Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3625	0.26	1000	0.3300	0.7651	0.7809	0.7729	0.8964
0.3099	0.51	2000	0.3070	0.7708	0.8041	0.7871	0.9002
0.2954	0.77	3000	0.2962	0.7793	0.8036	0.7913	0.9041
0.283	1.03	4000	0.2958	0.7843	0.8153	0.7995	0.9066
0.265	1.29	5000	0.2873	0.7930	0.8065	0.7997	0.9069
0.2613	1.54	6000	0.2838	0.7789	0.8289	0.8031	0.9092
0.2635	1.8	7000	0.2790	0.7902	0.8252	0.8073	0.9088
0.2574	2.06	8000	0.2946	0.7887	0.8345	0.8110	0.9098
0.2355	2.31	9000	0.2859	0.7975	0.8152	0.8063	0.9105
0.2361	2.57	10000	0.2806	0.7883	0.8313	0.8092	0.9104
0.2361	2.83	11000	0.2805	0.7931	0.8279	0.8101	0.9123
0.2268	3.08	12000	0.2934	0.7959	0.8323	0.8137	0.9130
0.2106	3.34	13000	0.2862	0.7934	0.8311	0.8118	0.9121
0.2106	3.6	14000	0.2876	0.8009	0.8332	0.8167	0.9143
0.2131	3.86	15000	0.2777	0.8015	0.8242	0.8127	0.9123
0.1993	4.11	16000	0.2999	0.7920	0.8311	0.8111	0.9113
0.1872	4.37	17000	0.2984	0.8003	0.8365	0.8180	0.9143
0.1861	4.63	18000	0.2894	0.7976	0.8321	0.8145	0.9151
0.1916	4.88	19000	0.2909	0.7958	0.8300	0.8125	0.9143
0.1745	5.14	20000	0.3075	0.7906	0.8386	0.8139	0.9136
0.1649	5.4	21000	0.2986	0.8055	0.8199	0.8127	0.9147
0.1678	5.66	22000	0.3043	0.7988	0.8303	0.8142	0.9147
0.1688	5.91	23000	0.2950	0.8026	0.8269	0.8146	0.9155
0.153	6.17	24000	0.3231	0.7995	0.8305	0.8147	0.9150
0.1468	6.43	25000	0.3145	0.7954	0.8326	0.8136	0.9156
0.1478	6.68	26000	0.3222	0.8034	0.8307	0.8168	0.9160
0.1489	6.94	27000	0.3184	0.8019	0.8318	0.8166	0.9161
0.1311	7.2	28000	0.3336	0.8022	0.8278	0.8148	0.9168
0.1298	7.46	29000	0.3430	0.8050	0.8281	0.8164	0.9164
0.1319	7.71	30000	0.3374	0.8005	0.8257	0.8129	0.9152
0.1312	7.97	31000	0.3320	0.8019	0.8353	0.8183	0.9173
0.1144	8.23	32000	0.3539	0.8007	0.8309	0.8155	0.9160
0.1132	8.48	33000	0.3581	0.7940	0.8376	0.8152	0.9158
0.1159	8.74	34000	0.3566	0.8032	0.8355	0.8191	0.9182
0.117	9.0	35000	0.3384	0.8113	0.8205	0.8159	0.9166
0.0996	9.25	36000	0.3637	0.8060	0.8256	0.8156	0.9166
0.1004	9.51	37000	0.3687	0.8043	0.8147	0.8095	0.9152
0.1015	9.77	38000	0.3715	0.8017	0.8359	0.8185	0.9173
0.1001	10.03	39000	0.3826	0.8047	0.8288	0.8166	0.9174
0.0874	10.28	40000	0.3857	0.8087	0.8231	0.8158	0.9168
0.0892	10.54	41000	0.3817	0.8069	0.8221	0.8145	0.9165
0.0895	10.8	42000	0.3800	0.8107	0.8291	0.8198	0.9183
0.0868	11.05	43000	0.4099	0.8032	0.8297	0.8162	0.9177
0.0777	11.31	44000	0.4099	0.8059	0.8255	0.8156	0.9170
0.0781	11.57	45000	0.4077	0.8044	0.8335	0.8187	0.9186
0.0779	11.83	46000	0.4172	0.8050	0.8243	0.8145	0.9161
0.0759	12.08	47000	0.4230	0.8034	0.8244	0.8138	0.9158
0.0691	12.34	48000	0.4286	0.8048	0.8221	0.8134	0.9162
0.0676	12.6	49000	0.4251	0.8091	0.8287	0.8188	0.9185
0.0695	12.85	50000	0.4289	0.8043	0.8284	0.8161	0.9168
0.0663	13.11	51000	0.4431	0.8060	0.8246	0.8152	0.9168
0.0618	13.37	52000	0.4484	0.8054	0.8214	0.8133	0.9162
0.0614	13.62	53000	0.4421	0.8044	0.8230	0.8136	0.9166
0.0611	13.88	54000	0.4468	0.8066	0.8231	0.8148	0.9166
0.0582	14.14	55000	0.4606	0.8055	0.8244	0.8148	0.9173
0.0552	14.4	56000	0.4642	0.8055	0.8274	0.8163	0.9175
0.0553	14.65	57000	0.4633	0.8083	0.8248	0.8165	0.9175
0.0556	14.91	58000	0.4625	0.8060	0.8246	0.8152	0.9173

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

livinNector/mBERT-naamapdam-fine-tuned

mBERT-naamapdam-fine-tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

livinNector/
mBERT-naamapdam-fine-tuned