Training Loss	Epoch	Step	Validation Loss	Wer
3.0151	0.2778	10	3.0286	56.4200
2.9308	0.5556	20	2.9485	48.8615
2.8354	0.8333	30	2.8120	47.7230
2.5887	1.1111	40	2.6141	45.8571
2.3781	1.3889	50	2.3393	44.5604
2.0871	1.6667	60	1.9773	43.1056
1.6094	1.9444	70	1.5469	46.4896
1.2354	2.2222	80	1.1572	56.1037
0.8648	2.5	90	0.9093	35.5787
0.7721	2.7778	100	0.8162	34.0291
0.6662	3.0556	110	0.7634	33.0487
0.6899	3.3333	120	0.7265	31.3093
0.6348	3.6111	130	0.6990	30.3922
0.6225	3.8889	140	0.6766	29.0323
0.5557	4.1667	150	0.6587	27.9886
0.5236	4.4444	160	0.6436	27.7356
0.5057	4.7222	170	0.6299	27.2929
0.5335	5.0	180	0.6176	26.9450
0.5073	5.2778	190	0.6050	26.6603
0.4444	5.5556	200	0.5965	26.2176
0.4432	5.8333	210	0.5872	25.6167
0.4254	6.1111	220	0.5813	25.2688
0.4108	6.3889	230	0.5729	25.1423
0.3889	6.6667	240	0.5672	25.2688
0.381	6.9444	250	0.5624	25.2688
0.3638	7.2222	260	0.5561	24.6679
0.3247	7.5	270	0.5508	24.6047
0.3051	7.7778	280	0.5465	24.4466
0.3347	8.0556	290	0.5405	24.0038
0.2621	8.3333	300	0.5404	23.9722
0.2869	8.6111	310	0.5362	24.3517
0.3108	8.8889	320	0.5306	23.7508
0.2519	9.1667	330	0.5283	24.1303
0.2571	9.4444	340	0.5296	23.9089
0.2308	9.7222	350	0.5297	23.6243
0.2582	10.0	360	0.5256	23.1499
0.1937	10.2778	370	0.5274	22.9285
0.1937	10.5556	380	0.5256	23.5927
0.2092	10.8333	390	0.5276	30.2657
0.1701	11.1111	400	0.5272	23.0550
0.1519	11.3889	410	0.5322	23.3713
0.1466	11.6667	420	0.5307	23.1183
0.1558	11.9444	430	0.5300	23.1499
0.1181	12.2222	440	0.5354	22.9602
0.1225	12.5	450	0.5338	23.5294
0.1308	12.7778	460	0.5337	22.9918
0.1235	13.0556	470	0.5360	23.4662
0.0819	13.3333	480	0.5467	29.6015
0.0805	13.6111	490	0.5518	23.5610
0.0937	13.8889	500	0.5495	23.3713
0.0657	14.1667	510	0.5500	23.0867
0.0616	14.4444	520	0.5599	23.2448
0.0648	14.7222	530	0.5605	23.7824
0.0671	15.0	540	0.5591	23.4662
0.0443	15.2778	550	0.5750	22.9918
0.0472	15.5556	560	0.5701	23.0550
0.0407	15.8333	570	0.5826	23.5610
0.0371	16.1111	580	0.5775	23.7824
0.0276	16.3889	590	0.5823	23.2764
0.035	16.6667	600	0.5821	22.6755
0.0353	16.9444	610	0.5810	23.3713
0.0228	17.2222	620	0.5944	23.6875
0.019	17.5	630	0.5957	23.5294
0.0195	17.7778	640	0.5962	23.0867
0.0196	18.0556	650	0.5968	23.5610
0.0146	18.3333	660	0.5978	23.0867
0.0148	18.6111	670	0.6028	23.5927
0.0147	18.8889	680	0.6033	24.0354
0.012	19.1667	690	0.6062	23.5294
0.0121	19.4444	700	0.6091	23.4345
0.0112	19.7222	710	0.6096	23.7508
0.0121	20.0	720	0.6121	23.3713
0.0093	20.2778	730	0.6150	23.5927
0.0092	20.5556	740	0.6140	23.4978
0.0095	20.8333	750	0.6142	23.2448
0.0091	21.1111	760	0.6177	23.3713
0.0085	21.3889	770	0.6185	23.4978
0.0086	21.6667	780	0.6189	23.4029
0.0086	21.9444	790	0.6201	23.5610
0.0072	22.2222	800	0.6211	23.6243
0.0072	22.5	810	0.6222	23.4662
0.0083	22.7778	820	0.6229	23.4662
0.0073	23.0556	830	0.6233	23.4978
0.0076	23.3333	840	0.6241	23.2132
0.0075	23.6111	850	0.6253	23.5610
0.0065	23.8889	860	0.6261	23.5294
0.0067	24.1667	870	0.6267	23.4662
0.0069	24.4444	880	0.6270	23.4978
0.0077	24.7222	890	0.6272	23.3713
0.0063	25.0	900	0.6276	23.3080
0.0064	25.2778	910	0.6277	23.5610
0.0062	25.5556	920	0.6283	23.5610
0.0065	25.8333	930	0.6285	23.5610
0.0054	26.1111	940	0.6288	23.5610
0.0065	26.3889	950	0.6291	23.5610
0.0062	26.6667	960	0.6294	23.5610
0.0061	26.9444	970	0.6296	23.5294
0.0066	27.2222	980	0.6298	23.5927
0.0062	27.5	990	0.6298	23.5927
0.0063	27.7778	1000	0.6298	23.5927

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1.dev0
Tokenizers 0.19.1

anniev18/checkpoints

checkpoints

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

anniev18/
checkpoints