Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.24	50	4.4161	0.0180	0.0182	0.0180	0.0106	0.1504
No log	0.48	100	4.2802	0.0375	0.0091	0.0375	0.0074	0.3082
No log	0.72	150	3.9834	0.0502	0.0086	0.0502	0.0109	0.3226
No log	0.96	200	3.7286	0.0569	0.0236	0.0569	0.0159	0.3276
4.2271	1.2	250	3.4426	0.0891	0.0270	0.0891	0.0322	0.3596
4.2271	1.44	300	3.2540	0.1169	0.0601	0.1169	0.0604	0.3788
4.2271	1.68	350	3.1869	0.1176	0.0721	0.1176	0.0598	0.3683
4.2271	1.92	400	2.8711	0.1618	0.1145	0.1618	0.0984	0.4101
3.3668	2.16	450	2.6606	0.2644	0.1518	0.2644	0.1626	0.4816
3.3668	2.4	500	2.3190	0.3670	0.2659	0.3670	0.2721	0.5538
3.3668	2.63	550	2.0561	0.4120	0.3507	0.4120	0.3239	0.5857
3.3668	2.87	600	1.8485	0.4764	0.4155	0.4764	0.4052	0.6330
2.5092	3.11	650	1.7040	0.5296	0.4975	0.5296	0.4731	0.6697
2.5092	3.35	700	1.4804	0.5970	0.5614	0.5970	0.5443	0.7167
2.5092	3.59	750	1.3268	0.6434	0.6271	0.6434	0.6047	0.7488
2.5092	3.83	800	1.2244	0.6749	0.6423	0.6749	0.6342	0.7728
1.771	4.07	850	1.0787	0.7348	0.7587	0.7348	0.7168	0.8142
1.771	4.31	900	1.0527	0.7281	0.7380	0.7281	0.7070	0.8097
1.771	4.55	950	0.9342	0.7596	0.7759	0.7596	0.7454	0.8314
1.771	4.79	1000	0.8399	0.7880	0.7986	0.7880	0.7766	0.8507
1.3767	5.03	1050	0.8286	0.7970	0.8035	0.7970	0.7883	0.8575
1.3767	5.27	1100	0.8207	0.7888	0.8016	0.7888	0.7823	0.8524
1.3767	5.51	1150	0.7596	0.8112	0.8180	0.8112	0.8033	0.8690
1.3767	5.75	1200	0.7087	0.8067	0.8139	0.8067	0.8007	0.8658
1.3767	5.99	1250	0.7088	0.8045	0.8178	0.8045	0.7991	0.8637
1.1079	6.23	1300	0.7062	0.8150	0.8256	0.8150	0.8101	0.8698
1.1079	6.47	1350	0.6382	0.8285	0.8385	0.8285	0.8272	0.8810
1.1079	6.71	1400	0.6746	0.8240	0.8386	0.8240	0.8209	0.8783
1.1079	6.95	1450	0.6312	0.8367	0.8523	0.8367	0.8347	0.8867
0.9652	7.19	1500	0.6707	0.8255	0.8438	0.8255	0.8215	0.8775
0.9652	7.43	1550	0.6126	0.8479	0.8578	0.8479	0.8449	0.8942
0.9652	7.66	1600	0.6500	0.8427	0.8528	0.8427	0.8397	0.8912
0.9652	7.9	1650	0.6272	0.8412	0.8512	0.8412	0.8375	0.8885
0.8436	8.14	1700	0.6499	0.8509	0.8630	0.8509	0.8470	0.8970
0.8436	8.38	1750	0.6836	0.8337	0.8423	0.8337	0.8294	0.8841
0.8436	8.62	1800	0.6261	0.8487	0.8614	0.8487	0.8478	0.8951
0.8436	8.86	1850	0.5969	0.8584	0.8631	0.8584	0.8555	0.9019
0.7658	9.1	1900	0.6646	0.8397	0.8561	0.8397	0.8357	0.8872
0.7658	9.34	1950	0.5753	0.8644	0.8715	0.8644	0.8624	0.9049
0.7658	9.58	2000	0.6675	0.8404	0.8511	0.8404	0.8365	0.8885
0.7658	9.82	2050	0.6864	0.8360	0.8479	0.8360	0.8319	0.8859
0.6854	10.06	2100	0.6580	0.8479	0.8599	0.8479	0.8435	0.8948
0.6854	10.3	2150	0.6755	0.8509	0.8627	0.8509	0.8487	0.8963
0.6854	10.54	2200	0.6949	0.8524	0.8625	0.8524	0.8499	0.8969
0.6854	10.78	2250	0.7240	0.8434	0.8511	0.8434	0.8411	0.8905
0.6444	11.02	2300	0.6266	0.8502	0.8607	0.8502	0.8462	0.8950
0.6444	11.26	2350	0.6061	0.8674	0.8795	0.8674	0.8647	0.9073
0.6444	11.5	2400	0.6550	0.8509	0.8616	0.8509	0.8477	0.8955
0.6444	11.74	2450	0.6460	0.8457	0.8553	0.8457	0.8441	0.8913
0.6444	11.98	2500	0.5699	0.8577	0.8679	0.8577	0.8572	0.9010
0.6038	12.22	2550	0.6236	0.8517	0.8576	0.8517	0.8491	0.8963
0.6038	12.46	2600	0.5718	0.8674	0.8766	0.8674	0.8639	0.9071
0.6038	12.69	2650	0.5904	0.8644	0.8753	0.8644	0.8649	0.9061
0.6038	12.93	2700	0.6894	0.8487	0.8614	0.8487	0.8470	0.8951
0.5691	13.17	2750	0.6029	0.8652	0.8777	0.8652	0.8643	0.9064
0.5691	13.41	2800	0.6195	0.8727	0.8842	0.8727	0.8721	0.9105
0.5691	13.65	2850	0.6300	0.8682	0.8776	0.8682	0.8668	0.9076
0.5691	13.89	2900	0.6413	0.8644	0.8729	0.8644	0.8618	0.9058
0.5315	14.13	2950	0.7475	0.8509	0.8632	0.8509	0.8477	0.8958
0.5315	14.37	3000	0.6623	0.8659	0.8756	0.8659	0.8641	0.9069
0.5315	14.61	3050	0.6826	0.8547	0.8643	0.8547	0.8522	0.8978
0.5315	14.85	3100	0.6302	0.8712	0.8797	0.8712	0.8694	0.9097
0.5031	15.09	3150	0.5901	0.8787	0.8846	0.8787	0.8769	0.9157
0.5031	15.33	3200	0.6089	0.8652	0.8746	0.8652	0.8632	0.9056
0.5031	15.57	3250	0.6068	0.8719	0.8783	0.8719	0.8708	0.9108
0.5031	15.81	3300	0.6462	0.8652	0.8738	0.8652	0.8632	0.9056
0.4759	16.05	3350	0.6459	0.8607	0.8718	0.8607	0.8591	0.9013
0.4759	16.29	3400	0.6432	0.8644	0.8741	0.8644	0.8629	0.9052
0.4759	16.53	3450	0.6266	0.8652	0.8731	0.8652	0.8640	0.9058
0.4759	16.77	3500	0.5806	0.8824	0.8904	0.8824	0.8823	0.9170
0.4731	17.01	3550	0.6293	0.8697	0.8792	0.8697	0.8698	0.9089
0.4731	17.25	3600	0.6389	0.8682	0.8786	0.8682	0.8681	0.9079
0.4731	17.49	3650	0.6320	0.8712	0.8773	0.8712	0.8696	0.9098
0.4731	17.72	3700	0.6363	0.8742	0.8812	0.8742	0.8724	0.9128
0.4731	17.96	3750	0.6116	0.8854	0.8926	0.8854	0.8841	0.9199
0.4605	18.2	3800	0.6574	0.8794	0.8897	0.8794	0.8778	0.9161
0.4605	18.44	3850	0.6271	0.8749	0.8842	0.8749	0.8731	0.9135
0.4605	18.68	3900	0.6418	0.8749	0.8830	0.8749	0.8736	0.9139
0.4605	18.92	3950	0.6398	0.8704	0.8825	0.8704	0.8688	0.9103
0.4339	19.16	4000	0.6366	0.8689	0.8760	0.8689	0.8664	0.9085
0.4339	19.4	4050	0.6164	0.8727	0.8824	0.8727	0.8716	0.9110
0.4339	19.64	4100	0.6044	0.8846	0.8904	0.8846	0.8837	0.9190
0.4339	19.88	4150	0.6749	0.8742	0.8807	0.8742	0.8716	0.9123
0.4057	20.12	4200	0.7049	0.8637	0.8748	0.8637	0.8617	0.9059
0.4057	20.36	4250	0.6698	0.8727	0.8821	0.8727	0.8718	0.9116
0.4057	20.6	4300	0.6165	0.8779	0.8900	0.8779	0.8776	0.9146
0.4057	20.84	4350	0.5957	0.8697	0.8791	0.8697	0.8688	0.9087
0.4144	21.08	4400	0.6662	0.8644	0.8741	0.8644	0.8644	0.9047
0.4144	21.32	4450	0.7379	0.8487	0.8573	0.8487	0.8481	0.8942

Framework versions

Transformers 4.38.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1

fydhfzh/hubert-classifier-aug-fold-1

hubert-classifier-aug-fold-1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

fydhfzh/
hubert-classifier-aug-fold-1