Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.0775	1.0	30	nan	0.375	0.3679	0.4021	0.375
1.084	2.0	60	nan	0.3875	0.3833	0.4203	0.3875
1.0727	3.0	90	nan	0.375	0.3691	0.4075	0.375
1.0743	4.0	120	nan	0.3792	0.3736	0.4007	0.3792
1.0735	5.0	150	nan	0.3875	0.3829	0.4342	0.3875
1.0916	6.0	180	nan	0.375	0.3651	0.3971	0.375
1.0802	7.0	210	nan	0.3792	0.3728	0.4181	0.3792
1.0761	8.0	240	nan	0.3792	0.3586	0.4018	0.3792
1.0793	9.0	270	nan	0.4042	0.4014	0.4436	0.4042
1.0647	10.0	300	nan	0.3958	0.3930	0.4418	0.3958
1.0842	11.0	330	nan	0.375	0.3645	0.3957	0.375
1.0724	12.0	360	nan	0.3792	0.3770	0.4186	0.3792
1.0973	13.0	390	nan	0.4083	0.4108	0.4453	0.4083
1.0667	14.0	420	nan	0.3833	0.3789	0.4315	0.3833
1.0631	15.0	450	nan	0.4	0.3996	0.4392	0.4
1.0837	16.0	480	nan	0.3833	0.3771	0.4097	0.3833
1.073	17.0	510	nan	0.4	0.4002	0.4272	0.4
1.075	18.0	540	nan	0.3708	0.3625	0.4032	0.3708
1.0865	19.0	570	nan	0.3875	0.3839	0.4255	0.3875
1.075	20.0	600	nan	0.3917	0.3872	0.4290	0.3917
1.0884	21.0	630	nan	0.3917	0.3914	0.4301	0.3917
1.0921	22.0	660	nan	0.3917	0.3900	0.4445	0.3917
1.0749	23.0	690	nan	0.3958	0.3955	0.4341	0.3958
1.0535	24.0	720	nan	0.3917	0.3879	0.4297	0.3917
1.058	25.0	750	nan	0.3917	0.3881	0.4361	0.3917
1.0812	26.0	780	nan	0.3958	0.3944	0.4459	0.3958
1.0743	27.0	810	nan	0.3875	0.3844	0.4607	0.3875
1.0721	28.0	840	nan	0.4	0.3982	0.4597	0.4
1.0776	29.0	870	nan	0.3833	0.3800	0.4291	0.3833
1.0815	30.0	900	nan	0.4	0.4006	0.4444	0.4
1.0757	31.0	930	nan	0.3958	0.3975	0.4246	0.3958
1.065	32.0	960	nan	0.3833	0.3800	0.4252	0.3833
1.0669	33.0	990	nan	0.3875	0.3841	0.4173	0.3875
1.0685	34.0	1020	nan	0.3875	0.3852	0.4353	0.3875
1.0687	35.0	1050	nan	0.3792	0.3792	0.4163	0.3792
1.0736	36.0	1080	nan	0.3958	0.3942	0.4350	0.3958
1.0919	37.0	1110	nan	0.3792	0.3718	0.4349	0.3792
1.0905	38.0	1140	nan	0.3875	0.3840	0.4174	0.3875
1.0858	39.0	1170	nan	0.4042	0.4026	0.4337	0.4042
1.0809	40.0	1200	nan	0.375	0.3725	0.4152	0.375
1.0803	41.0	1230	nan	0.3958	0.3957	0.4386	0.3958
1.0688	42.0	1260	nan	0.3875	0.3847	0.4412	0.3875
1.0928	43.0	1290	nan	0.3875	0.3875	0.4485	0.3875
1.0711	44.0	1320	nan	0.3917	0.3914	0.4233	0.3917
1.0761	45.0	1350	nan	0.3958	0.3902	0.4515	0.3958
1.0738	46.0	1380	nan	0.3792	0.3776	0.4134	0.3792
1.0743	47.0	1410	nan	0.3792	0.3759	0.4295	0.3792
1.0849	48.0	1440	nan	0.3833	0.3793	0.4246	0.3833
1.0701	49.0	1470	nan	0.4	0.4002	0.4257	0.4
1.0496	50.0	1500	nan	0.3917	0.3898	0.4398	0.3917
1.0899	51.0	1530	nan	0.3958	0.3946	0.4314	0.3958
1.066	52.0	1560	nan	0.375	0.3725	0.4095	0.375
1.0777	53.0	1590	nan	0.3833	0.3793	0.4119	0.3833
1.0721	54.0	1620	nan	0.3792	0.3744	0.4108	0.3792
1.0887	55.0	1650	nan	0.3792	0.3766	0.4038	0.3792
1.0793	56.0	1680	nan	0.375	0.3725	0.4060	0.375
1.0747	57.0	1710	nan	0.3792	0.3750	0.4157	0.3792
1.0748	58.0	1740	nan	0.3792	0.3730	0.4188	0.3792
1.0589	59.0	1770	nan	0.3833	0.3810	0.4335	0.3833
1.078	60.0	1800	nan	0.3833	0.3841	0.4061	0.3833
1.0798	61.0	1830	nan	0.3833	0.3816	0.4312	0.3833
1.0864	62.0	1860	nan	0.3875	0.3848	0.4313	0.3875
1.074	63.0	1890	nan	0.3833	0.3821	0.4372	0.3833
1.0707	64.0	1920	nan	0.375	0.3726	0.4062	0.375
1.0793	65.0	1950	nan	0.375	0.3723	0.4046	0.375
1.0849	66.0	1980	nan	0.3917	0.3921	0.4408	0.3917
1.0722	67.0	2010	nan	0.3875	0.3841	0.4191	0.3875
1.0646	68.0	2040	nan	0.3958	0.3931	0.4241	0.3958
1.0771	69.0	2070	nan	0.3958	0.3950	0.4379	0.3958
1.0761	70.0	2100	nan	0.3917	0.3915	0.4408	0.3917
1.0759	71.0	2130	nan	0.3875	0.3862	0.4146	0.3875
1.0727	72.0	2160	nan	0.3792	0.3784	0.4375	0.3792
1.0758	73.0	2190	nan	0.3875	0.3829	0.4411	0.3875
1.0848	74.0	2220	nan	0.375	0.3713	0.4352	0.375
1.0659	75.0	2250	nan	0.3958	0.3927	0.4305	0.3958
1.0784	76.0	2280	nan	0.3792	0.3743	0.4097	0.3792
1.0831	77.0	2310	nan	0.3833	0.3776	0.4187	0.3833
1.074	78.0	2340	nan	0.3833	0.3789	0.4226	0.3833
1.08	79.0	2370	nan	0.375	0.3727	0.4187	0.375
1.0744	80.0	2400	nan	0.3917	0.3917	0.4181	0.3917
1.0568	81.0	2430	nan	0.3708	0.3681	0.4320	0.3708
1.074	82.0	2460	nan	0.3875	0.3853	0.4158	0.3875
1.0822	83.0	2490	nan	0.3917	0.3897	0.4406	0.3917
1.0735	84.0	2520	nan	0.3833	0.3820	0.4121	0.3833
1.0679	85.0	2550	nan	0.3875	0.3860	0.4324	0.3875
1.0775	86.0	2580	nan	0.375	0.3670	0.4306	0.375
1.0683	87.0	2610	nan	0.3792	0.3736	0.4168	0.3792
1.0611	88.0	2640	nan	0.3792	0.3738	0.4278	0.3792
1.0844	89.0	2670	nan	0.3833	0.3808	0.4212	0.3833
1.0628	90.0	2700	nan	0.3917	0.3891	0.4244	0.3917
1.0639	91.0	2730	nan	0.3833	0.3779	0.4159	0.3833
1.0758	92.0	2760	nan	0.3708	0.3677	0.4278	0.3708
1.0991	93.0	2790	nan	0.375	0.3726	0.4107	0.375
1.0733	94.0	2820	nan	0.3833	0.3781	0.4232	0.3833
1.0632	95.0	2850	nan	0.3875	0.3856	0.4115	0.3875
1.0791	96.0	2880	nan	0.3667	0.3595	0.4048	0.3667
1.0633	97.0	2910	nan	0.3792	0.3768	0.4145	0.3792
1.074	98.0	2940	nan	0.375	0.3723	0.4008	0.375
1.0983	99.0	2970	nan	0.375	0.3695	0.4072	0.375
1.0959	100.0	3000	nan	0.3792	0.3757	0.4054	0.3792
1.0695	101.0	3030	nan	0.3917	0.3869	0.4205	0.3917
1.0708	102.0	3060	nan	0.3958	0.3921	0.4264	0.3958
1.0741	103.0	3090	nan	0.3792	0.3714	0.4111	0.3792
1.0773	104.0	3120	nan	0.3833	0.3784	0.4154	0.3833
1.0824	105.0	3150	nan	0.375	0.3683	0.4067	0.375
1.071	106.0	3180	nan	0.3708	0.3668	0.4033	0.3708
1.0631	107.0	3210	nan	0.3833	0.3808	0.4136	0.3833
1.0824	108.0	3240	nan	0.375	0.3691	0.4184	0.375
1.0779	109.0	3270	nan	0.3708	0.3644	0.4080	0.3708
1.0739	110.0	3300	nan	0.3792	0.3775	0.4139	0.3792
1.0715	111.0	3330	nan	0.3917	0.3878	0.4404	0.3917
1.0633	112.0	3360	nan	0.3792	0.3777	0.4302	0.3792
1.063	113.0	3390	nan	0.375	0.3729	0.4195	0.375
1.078	114.0	3420	nan	0.3875	0.3826	0.4360	0.3875
1.0737	115.0	3450	nan	0.3875	0.3857	0.4427	0.3875
1.067	116.0	3480	nan	0.3917	0.3876	0.4393	0.3917
1.0581	117.0	3510	nan	0.375	0.3718	0.4174	0.375
1.0691	118.0	3540	nan	0.375	0.3695	0.4130	0.375
1.0589	119.0	3570	nan	0.375	0.3718	0.4174	0.375
1.0616	120.0	3600	nan	0.3708	0.3650	0.4020	0.3708
1.0541	121.0	3630	nan	0.375	0.3715	0.4067	0.375
1.0699	122.0	3660	nan	0.3875	0.3831	0.4214	0.3875
1.0697	123.0	3690	nan	0.3958	0.3925	0.4297	0.3958
1.0744	124.0	3720	nan	0.3917	0.3881	0.4271	0.3917
1.0817	125.0	3750	nan	0.3875	0.3816	0.4359	0.3875
1.085	126.0	3780	nan	0.3708	0.3680	0.4096	0.3708
1.0918	127.0	3810	nan	0.3792	0.3772	0.4240	0.3792
1.071	128.0	3840	nan	0.3917	0.3904	0.4357	0.3917
1.0684	129.0	3870	nan	0.4	0.3996	0.4426	0.4
1.0647	130.0	3900	nan	0.3792	0.3769	0.4430	0.3792
1.0814	131.0	3930	nan	0.375	0.3721	0.4137	0.375
1.0806	132.0	3960	nan	0.3833	0.3801	0.4489	0.3833
1.0877	133.0	3990	nan	0.3875	0.3861	0.4332	0.3875
1.0694	134.0	4020	nan	0.3917	0.3891	0.4266	0.3917
1.071	135.0	4050	nan	0.3917	0.3899	0.4383	0.3917
1.0647	136.0	4080	nan	0.3917	0.3904	0.4357	0.3917
1.0877	137.0	4110	nan	0.3917	0.3902	0.4327	0.3917
1.0661	138.0	4140	nan	0.3875	0.3855	0.4266	0.3875
1.0855	139.0	4170	nan	0.3917	0.3912	0.4464	0.3917
1.0824	140.0	4200	nan	0.3917	0.3888	0.4509	0.3917
1.0615	141.0	4230	nan	0.3917	0.3905	0.4384	0.3917
1.0684	142.0	4260	nan	0.3958	0.3942	0.4474	0.3958
1.0514	143.0	4290	nan	0.3875	0.3857	0.4259	0.3875
1.0786	144.0	4320	nan	0.3917	0.3899	0.4294	0.3917
1.0908	145.0	4350	nan	0.3875	0.3863	0.4493	0.3875
1.0945	146.0	4380	nan	0.3875	0.3850	0.4275	0.3875
1.0635	147.0	4410	nan	0.3792	0.3769	0.4199	0.3792
1.0573	148.0	4440	nan	0.3917	0.3904	0.4378	0.3917
1.0602	149.0	4470	nan	0.3875	0.3855	0.4331	0.3875
1.0824	150.0	4500	nan	0.3958	0.3938	0.4369	0.3958
1.0759	151.0	4530	nan	0.3958	0.3939	0.4391	0.3958
1.0373	152.0	4560	nan	0.4	0.3985	0.4577	0.4
1.0891	153.0	4590	nan	0.3875	0.3863	0.4493	0.3875
1.0836	154.0	4620	nan	0.3917	0.3909	0.4426	0.3917
1.043	155.0	4650	nan	0.3917	0.3903	0.4350	0.3917
1.0686	156.0	4680	nan	0.3875	0.3863	0.4493	0.3875
1.0733	157.0	4710	nan	0.3833	0.3810	0.4452	0.3833
1.0777	158.0	4740	nan	0.3833	0.3802	0.4461	0.3833
1.0842	159.0	4770	nan	0.3917	0.3898	0.4398	0.3917
1.0586	160.0	4800	nan	0.3875	0.3859	0.4372	0.3875
1.072	161.0	4830	nan	0.375	0.3730	0.4301	0.375
1.0853	162.0	4860	nan	0.3875	0.3868	0.4237	0.3875
1.061	163.0	4890	nan	0.3792	0.3759	0.4202	0.3792
1.0806	164.0	4920	nan	0.3917	0.3903	0.4470	0.3917
1.0873	165.0	4950	nan	0.3958	0.3937	0.4431	0.3958
1.0696	166.0	4980	nan	0.3958	0.3943	0.4504	0.3958
1.0828	167.0	5010	nan	0.3958	0.3937	0.4431	0.3958
1.069	168.0	5040	nan	0.3917	0.3900	0.4433	0.3917
1.0798	169.0	5070	nan	0.3833	0.3817	0.4329	0.3833
1.0801	170.0	5100	nan	0.3875	0.3857	0.4364	0.3875
1.055	171.0	5130	nan	0.3833	0.3820	0.4364	0.3833
1.0737	172.0	5160	nan	0.3833	0.3824	0.4305	0.3833
1.0805	173.0	5190	nan	0.3833	0.3821	0.4275	0.3833
1.0783	174.0	5220	nan	0.3917	0.3912	0.4353	0.3917
1.0823	175.0	5250	nan	0.3833	0.3820	0.4330	0.3833
1.0732	176.0	5280	nan	0.3917	0.3900	0.4433	0.3917
1.0651	177.0	5310	nan	0.3875	0.3863	0.4436	0.3875
1.0865	178.0	5340	nan	0.3792	0.3783	0.4367	0.3792
1.0693	179.0	5370	nan	0.3958	0.3947	0.4551	0.3958
1.0764	180.0	5400	nan	0.3833	0.3811	0.4267	0.3833
1.069	181.0	5430	nan	0.3875	0.3859	0.4372	0.3875
1.0872	182.0	5460	nan	0.3792	0.3777	0.4302	0.3792
1.0919	183.0	5490	nan	0.3958	0.3939	0.4439	0.3958
1.0715	184.0	5520	nan	0.3875	0.3859	0.4372	0.3875
1.0569	185.0	5550	nan	0.3792	0.3767	0.4195	0.3792
1.0665	186.0	5580	nan	0.3958	0.3937	0.4408	0.3958
1.0702	187.0	5610	nan	0.3875	0.3856	0.4249	0.3875
1.0809	188.0	5640	nan	0.3792	0.3777	0.4302	0.3792
1.0659	189.0	5670	nan	0.3917	0.3899	0.4406	0.3917
1.0581	190.0	5700	nan	0.3833	0.3816	0.4305	0.3833
1.0776	191.0	5730	nan	0.3875	0.3862	0.4406	0.3875
1.0802	192.0	5760	nan	0.3958	0.3942	0.4474	0.3958
1.0845	193.0	5790	nan	0.3917	0.3899	0.4534	0.3917
1.0512	194.0	5820	nan	0.3917	0.3912	0.4512	0.3917
1.0702	195.0	5850	nan	0.3958	0.3940	0.4466	0.3958
1.0794	196.0	5880	nan	0.3875	0.3862	0.4406	0.3875
1.0817	197.0	5910	nan	0.3917	0.3902	0.4440	0.3917
1.0798	198.0	5940	nan	0.3958	0.3942	0.4474	0.3958
1.0764	199.0	5970	nan	0.3958	0.3944	0.4511	0.3958
1.0625	200.0	6000	nan	0.3833	0.3812	0.44	0.3833
1.0746	201.0	6030	nan	0.3917	0.3905	0.4478	0.3917
1.0912	202.0	6060	nan	0.3792	0.3770	0.4213	0.3792
1.0557	203.0	6090	nan	0.3833	0.3812	0.44	0.3833
1.057	204.0	6120	nan	0.3875	0.3850	0.4275	0.3875
1.0635	205.0	6150	nan	0.3875	0.3852	0.4302	0.3875
1.0744	206.0	6180	nan	0.375	0.3721	0.4196	0.375
1.0939	207.0	6210	nan	0.3875	0.3852	0.4302	0.3875
1.0703	208.0	6240	nan	0.3792	0.3755	0.4308	0.3792
1.0812	209.0	6270	nan	0.3708	0.3676	0.4247	0.3708
1.0797	210.0	6300	nan	0.3875	0.3854	0.4311	0.3875
1.0783	211.0	6330	nan	0.3833	0.3807	0.4215	0.3833
1.0645	212.0	6360	nan	0.3917	0.3895	0.4345	0.3917
1.075	213.0	6390	nan	0.375	0.3723	0.4225	0.375
1.0549	214.0	6420	nan	0.3917	0.3893	0.4319	0.3917
1.0732	215.0	6450	nan	0.375	0.3726	0.4258	0.375
1.081	216.0	6480	nan	0.3708	0.3674	0.4239	0.3708
1.0623	217.0	6510	nan	0.3833	0.3793	0.4335	0.3833
1.066	218.0	6540	nan	0.3833	0.3793	0.4335	0.3833
1.0761	219.0	6570	nan	0.3833	0.3795	0.4342	0.3833
1.0528	220.0	6600	nan	0.3917	0.3899	0.4406	0.3917
1.0577	221.0	6630	nan	0.3792	0.3755	0.4308	0.3792
1.0796	222.0	6660	nan	0.3875	0.3846	0.4381	0.3875
1.0571	223.0	6690	nan	0.3875	0.3859	0.4372	0.3875
1.081	224.0	6720	nan	0.3875	0.3859	0.4372	0.3875
1.0761	225.0	6750	nan	0.3792	0.3772	0.4240	0.3792
1.0667	226.0	6780	nan	0.3833	0.3810	0.4362	0.3833
1.0838	227.0	6810	nan	0.375	0.3723	0.4225	0.375
1.0441	228.0	6840	nan	0.375	0.3723	0.4225	0.375
1.053	229.0	6870	nan	0.375	0.3729	0.4293	0.375
1.0648	230.0	6900	nan	0.3792	0.3769	0.4328	0.3792
1.0704	231.0	6930	nan	0.3792	0.3758	0.4347	0.3792
1.0909	232.0	6960	nan	0.375	0.3718	0.4313	0.375
1.0405	233.0	6990	nan	0.3833	0.3808	0.4354	0.3833
1.0957	234.0	7020	nan	0.375	0.3718	0.4313	0.375
1.0526	235.0	7050	nan	0.3792	0.3758	0.4347	0.3792
1.07	236.0	7080	nan	0.3708	0.3674	0.4239	0.3708
1.081	237.0	7110	nan	0.375	0.3718	0.4313	0.375
1.0567	238.0	7140	nan	0.375	0.3718	0.4313	0.375
1.0558	239.0	7170	nan	0.375	0.3715	0.4274	0.375
1.1036	240.0	7200	nan	0.375	0.3715	0.4274	0.375
1.0691	241.0	7230	nan	0.3792	0.3769	0.4328	0.3792
1.0531	242.0	7260	nan	0.3833	0.3821	0.4372	0.3833
1.0711	243.0	7290	nan	0.3833	0.3818	0.4337	0.3833
1.0812	244.0	7320	nan	0.3833	0.3821	0.4372	0.3833
1.0796	245.0	7350	nan	0.3833	0.3821	0.4372	0.3833
1.0536	246.0	7380	nan	0.3833	0.3818	0.4337	0.3833
1.0766	247.0	7410	nan	0.3833	0.3818	0.4337	0.3833
1.0803	248.0	7440	nan	0.3833	0.3818	0.4337	0.3833
1.075	249.0	7470	nan	0.3833	0.3818	0.4337	0.3833
1.0688	250.0	7500	nan	0.3833	0.3818	0.4337	0.3833

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

ynclinebk/phobert_finetuned

phobert_finetuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ynclinebk/
phobert_finetuned