Training Loss	Epoch	Step	Validation Loss	Wer
194.7445	11.11	200	81.6865	1.0
61.9115	22.22	400	39.3596	1.0
22.518	33.32	600	9.0332	1.0
6.0776	44.43	800	5.0634	1.0
4.555	55.54	1000	4.1552	1.0
2.402	66.65	1200	2.1760	0.7746
0.8349	77.76	1400	2.0442	0.8326
0.5484	88.86	1600	1.4219	0.8415
0.4242	99.97	1800	3.1197	0.8817
0.3173	111.11	2000	1.7508	0.7388
0.1989	122.22	2200	2.4075	0.6920
0.1169	133.32	2400	6.3769	0.5670
0.0925	144.43	2600	1.6440	0.4710
0.0681	155.54	2800	1.4864	0.3103
0.0561	166.65	3000	2.2973	0.3996
0.0445	177.76	3200	1.8107	0.4219
0.0336	188.86	3400	1.3867	0.3728
0.0344	199.97	3600	1.7830	0.3281
0.0345	211.11	3800	2.3773	0.3638
0.0304	222.22	4000	1.4448	0.1987
0.0357	233.32	4200	2.5893	0.3125
0.0253	244.43	4400	2.4619	0.3013
0.0212	255.54	4600	2.8144	0.2790
0.0186	266.65	4800	2.1155	0.2545
0.0196	277.76	5000	1.7306	0.2254
0.0153	288.86	5200	1.6247	0.2121
0.0146	299.97	5400	3.0580	0.4442
0.0296	311.11	5600	1.7865	0.2857
0.017	322.22	5800	5.4352	0.3795
0.0162	333.32	6000	1.9186	0.25
0.0139	344.43	6200	2.6566	0.2589
0.0139	355.54	6400	2.6532	0.2946
0.0111	366.65	6600	1.9131	0.2567
0.0119	377.76	6800	1.8914	0.3214
0.012	388.86	7000	2.2985	0.3371
0.0117	399.97	7200	3.3127	0.3393
0.0185	411.11	7400	3.1641	0.3862
0.0096	422.22	7600	2.3704	0.3973
0.007	433.32	7800	5.5839	0.4375
0.0104	444.43	8000	3.1441	0.3973
0.0098	455.54	8200	1.8188	0.2768
0.009	466.65	8400	1.8589	0.3058
0.0142	477.76	8600	3.9817	0.3772
0.0095	488.86	8800	2.1353	0.3237
0.0071	499.97	9000	1.5266	0.2902
0.0071	511.11	9200	1.4713	0.2746
0.0068	522.22	9400	2.2041	0.3125
0.0046	533.32	9600	1.4471	0.2522
0.0078	544.43	9800	1.6511	0.2946
0.0077	555.54	10000	1.7329	0.2121
0.004	566.65	10200	1.8652	0.2031
0.0049	577.76	10400	1.3661	0.2210
0.0066	588.86	10600	1.7544	0.2321
0.0072	599.97	10800	1.8081	0.2835
0.0055	611.11	11000	1.5139	0.2232
0.0053	622.22	11200	1.6138	0.2991
0.0052	633.32	11400	1.4865	0.2924
0.0067	644.43	11600	2.4807	0.3705
0.0044	655.54	11800	1.4097	0.3371
0.0026	666.65	12000	1.5313	0.3348
0.0055	677.76	12200	2.1968	0.3661
0.0034	688.86	12400	1.5198	0.3839
0.0028	699.97	12600	1.5379	0.3683
0.0033	711.11	12800	2.1355	0.3571
0.0044	722.22	13000	1.4440	0.3371
0.0024	733.32	13200	3.5154	0.3438
0.0012	744.43	13400	2.8505	0.3214
0.002	755.54	13600	2.9340	0.3304
0.0029	766.65	13800	2.8148	0.3214
0.0034	777.76	14000	2.7587	0.2835
0.0025	788.86	14200	2.8232	0.3638
0.0012	799.97	14400	2.6047	0.375
0.0015	811.11	14600	2.6364	0.3772
0.0018	822.22	14800	2.5143	0.3929
0.0032	833.32	15000	2.9826	0.5469
0.0035	844.43	15200	1.5761	0.4420
0.0023	855.54	15400	1.7465	0.4598
0.0016	866.65	15600	1.7740	0.4397
0.0007	877.76	15800	1.8296	0.4286
0.0012	888.86	16000	2.2368	0.3906
0.0027	899.97	16200	1.7112	0.3527
0.0009	911.11	16400	2.5084	0.375
0.001	922.22	16600	2.3311	0.3304
0.0019	933.32	16800	1.6653	0.3080
0.0018	944.43	17000	1.4620	0.2768
0.0007	955.54	17200	1.8509	0.2723
0.0005	966.65	17400	1.9279	0.2879
0.0009	977.76	17600	2.3558	0.2812
0.0004	988.86	17800	2.8907	0.2924
0.0011	999.97	18000	2.4847	0.2902
0.0016	1011.11	18200	2.2670	0.3058
0.0004	1022.22	18400	2.2399	0.3125
0.0004	1033.32	18600	2.4376	0.3192
0.001	1044.43	18800	2.4744	0.3214
0.0006	1055.54	19000	2.4975	0.3147
0.0006	1066.65	19200	2.6372	0.3259
0.0005	1077.76	19400	2.5817	0.3304
0.0004	1088.86	19600	2.5573	0.3326
0.0001	1099.97	19800	2.5579	0.3348
0.0003	1111.11	20000	2.5641	0.3326

Framework versions

Transformers 4.26.1
Pytorch 2.0.1+cu118
Datasets 2.11.0
Tokenizers 0.13.3

slplab/apac_5sents_XLS-R_2_e-4

apac_5sents_XLS-R_2_e-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

slplab/
apac_5sents_XLS-R_2_e-4