learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
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
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Ei Accuracy	Ei F1	Ei Mcc	Sn Accuracy	Sn F1	Sn Mcc	Ft Accuracy	Ft F1	Ft Mcc	Jp Accuracy	Jp F1	Jp Mcc	Order Accuracy
0.1372	0.0797	500	0.1065	0.7782	0.7762	0.5623	0.6522	0.6411	0.3267	0.7363	0.7363	0.4726	0.7271	0.7271	0.4549	0.6865
0.1549	0.1593	1000	0.1146	0.7454	0.7417	0.5115	0.6879	0.6874	0.3769	0.7268	0.7214	0.4684	0.7101	0.7056	0.4284	0.6967
0.1289	0.2390	1500	0.0995	0.7894	0.7891	0.5789	0.6991	0.6991	0.3983	0.7363	0.7327	0.4828	0.7358	0.7318	0.4925	0.7121
0.1179	0.3186	2000	0.0969	0.7959	0.7956	0.5918	0.6631	0.6436	0.3674	0.7226	0.7126	0.4743	0.7420	0.7410	0.4852	0.7173
0.1066	0.3983	2500	0.0946	0.7844	0.7828	0.5823	0.7107	0.7046	0.4393	0.7041	0.6868	0.4550	0.7571	0.7566	0.5145	0.7156
0.1082	0.4779	3000	0.0915	0.7442	0.7411	0.5062	0.6730	0.6537	0.3906	0.7511	0.7499	0.5095	0.6984	0.6850	0.4255	0.7162
0.1013	0.5576	3500	0.0917	0.7912	0.7905	0.5900	0.7018	0.7006	0.4071	0.7611	0.7595	0.5271	0.7538	0.7536	0.5075	0.7105
0.0996	0.6372	4000	0.0873	0.8165	0.8164	0.6328	0.7082	0.7082	0.4164	0.7578	0.7578	0.5156	0.7592	0.7585	0.5239	0.7152
0.0945	0.7169	4500	0.0891	0.8246	0.8240	0.6509	0.6712	0.6569	0.3764	0.7273	0.7171	0.4858	0.7598	0.7596	0.5194	0.7190
0.0993	0.7966	5000	0.0874	0.7742	0.7742	0.5484	0.7097	0.7077	0.4246	0.7491	0.7458	0.5085	0.7392	0.7358	0.4861	0.7197
0.0949	0.8762	5500	0.0846	0.7910	0.7904	0.5883	0.7148	0.7130	0.4347	0.7645	0.7635	0.5358	0.7509	0.7490	0.5059	0.7215
0.0916	0.9559	6000	0.0834	0.8179	0.8151	0.6496	0.7143	0.7133	0.4314	0.7646	0.7646	0.5294	0.7543	0.7533	0.5103	0.7217
0.0916	1.0355	6500	0.0865	0.7677	0.7652	0.5533	0.7016	0.6975	0.4153	0.7320	0.7210	0.4996	0.7523	0.7497	0.5203	0.7233
0.0858	1.1152	7000	0.0873	0.8212	0.8195	0.6494	0.7035	0.7002	0.4167	0.7664	0.7658	0.5343	0.7590	0.7544	0.5455	0.7236
0.0799	1.1948	7500	0.0857	0.8088	0.8078	0.6277	0.7248	0.7239	0.4525	0.7532	0.7476	0.5261	0.7498	0.7431	0.5365	0.7182
0.0776	1.2745	8000	0.0871	0.8137	0.8102	0.6447	0.7250	0.7246	0.4510	0.7092	0.6884	0.4802	0.7660	0.7641	0.5460	0.7170
0.0908	1.3542	8500	0.0852	0.8239	0.8239	0.6478	0.7061	0.7016	0.4259	0.7779	0.7779	0.5569	0.7569	0.7549	0.5185	0.7168
0.082	1.4338	9000	0.0823	0.8297	0.8296	0.6593	0.7183	0.7167	0.4420	0.7700	0.7674	0.5558	0.7760	0.7757	0.5556	0.7175
0.0758	1.5135	9500	0.0813	0.8223	0.8190	0.6623	0.7111	0.7076	0.4336	0.7815	0.7786	0.5752	0.7800	0.7794	0.5609	0.7177
0.0771	1.5931	10000	0.0797	0.8295	0.8281	0.6652	0.7248	0.7227	0.4574	0.7827	0.7817	0.5691	0.7652	0.7621	0.5512	0.7232
0.0778	1.6728	10500	0.0836	0.8008	0.8008	0.6028	0.6927	0.6804	0.4207	0.7854	0.7853	0.5710	0.7138	0.6991	0.4657	0.7168
0.0836	1.7524	11000	0.0797	0.8429	0.8423	0.6877	0.7290	0.7289	0.4587	0.7888	0.7883	0.5790	0.7811	0.7811	0.5627	0.7194
0.0756	1.8321	11500	0.0802	0.8294	0.8292	0.6589	0.6930	0.6832	0.4136	0.7504	0.7408	0.5365	0.7787	0.7763	0.5754	0.7223
0.0738	1.9117	12000	0.0802	0.8213	0.8209	0.6492	0.7353	0.7348	0.4720	0.7825	0.7805	0.5727	0.7783	0.7765	0.5704	0.7201
0.0718	1.9914	12500	0.0784	0.8311	0.8311	0.6621	0.7382	0.7382	0.4764	0.7716	0.7682	0.5567	0.7718	0.7711	0.5450	0.7230
0.0712	2.0711	13000	0.0860	0.8454	0.8453	0.6907	0.7419	0.7418	0.4838	0.7500	0.7399	0.5380	0.7789	0.7761	0.5782	0.7138
0.0649	2.1507	13500	0.0802	0.8271	0.8269	0.6573	0.7409	0.7406	0.4829	0.7922	0.7911	0.5890	0.7837	0.7829	0.5751	0.7103
0.0618	2.2304	14000	0.0820	0.8218	0.8213	0.6507	0.7321	0.7320	0.4642	0.7904	0.7874	0.5942	0.7900	0.7899	0.5798	0.7084
0.066	2.3100	14500	0.0812	0.8190	0.8188	0.6421	0.7268	0.7210	0.4725	0.7820	0.7794	0.5747	0.7915	0.7912	0.5870	0.7143
0.0655	2.3897	15000	0.0788	0.8411	0.8410	0.6821	0.7374	0.7370	0.4769	0.8027	0.8027	0.6054	0.7978	0.7977	0.5980	0.7148
0.0626	2.4693	15500	0.0803	0.8235	0.8234	0.6502	0.7454	0.7449	0.4927	0.8072	0.8067	0.6162	0.7721	0.7692	0.5531	0.7075
0.0687	2.5490	16000	0.0795	0.8483	0.8481	0.6969	0.7452	0.7439	0.4948	0.7882	0.7852	0.5901	0.7900	0.7894	0.5862	0.7108
0.0653	2.6286	16500	0.0817	0.8365	0.8364	0.6751	0.7454	0.7454	0.4909	0.8019	0.8007	0.6090	0.7935	0.7922	0.5988	0.7042
0.0644	2.7083	17000	0.0826	0.8543	0.8541	0.7090	0.7495	0.7483	0.5032	0.7982	0.7968	0.6022	0.7785	0.7759	0.5764	0.7060
0.0605	2.7880	17500	0.0797	0.8575	0.8572	0.7158	0.7473	0.7461	0.4989	0.8083	0.8073	0.6206	0.8004	0.8001	0.6045	0.7089
0.0656	2.8676	18000	0.0781	0.8519	0.8506	0.7115	0.7573	0.7571	0.5154	0.7962	0.7933	0.6061	0.7972	0.7962	0.6047	0.7105
0.0585	2.9473	18500	0.0781	0.8429	0.8429	0.6872	0.7541	0.7541	0.5083	0.8114	0.8104	0.6271	0.8036	0.8029	0.6145	0.7068
0.0533	3.0269	19000	0.0814	0.8602	0.8600	0.7204	0.7505	0.7480	0.5101	0.7785	0.7721	0.5855	0.8022	0.8018	0.6049	0.7010
0.0501	3.1066	19500	0.0810	0.8560	0.8560	0.7126	0.7348	0.7320	0.4806	0.8155	0.8149	0.6338	0.8053	0.8052	0.6107	0.7005
0.0537	3.1862	20000	0.0819	0.8512	0.8511	0.7038	0.7627	0.7614	0.5308	0.8194	0.8194	0.6391	0.8100	0.8100	0.6201	0.6966
0.0543	3.2659	20500	0.0808	0.8622	0.8616	0.7274	0.7607	0.7607	0.5215	0.8157	0.8149	0.6352	0.8035	0.8034	0.6069	0.6990
0.0457	3.3455	21000	0.0816	0.8543	0.8542	0.7087	0.7629	0.7615	0.5313	0.8160	0.8144	0.6408	0.8092	0.8088	0.6242	0.6978
0.0551	3.4252	21500	0.0821	0.8655	0.8649	0.7341	0.7525	0.7519	0.5079	0.8170	0.8156	0.6415	0.8082	0.8082	0.6164	0.6915
0.0548	3.5049	22000	0.0825	0.8594	0.8593	0.7187	0.7611	0.7609	0.5236	0.7895	0.7840	0.6059	0.8092	0.8092	0.6200	0.6940
0.057	3.5845	22500	0.0800	0.8715	0.8712	0.7445	0.7672	0.7671	0.5350	0.8216	0.8205	0.6496	0.8176	0.8175	0.6370	0.6946
0.0535	3.6642	23000	0.0808	0.8639	0.8637	0.7284	0.7488	0.7471	0.5049	0.8027	0.7989	0.6256	0.8013	0.8012	0.6024	0.6978
0.0553	3.7438	23500	0.0807	0.8641	0.8641	0.7282	0.7644	0.7642	0.5297	0.8263	0.8256	0.6555	0.8116	0.8111	0.6301	0.6947
0.0539	3.8235	24000	0.0830	0.8404	0.8399	0.6882	0.7681	0.7677	0.5377	0.8167	0.8148	0.6433	0.8131	0.8130	0.6284	0.6959
0.0514	3.9031	24500	0.0803	0.8690	0.8690	0.7380	0.7675	0.7670	0.5380	0.7991	0.7943	0.6232	0.8153	0.8152	0.6331	0.6926
0.0511	3.9828	25000	0.0811	0.8596	0.8596	0.7202	0.7609	0.7600	0.5261	0.8038	0.7997	0.6294	0.8160	0.8160	0.6319	0.6951
0.0407	4.0625	25500	0.0826	0.8702	0.8699	0.7419	0.7717	0.7706	0.5482	0.8200	0.8180	0.6515	0.8173	0.8173	0.6355	0.6900
0.0409	4.1421	26000	0.0838	0.8660	0.8660	0.7322	0.7679	0.7676	0.5370	0.8014	0.7974	0.6236	0.8155	0.8155	0.6310	0.6872
0.0409	4.2218	26500	0.0841	0.8651	0.8650	0.7303	0.7666	0.7658	0.5374	0.8175	0.8151	0.6486	0.8201	0.8200	0.6431	0.6845
0.0388	4.3014	27000	0.0828	0.8700	0.8700	0.7399	0.7615	0.7606	0.5271	0.8199	0.8180	0.6505	0.8173	0.8171	0.6369	0.6888
0.0385	4.3811	27500	0.0831	0.8623	0.8623	0.7267	0.7762	0.7760	0.5532	0.8283	0.8276	0.6602	0.8230	0.8227	0.6495	0.6878
0.0432	4.4607	28000	0.0855	0.8553	0.8552	0.7146	0.7752	0.7752	0.5507	0.7979	0.7931	0.6211	0.8188	0.8181	0.6470	0.6853
0.0393	4.5404	28500	0.0832	0.8620	0.8620	0.7258	0.7769	0.7768	0.5538	0.8325	0.8320	0.6679	0.8199	0.8199	0.6410	0.6857
0.0421	4.6200	29000	0.0832	0.8652	0.8651	0.7321	0.7729	0.7726	0.5475	0.8337	0.8334	0.6689	0.8208	0.8206	0.6456	0.6843
0.041	4.6997	29500	0.0835	0.8726	0.8725	0.7451	0.7782	0.7781	0.5567	0.8288	0.8276	0.6643	0.8207	0.8207	0.6422	0.6837
0.0424	4.7794	30000	0.0827	0.8762	0.8761	0.7527	0.7767	0.7767	0.5534	0.8307	0.8297	0.6667	0.8228	0.8227	0.6489	0.6853
0.0428	4.8590	30500	0.0831	0.8753	0.8753	0.7505	0.7730	0.7727	0.5483	0.8281	0.8269	0.6635	0.8234	0.8233	0.6499	0.6849
0.04	4.9387	31000	0.0834	0.8745	0.8745	0.7489	0.7764	0.7764	0.5529	0.8251	0.8235	0.6593	0.8231	0.8229	0.6496	0.6844

Framework versions

Transformers 4.47.1
Pytorch 2.3.0+cu121
Datasets 3.2.0
Tokenizers 0.21.0

tiya0825/MBTI-ScoreModel2.0

MBTI-ScoreModel2.0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tiya0825/
MBTI-ScoreModel2.0