454
0.0006	108.0	13824	2.9146	0.7506
0.001	109.0	13952	2.9090	0.7486
0.0007	110.0	14080	2.9166	0.7526
0.0009	111.0	14208	2.9191	0.7466
0.0006	112.0	14336	2.9206	0.7500
0.0007	113.0	14464	2.9245	0.7500
0.0007	114.0	14592	2.9265	0.7521
0.0004	115.0	14720	2.9316	0.7501
0.0006	116.0	14848	2.9349	0.7539
0.0006	117.0	14976	2.9335	0.7519
0.0009	118.0	15104	2.9385	0.7495
0.0006	119.0	15232	2.9471	0.7519
0.0009	120.0	15360	2.9502	0.7493
0.0003	121.0	15488	2.9488	0.7516
0.0004	122.0	15616	2.9575	0.7516
0.001	123.0	15744	2.9512	0.7529
0.0005	124.0	15872	2.9597	0.7516
0.0005	125.0	16000	2.9621	0.7529
0.0009	126.0	16128	2.9661	0.7529
0.0006	127.0	16256	2.9651	0.7509
0.0007	128.0	16384	2.9742	0.7508
0.0007	129.0	16512	2.9781	0.7529
0.0007	130.0	16640	2.9807	0.7505
0.0004	131.0	16768	2.9855	0.7504
0.0007	132.0	16896	2.9804	0.7509
0.0002	133.0	17024	2.9816	0.7561
0.0012	134.0	17152	2.9952	0.7501
0.0007	135.0	17280	2.9941	0.7522
0.0009	136.0	17408	2.9973	0.7537
0.0004	137.0	17536	2.9987	0.7537
0.0014	138.0	17664	3.0021	0.7521
0.0004	139.0	17792	3.0044	0.7521
0.0007	140.0	17920	3.0095	0.7516
0.0004	141.0	18048	3.0137	0.7537
0.0005	142.0	18176	3.0210	0.7539
0.0007	143.0	18304	3.0259	0.7539
0.0004	144.0	18432	3.0249	0.7540
0.0011	145.0	18560	3.0261	0.7517
0.0006	146.0	18688	3.0299	0.7536
0.0009	147.0	18816	3.0334	0.7540
0.0002	148.0	18944	3.0421	0.7576
0.0011	149.0	19072	3.0379	0.7576
0.0007	150.0	19200	3.0405	0.7555
0.0006	151.0	19328	3.0509	0.7556
0.0009	152.0	19456	3.0489	0.7538
0.0004	153.0	19584	3.0532	0.7559
0.0009	154.0	19712	3.0591	0.7535
0.0006	155.0	19840	3.0563	0.7535
0.0007	156.0	19968	3.0635	0.7535
0.0004	157.0	20096	3.0679	0.7535
0.0009	158.0	20224	3.0686	0.7538
0.0004	159.0	20352	3.0719	0.7535
0.0005	160.0	20480	3.0798	0.7556
0.0004	161.0	20608	3.0773	0.7538
0.0009	162.0	20736	3.0802	0.7538
0.0008	163.0	20864	3.0832	0.7538
0.0002	164.0	20992	3.0835	0.7538
0.0006	165.0	21120	3.0912	0.7538
0.0009	166.0	21248	3.0921	0.7519
0.0004	167.0	21376	3.0970	0.7538
0.0005	168.0	21504	3.0997	0.7538
0.0008	169.0	21632	3.1082	0.7538
0.0006	170.0	21760	3.1084	0.7538
0.0002	171.0	21888	3.1156	0.7535
0.0004	172.0	22016	3.1164	0.7538
0.0006	173.0	22144	3.1149	0.7559
0.0009	174.0	22272	3.1236	0.7522
0.0008	175.0	22400	3.1219	0.7538
0.0008	176.0	22528	3.1236	0.7522
0.0004	177.0	22656	3.1242	0.7538
0.0008	178.0	22784	3.1230	0.7538
0.0005	179.0	22912	3.1316	0.7538
0.0002	180.0	23040	3.1308	0.7538
0.0006	181.0	23168	3.1302	0.7538
0.0012	182.0	23296	3.1332	0.7538
0.0008	183.0	23424	3.1409	0.7538
0.0006	184.0	23552	3.1350	0.7538
0.0004	185.0	23680	3.1352	0.7538
0.0008	186.0	23808	3.1401	0.7538
0.0006	187.0	23936	3.1409	0.7538
0.0006	188.0	24064	3.1387	0.7538
0.0004	189.0	24192	3.1466	0.7538
0.0004	190.0	24320	3.1518	0.7538
0.0006	191.0	24448	3.1505	0.7538
0.0008	192.0	24576	3.1482	0.7538
0.0008	193.0	24704	3.1458	0.7522
0.0004	194.0	24832	3.1473	0.7538
0.0006	195.0	24960	3.1493	0.7538
0.0012	196.0	25088	3.1480	0.7538
0.0006	197.0	25216	3.1499	0.7538
0.0008	198.0	25344	3.1504	0.7538
0.0006	199.0	25472	3.1493	0.7538
0.0006	200.0	25600	3.1500	0.7538

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.1.0
Tokenizers 0.21.0

BenPhan/ST1_modernbert-base_product-category_V1

ST1_modernbert-base_product-category_V1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BenPhan/
ST1_modernbert-base_product-category_V1