Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.22	50	3.8853	0.0499	0.0125	0.0499	0.0132	0.3132
No log	0.43	100	3.4193	0.0702	0.0103	0.0702	0.0167	0.3417
No log	0.65	150	3.1606	0.1161	0.0528	0.1161	0.0512	0.3783
No log	0.86	200	2.9650	0.1714	0.1093	0.1714	0.1008	0.4148
3.6314	1.08	250	2.7976	0.1714	0.0947	0.1714	0.0927	0.4148
3.6314	1.29	300	2.4874	0.2740	0.1723	0.2740	0.1770	0.4870
3.6314	1.51	350	2.3192	0.3414	0.2741	0.3414	0.2588	0.5379
3.6314	1.72	400	2.1116	0.4278	0.3772	0.4278	0.3514	0.5988
3.6314	1.94	450	1.9495	0.4683	0.4712	0.4683	0.4075	0.6273
2.6994	2.16	500	1.8632	0.4764	0.4337	0.4764	0.4098	0.6313
2.6994	2.37	550	1.6822	0.5250	0.4922	0.5250	0.4621	0.6664
2.6994	2.59	600	1.5299	0.5938	0.5590	0.5938	0.5296	0.7165
2.6994	2.8	650	1.4177	0.6140	0.5885	0.6140	0.5664	0.7298
2.1575	3.02	700	1.3614	0.6100	0.5558	0.6100	0.5498	0.7283
2.1575	3.23	750	1.2209	0.6491	0.6650	0.6491	0.6086	0.7528
2.1575	3.45	800	1.2059	0.6829	0.7281	0.6829	0.6553	0.7748
2.1575	3.66	850	1.1207	0.7031	0.7301	0.7031	0.6741	0.7908
2.1575	3.88	900	1.0553	0.7139	0.7368	0.7139	0.6967	0.7993
1.8321	4.09	950	0.9864	0.7490	0.7412	0.7490	0.7291	0.8250
1.8321	4.31	1000	0.9365	0.7544	0.7653	0.7544	0.7360	0.8278
1.8321	4.53	1050	0.9050	0.7584	0.7617	0.7584	0.7438	0.8323
1.8321	4.74	1100	0.8241	0.7760	0.7896	0.7760	0.7638	0.8424
1.8321	4.96	1150	0.8260	0.7611	0.7712	0.7611	0.7455	0.8335
1.6136	5.17	1200	0.8008	0.7814	0.8013	0.7814	0.7738	0.8474
1.6136	5.39	1250	0.7588	0.7787	0.7981	0.7787	0.7694	0.8453
1.6136	5.6	1300	0.7619	0.7868	0.7963	0.7868	0.7755	0.8520
1.6136	5.82	1350	0.7611	0.7868	0.7972	0.7868	0.7755	0.8514
1.4406	6.03	1400	0.7094	0.7976	0.8090	0.7976	0.7923	0.8591
1.4406	6.25	1450	0.7133	0.7976	0.7990	0.7976	0.7873	0.8595
1.4406	6.47	1500	0.7126	0.8016	0.8151	0.8016	0.7921	0.8618
1.4406	6.68	1550	0.6381	0.8043	0.8242	0.8043	0.7979	0.8642
1.4406	6.9	1600	0.6248	0.8111	0.8253	0.8111	0.8079	0.8683
1.3033	7.11	1650	0.6333	0.8138	0.8391	0.8138	0.8125	0.8699
1.3033	7.33	1700	0.6087	0.8232	0.8450	0.8232	0.8188	0.8775
1.3033	7.54	1750	0.5919	0.8273	0.8347	0.8273	0.8199	0.8796
1.3033	7.76	1800	0.5835	0.8232	0.8398	0.8232	0.8198	0.8779
1.3033	7.97	1850	0.5697	0.8232	0.8397	0.8232	0.8192	0.8780
1.2408	8.19	1900	0.6186	0.8111	0.8354	0.8111	0.8050	0.8694
1.2408	8.41	1950	0.5622	0.8327	0.8445	0.8327	0.8306	0.8830
1.2408	8.62	2000	0.5740	0.8205	0.8407	0.8205	0.8184	0.8756
1.2408	8.84	2050	0.5627	0.8300	0.8460	0.8300	0.8286	0.8816
1.1272	9.05	2100	0.5340	0.8381	0.8538	0.8381	0.8354	0.8873
1.1272	9.27	2150	0.5927	0.8246	0.8475	0.8246	0.8205	0.8795
1.1272	9.48	2200	0.5429	0.8300	0.8511	0.8300	0.8292	0.8812
1.1272	9.7	2250	0.4781	0.8529	0.8659	0.8529	0.8518	0.8977
1.1272	9.91	2300	0.5081	0.8448	0.8621	0.8448	0.8442	0.8930
1.0783	10.13	2350	0.5015	0.8489	0.8680	0.8489	0.8473	0.8947
1.0783	10.34	2400	0.5225	0.8421	0.8627	0.8421	0.8389	0.8906
1.0783	10.56	2450	0.5113	0.8435	0.8602	0.8435	0.8426	0.8911
1.0783	10.78	2500	0.5009	0.8556	0.8712	0.8556	0.8526	0.9005
1.0783	10.99	2550	0.5112	0.8421	0.8571	0.8421	0.8416	0.8922
1.0384	11.21	2600	0.4812	0.8543	0.8663	0.8543	0.8516	0.8996
1.0384	11.42	2650	0.5017	0.8583	0.8731	0.8583	0.8561	0.9020
1.0384	11.64	2700	0.4837	0.8489	0.8650	0.8489	0.8450	0.8958
1.0384	11.85	2750	0.5084	0.8435	0.8643	0.8435	0.8389	0.8920
1.0115	12.07	2800	0.4843	0.8583	0.8767	0.8583	0.8562	0.9024
1.0115	12.28	2850	0.5396	0.8502	0.8703	0.8502	0.8465	0.8973
1.0115	12.5	2900	0.5440	0.8435	0.8563	0.8435	0.8375	0.8916
1.0115	12.72	2950	0.5076	0.8489	0.8600	0.8489	0.8460	0.8958
1.0115	12.93	3000	0.5253	0.8435	0.8571	0.8435	0.8422	0.8915
0.9461	13.15	3050	0.4841	0.8664	0.8828	0.8664	0.8669	0.9081
0.9461	13.36	3100	0.4890	0.8637	0.8759	0.8637	0.8626	0.9062
0.9461	13.58	3150	0.4948	0.8664	0.8819	0.8664	0.8656	0.9076
0.9461	13.79	3200	0.4851	0.8596	0.8763	0.8596	0.8590	0.9028
0.9001	14.01	3250	0.4966	0.8583	0.8735	0.8583	0.8587	0.9020
0.9001	14.22	3300	0.5335	0.8543	0.8688	0.8543	0.8531	0.8985
0.9001	14.44	3350	0.5072	0.8596	0.8732	0.8596	0.8587	0.9024
0.9001	14.66	3400	0.4968	0.8664	0.8793	0.8664	0.8654	0.9076
0.9001	14.87	3450	0.4763	0.8637	0.8779	0.8637	0.8629	0.9057
0.8663	15.09	3500	0.5134	0.8704	0.8781	0.8704	0.8685	0.9104
0.8663	15.3	3550	0.5010	0.8664	0.8812	0.8664	0.8668	0.9081
0.8663	15.52	3600	0.5221	0.8556	0.8694	0.8556	0.8547	0.9000
0.8663	15.73	3650	0.4990	0.8704	0.8813	0.8704	0.8691	0.9104
0.8663	15.95	3700	0.5046	0.8610	0.8729	0.8610	0.8591	0.9032
0.8499	16.16	3750	0.5099	0.8583	0.8730	0.8583	0.8562	0.9019
0.8499	16.38	3800	0.4859	0.8677	0.8786	0.8677	0.8668	0.9085
0.8499	16.59	3850	0.5005	0.8637	0.8731	0.8637	0.8619	0.9067
0.8499	16.81	3900	0.5169	0.8596	0.8735	0.8596	0.8590	0.9023
0.8149	17.03	3950	0.5035	0.8583	0.8710	0.8583	0.8579	0.9019
0.8149	17.24	4000	0.5211	0.8570	0.8748	0.8570	0.8573	0.9009

Framework versions

Transformers 4.38.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1

fydhfzh/hubert-classifier-aug-fold-1-old

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-old