learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
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: 3362
training_steps: 67250

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
5.4256	5.0002	184	48.4291	0.4217	0.0942
3.1512	10.0004	368	47.5302	0.5333	0.1349
2.8694	16.0001	552	48.2082	0.5663	0.1433
2.6821	21.0003	736	47.5202	0.5908	0.1634
2.4055	27.0000	920	47.1397	0.6122	0.1869
2.1891	32.0002	1104	47.2364	0.6309	0.2246
2.0604	37.0004	1288	46.8540	0.6547	0.2509
1.9368	43.0001	1472	46.8027	0.6658	0.2750
1.8052	48.0004	1656	46.1119	0.6541	0.3007
1.7275	54.0001	1840	46.1353	0.6916	0.3484
1.5505	59.0003	2024	45.9264	0.7163	0.3985
1.2871	64.0005	2208	47.1247	0.7118	0.4095
1.0962	70.0002	2392	46.4402	0.7424	0.4325
0.9713	75.0004	2576	46.8574	0.7461	0.4597
0.8134	81.0001	2760	45.9537	0.7552	0.4793
0.7063	86.0003	2944	46.2723	0.7667	0.5031
0.608	91.0005	3128	45.9073	0.7684	0.5231
0.509	97.0002	3312	46.6080	0.7731	0.5265
0.4519	102.0004	3496	46.2714	0.7735	0.5294
0.382	108.0001	3680	46.6938	0.7817	0.5483
0.3321	113.0003	3864	45.8621	0.7843	0.5497
0.286	119.0000	4048	45.5074	0.7886	0.5588
0.2557	124.0002	4232	46.3840	0.7915	0.5562
0.2286	129.0004	4416	46.3640	0.7866	0.5568
0.1957	135.0001	4600	46.1961	0.7922	0.5660
0.1775	140.0004	4784	46.0122	0.7969	0.5683
0.1593	146.0001	4968	45.5680	0.7899	0.5628
0.1376	151.0003	5152	45.0703	0.8011	0.5751
0.1289	156.0005	5336	45.0031	0.7998	0.5822
0.1093	162.0002	5520	45.5972	0.8035	0.5810
0.0966	167.0004	5704	44.7798	0.8024	0.5814
0.0874	173.0001	5888	45.1155	0.8033	0.5852
0.0814	178.0003	6072	44.5588	0.7937	0.5806
0.0712	183.0005	6256	45.1706	0.8069	0.5919
0.0645	189.0002	6440	45.4638	0.8071	0.5904
0.0621	194.0004	6624	45.0921	0.8085	0.5908
0.0509	200.0001	6808	44.6064	0.8079	0.5931
0.0504	205.0003	6992	45.4490	0.8089	0.5942
0.0434	211.0000	7176	45.0113	0.8075	0.5887
0.0441	216.0002	7360	45.5112	0.8072	0.5910
0.0435	221.0004	7544	44.3259	0.8096	0.5991
0.0375	227.0001	7728	44.6645	0.8120	0.5979
0.0405	232.0004	7912	45.1229	0.8143	0.6021
0.0387	238.0001	8096	44.5065	0.8149	0.5998
0.031	243.0003	8280	44.9566	0.8160	0.6031
0.0279	248.0005	8464	44.6569	0.8164	0.6032
0.0318	254.0002	8648	44.0304	0.8176	0.6030
0.0273	259.0004	8832	43.9174	0.8174	0.6059
0.0289	265.0001	9016	43.9305	0.8156	0.5995
0.0305	270.0003	9200	43.8282	0.8131	0.6049
0.029	275.0005	9384	43.2818	0.8217	0.6112
0.0395	281.0002	9568	44.6161	0.8168	0.6019
0.0281	286.0004	9752	44.2539	0.8149	0.6077
0.0235	292.0001	9936	43.6689	0.8142	0.6087
0.027	297.0003	10120	43.9773	0.8202	0.6082
0.0196	303.0000	10304	43.9220	0.8226	0.6146
0.0171	308.0002	10488	43.4254	0.8228	0.6153
0.0155	313.0004	10672	44.3738	0.8245	0.6171
0.0166	319.0001	10856	44.2535	0.8235	0.6148
0.0154	324.0004	11040	44.7280	0.8235	0.6141
0.0294	330.0001	11224	44.2243	0.8192	0.6047
0.0192	335.0003	11408	43.5058	0.8243	0.6159
0.014	340.0005	11592	43.6913	0.8263	0.6192
0.0136	346.0002	11776	43.7161	0.8243	0.6197
0.0205	351.0004	11960	44.1256	0.8229	0.6143
0.0147	357.0001	12144	43.9620	0.8218	0.6119
0.0154	362.0003	12328	43.7506	0.8260	0.6187
0.0146	367.0005	12512	43.7509	0.8250	0.6184
0.0134	373.0002	12696	44.0918	0.8272	0.6213
0.0137	378.0004	12880	42.7483	0.8232	0.6140
0.0151	384.0001	13064	43.7117	0.8273	0.6180
0.0196	389.0003	13248	43.8016	0.8268	0.6185
0.0147	395.0000	13432	43.1048	0.8278	0.6231
0.0099	400.0002	13616	43.2094	0.8296	0.6252
0.0088	405.0004	13800	43.8947	0.8281	0.6230
0.0091	411.0001	13984	43.9607	0.8291	0.6230
0.008	416.0004	14168	43.8726	0.8274	0.6217
0.0081	422.0001	14352	44.4993	0.8252	0.6166
0.0106	427.0003	14536	44.4047	0.8208	0.6170
0.0115	432.0005	14720	43.5954	0.8251	0.6246
0.0081	438.0002	14904	44.0508	0.8292	0.6247
0.0076	443.0004	15088	43.9636	0.8300	0.6255
0.0067	449.0001	15272	42.7326	0.8297	0.6269
0.0509	454.0003	15456	42.9371	0.8262	0.6228
0.014	459.0005	15640	43.2689	0.8314	0.6305
0.0062	465.0002	15824	43.5745	0.8317	0.6265
0.0057	470.0004	16008	43.6457	0.8315	0.6284
0.0052	476.0001	16192	43.1430	0.8319	0.6299
0.0052	481.0003	16376	43.7944	0.8323	0.6298
0.0052	487.0000	16560	43.6409	0.8310	0.6259
0.0049	492.0002	16744	44.2669	0.8320	0.6261
0.0047	497.0004	16928	43.2488	0.8312	0.6274
0.0045	503.0001	17112	44.2308	0.8311	0.6262
0.0046	508.0004	17296	44.4540	0.8325	0.6312
0.0042	514.0001	17480	44.2831	0.8313	0.6271
0.0297	519.0003	17664	43.8311	0.8271	0.6212
0.0221	524.0005	17848	43.0160	0.8273	0.6254
0.0045	530.0002	18032	44.1287	0.8323	0.6315
0.0042	535.0004	18216	44.4358	0.8325	0.6329
0.0037	541.0001	18400	43.8077	0.8323	0.6319
0.0034	546.0003	18584	43.6864	0.8327	0.6317
0.0032	551.0005	18768	43.0531	0.8318	0.6324
0.003	557.0002	18952	43.4887	0.8331	0.6327
0.0029	562.0004	19136	43.1932	0.8326	0.6310
0.0028	568.0001	19320	44.2302	0.8330	0.6320
0.0028	573.0003	19504	43.4784	0.8318	0.6308
0.0025	579.0000	19688	43.5830	0.8334	0.6310
0.0058	584.0002	19872	43.7212	0.8291	0.6294
0.0152	589.0004	20056	42.3708	0.8383	0.6405
0.0029	595.0001	20240	43.7145	0.8373	0.6370
0.0025	600.0004	20424	42.9279	0.8381	0.6385
0.0024	606.0001	20608	44.1511	0.8371	0.6366
0.0022	611.0003	20792	44.0055	0.8370	0.6391
0.0021	616.0005	20976	43.2940	0.8372	0.6388
0.002	622.0002	21160	44.6996	0.8366	0.6346
0.0036	627.0004	21344	43.8618	0.8363	0.6379
0.0029	633.0001	21528	44.2043	0.8354	0.6354
0.0174	638.0003	21712	42.9525	0.8330	0.6306
0.004	643.0005	21896	43.2314	0.8361	0.6382
0.0023	649.0002	22080	43.3711	0.8355	0.6358
0.0019	654.0004	22264	44.0582	0.8366	0.6375
0.0018	660.0001	22448	43.3902	0.8369	0.6381
0.0018	665.0003	22632	43.2665	0.8370	0.6385
0.0016	671.0000	22816	43.8846	0.8365	0.6359
0.0015	676.0002	23000	42.3316	0.8374	0.6375
0.0015	681.0004	23184	43.9551	0.8369	0.6372
0.0015	687.0001	23368	43.9373	0.8362	0.6366
0.0015	692.0004	23552	43.3758	0.8354	0.6337
0.002	698.0001	23736	43.6962	0.8371	0.6349

Framework versions

Transformers 4.46.0
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.1

Arthur-Tsai/pretrained-hist-l2_tenKQ_finetune-itemseg_v11

pretrained-hist-l2_tenKQ_finetune-itemseg_v11

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Arthur-Tsai/
pretrained-hist-l2_tenKQ_finetune-itemseg_v11