Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8623	0.03	1000	0.9076	0.5917
0.7528	0.07	2000	0.8587	0.6119
0.7074	0.1	3000	0.7867	0.6647
0.6949	0.14	4000	0.7474	0.6772
0.6681	0.17	5000	0.7661	0.6814
0.6597	0.2	6000	0.7264	0.6943
0.6495	0.24	7000	0.7841	0.6781
0.6323	0.27	8000	0.7256	0.6952
0.6308	0.31	9000	0.7319	0.6958
0.6254	0.34	10000	0.7054	0.7004
0.6233	0.37	11000	0.7069	0.7085
0.6165	0.41	12000	0.6880	0.7181
0.6033	0.44	13000	0.6844	0.7197
0.6014	0.48	14000	0.6753	0.7129
0.5947	0.51	15000	0.7000	0.7039
0.5965	0.54	16000	0.6708	0.7263
0.5979	0.58	17000	0.6562	0.7285
0.5787	0.61	18000	0.6554	0.7297
0.58	0.65	19000	0.6544	0.7315
0.574	0.68	20000	0.6549	0.7339
0.5751	0.71	21000	0.6545	0.7289
0.5659	0.75	22000	0.6467	0.7371
0.5732	0.78	23000	0.6448	0.7362
0.5637	0.82	24000	0.6520	0.7355
0.5648	0.85	25000	0.6412	0.7345
0.5622	0.88	26000	0.6350	0.7358
0.5579	0.92	27000	0.6347	0.7393
0.5518	0.95	28000	0.6417	0.7392
0.5547	0.99	29000	0.6321	0.7437
0.524	1.02	30000	0.6430	0.7412
0.4982	1.05	31000	0.6253	0.7458
0.5002	1.09	32000	0.6316	0.7418
0.4993	1.12	33000	0.6197	0.7487
0.4963	1.15	34000	0.6307	0.7462
0.504	1.19	35000	0.6272	0.7480
0.4922	1.22	36000	0.6410	0.7433
0.5016	1.26	37000	0.6295	0.7461
0.4957	1.29	38000	0.6183	0.7506
0.4883	1.32	39000	0.6261	0.7502
0.4985	1.36	40000	0.6315	0.7496
0.4885	1.39	41000	0.6189	0.7529
0.4909	1.43	42000	0.6189	0.7473
0.4894	1.46	43000	0.6314	0.7433
0.4912	1.49	44000	0.6184	0.7446
0.4851	1.53	45000	0.6258	0.7461
0.4879	1.56	46000	0.6286	0.7480
0.4907	1.6	47000	0.6196	0.7512
0.4884	1.63	48000	0.6157	0.7526
0.4755	1.66	49000	0.6056	0.7591
0.4811	1.7	50000	0.5977	0.7582
0.4787	1.73	51000	0.5915	0.7621
0.4779	1.77	52000	0.6014	0.7583
0.4767	1.8	53000	0.6041	0.7623
0.4737	1.83	54000	0.6093	0.7563
0.4836	1.87	55000	0.6001	0.7568
0.4765	1.9	56000	0.6109	0.7601
0.4776	1.94	57000	0.6046	0.7599
0.4769	1.97	58000	0.5970	0.7568
0.4654	2.0	59000	0.6147	0.7614
0.4144	2.04	60000	0.6439	0.7566
0.4101	2.07	61000	0.6373	0.7527
0.4192	2.11	62000	0.6136	0.7575
0.4128	2.14	63000	0.6283	0.7560
0.4204	2.17	64000	0.6187	0.7625
0.4114	2.21	65000	0.6127	0.7621
0.4097	2.24	66000	0.6188	0.7626
0.4129	2.28	67000	0.6156	0.7639
0.4085	2.31	68000	0.6232	0.7616
0.4074	2.34	69000	0.6240	0.7605
0.409	2.38	70000	0.6153	0.7591
0.4046	2.41	71000	0.6375	0.7587
0.4117	2.45	72000	0.6145	0.7629
0.4002	2.48	73000	0.6279	0.7610
0.4042	2.51	74000	0.6176	0.7646
0.4055	2.55	75000	0.6277	0.7643
0.4021	2.58	76000	0.6196	0.7642
0.4081	2.62	77000	0.6127	0.7659
0.408	2.65	78000	0.6237	0.7638
0.3997	2.68	79000	0.6190	0.7636
0.4093	2.72	80000	0.6152	0.7648
0.4095	2.75	81000	0.6155	0.7627
0.4088	2.79	82000	0.6130	0.7641
0.4063	2.82	83000	0.6072	0.7646
0.3978	2.85	84000	0.6128	0.7662
0.4034	2.89	85000	0.6157	0.7627
0.4044	2.92	86000	0.6127	0.7661
0.403	2.96	87000	0.6126	0.7664
0.4033	2.99	88000	0.6144	0.7662

Framework versions

Transformers 4.12.3
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3

emrecan/bert-base-multilingual-cased-allnli_tr

bert-base-multilingual-cased_allnli_tr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

emrecan/
bert-base-multilingual-cased-allnli_tr