learning_rate: 5e-06
train_batch_size: 4
eval_batch_size: 20
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.5692	0.0341	100	0.5455	0.5220	0.6605	0.3677
0.5286	0.0681	200	0.5233	0.5602	0.6848	0.4136
0.5048	0.1022	300	0.5009	0.5681	0.6902	0.4179
0.5125	0.1362	400	0.4740	0.5957	0.7091	0.4298
0.479	0.1703	500	0.4516	0.6236	0.7269	0.4570
0.4599	0.2044	600	0.4334	0.6564	0.7482	0.4638
0.4439	0.2384	700	0.4143	0.6747	0.7596	0.5157
0.4116	0.2725	800	0.4023	0.6802	0.7631	0.5319
0.4044	0.3065	900	0.4056	0.6810	0.7642	0.5362
0.4368	0.3406	1000	0.3900	0.7054	0.7828	0.5413
0.4258	0.3747	1100	0.3831	0.7153	0.7870	0.5651
0.3962	0.4087	1200	0.3750	0.7219	0.7910	0.5796
0.407	0.4428	1300	0.3775	0.7051	0.7786	0.5532
0.3808	0.4768	1400	0.3879	0.7301	0.7997	0.5813
0.3669	0.5109	1500	0.3661	0.7325	0.8000	0.6026
0.3701	0.5450	1600	0.3663	0.7331	0.8000	0.5906
0.3719	0.5790	1700	0.3707	0.7267	0.7951	0.5923
0.3734	0.6131	1800	0.3721	0.7306	0.7975	0.5983
0.3624	0.6471	1900	0.3536	0.7365	0.8026	0.6085
0.3617	0.6812	2000	0.3552	0.7407	0.8054	0.6060
0.3933	0.7153	2100	0.3517	0.7211	0.7902	0.5932
0.3564	0.7493	2200	0.3466	0.7384	0.8040	0.5957
0.3728	0.7834	2300	0.3592	0.7327	0.8005	0.5770
0.3539	0.8174	2400	0.3491	0.7390	0.8048	0.6281
0.3452	0.8515	2500	0.3431	0.7501	0.8138	0.6238
0.3453	0.8856	2600	0.3449	0.7457	0.8103	0.6162
0.3552	0.9196	2700	0.3485	0.7464	0.8114	0.6145
0.3619	0.9537	2800	0.3539	0.7402	0.8048	0.5983
0.345	0.9877	2900	0.3480	0.7484	0.8124	0.6009
0.3551	1.0218	3000	0.3477	0.7511	0.8142	0.5949
0.2926	1.0559	3100	0.3485	0.7469	0.8101	0.6383
0.3164	1.0899	3200	0.3355	0.7483	0.8114	0.6128
0.3536	1.1240	3300	0.3330	0.7597	0.8207	0.6247
0.2983	1.1580	3400	0.3454	0.7552	0.8178	0.6179
0.3007	1.1921	3500	0.3357	0.7567	0.8195	0.6332
0.3361	1.2262	3600	0.3329	0.7560	0.8178	0.6255
0.3173	1.2602	3700	0.3350	0.7570	0.8166	0.6255
0.3201	1.2943	3800	0.3340	0.7574	0.8173	0.6128
0.3205	1.3283	3900	0.3309	0.7653	0.8250	0.6349
0.3194	1.3624	4000	0.3331	0.7598	0.8200	0.6272
0.3228	1.3965	4100	0.3382	0.7585	0.8192	0.6187
0.323	1.4305	4200	0.3294	0.7640	0.8235	0.6366
0.3262	1.4646	4300	0.3271	0.7693	0.8281	0.6374
0.3042	1.4986	4400	0.3367	0.7511	0.8145	0.6417
0.3306	1.5327	4500	0.3353	0.7582	0.8197	0.6451
0.2829	1.5668	4600	0.3430	0.7534	0.8159	0.6426
0.2957	1.6008	4700	0.3406	0.7570	0.8177	0.6281
0.3095	1.6349	4800	0.3386	0.7486	0.8110	0.6221
0.316	1.6689	4900	0.3338	0.7587	0.8198	0.6332
0.3053	1.7030	5000	0.3386	0.7629	0.8243	0.6255
0.3303	1.7371	5100	0.3394	0.7516	0.8156	0.6357
0.2987	1.7711	5200	0.3339	0.7589	0.8205	0.6281
0.3252	1.8052	5300	0.3312	0.7666	0.8265	0.6357
0.337	1.8392	5400	0.3290	0.7631	0.8216	0.6426
0.3328	1.8733	5500	0.3283	0.7662	0.8252	0.6383
0.3041	1.9074	5600	0.3607	0.7557	0.8207	0.6111
0.319	1.9414	5700	0.3246	0.7686	0.8283	0.6545
0.3418	1.9755	5800	0.3201	0.7668	0.8262	0.6434
0.2795	2.0095	5900	0.3350	0.7620	0.8239	0.6230
0.2989	2.0436	6000	0.3377	0.7513	0.8134	0.6349
0.2599	2.0777	6100	0.3363	0.7657	0.8249	0.6468
0.2731	2.1117	6200	0.3308	0.7700	0.8286	0.6570
0.2891	2.1458	6300	0.3360	0.7622	0.8243	0.6434
0.2824	2.1798	6400	0.3406	0.7663	0.8281	0.6349
0.271	2.2139	6500	0.3393	0.7727	0.8333	0.6340
0.269	2.2480	6600	0.3395	0.7669	0.8262	0.6340
0.3048	2.2820	6700	0.3371	0.7666	0.8253	0.6443
0.2659	2.3161	6800	0.3453	0.7678	0.8278	0.6281
0.281	2.3501	6900	0.3386	0.7574	0.8193	0.6366
0.28	2.3842	7000	0.3286	0.7691	0.8287	0.6349
0.2895	2.4183	7100	0.3323	0.7656	0.8261	0.6315
0.2566	2.4523	7200	0.3327	0.7629	0.8234	0.6315
0.238	2.4864	7300	0.3450	0.7614	0.8226	0.6426
0.284	2.5204	7400	0.3392	0.7709	0.8305	0.6340
0.3125	2.5545	7500	0.3371	0.7668	0.8289	0.6315
0.2601	2.5886	7600	0.3394	0.7640	0.8228	0.6374
0.3005	2.6226	7700	0.3308	0.7696	0.8285	0.6426
0.2517	2.6567	7800	0.3342	0.7756	0.8331	0.6519
0.2805	2.6907	7900	0.3392	0.7729	0.8323	0.6417
0.2629	2.7248	8000	0.3403	0.7737	0.8311	0.6434
0.2958	2.7589	8100	0.3335	0.7715	0.8300	0.6391
0.2547	2.7929	8200	0.3470	0.7680	0.8271	0.6468
0.2957	2.8270	8300	0.3351	0.7755	0.8343	0.6511
0.2651	2.8610	8400	0.3348	0.7706	0.8303	0.6391
0.2991	2.8951	8500	0.3453	0.7689	0.8299	0.6443
0.2956	2.9292	8600	0.3418	0.7738	0.8320	0.6502
0.2929	2.9632	8700	0.3570	0.7601	0.8221	0.6485
0.2558	2.9973	8800	0.3511	0.7682	0.8272	0.6485
0.2929	3.0313	8900	0.3528	0.7683	0.8294	0.6281
0.229	3.0654	9000	0.3473	0.7695	0.8309	0.6357
0.2221	3.0995	9100	0.3576	0.7668	0.8265	0.6409
0.2308	3.1335	9200	0.3492	0.7703	0.8297	0.6426
0.2647	3.1676	9300	0.3570	0.7662	0.8252	0.6460
0.2455	3.2016	9400	0.3722	0.7642	0.8251	0.6562
0.2563	3.2357	9500	0.3690	0.7682	0.8284	0.6417
0.2985	3.2698	9600	0.3600	0.7654	0.8259	0.6443
0.2471	3.3038	9700	0.3563	0.7683	0.8294	0.6426
0.2182	3.3379	9800	0.3663	0.7658	0.8248	0.6494
0.251	3.3719	9900	0.3578	0.7624	0.8223	0.6349
0.2345	3.4060	10000	0.3704	0.7618	0.8225	0.6281
0.2358	3.4401	10100	0.3725	0.7676	0.8277	0.6460
0.2569	3.4741	10200	0.3742	0.7659	0.8246	0.6374
0.274	3.5082	10300	0.3616	0.7631	0.8243	0.6323
0.2449	3.5422	10400	0.3647	0.7642	0.8256	0.6374
0.2209	3.5763	10500	0.3665	0.7665	0.8261	0.6426
0.2567	3.6104	10600	0.3729	0.7586	0.8209	0.6451
0.2285	3.6444	10700	0.3734	0.7638	0.8277	0.6443
0.2177	3.6785	10800	0.3684	0.7734	0.8321	0.6502
0.2458	3.7125	10900	0.3934	0.7714	0.8342	0.6357
0.2508	3.7466	11000	0.3751	0.7656	0.8276	0.6323
0.2225	3.7807	11100	0.3892	0.7543	0.8166	0.6485
0.253	3.8147	11200	0.3854	0.7596	0.8205	0.6451
0.2074	3.8488	11300	0.4023	0.7593	0.8196	0.6468
0.2394	3.8828	11400	0.3922	0.7665	0.8266	0.6357
0.269	3.9169	11500	0.3742	0.7659	0.8275	0.6383
0.2502	3.9510	11600	0.3824	0.7645	0.8246	0.6502
0.2483	3.9850	11700	0.3704	0.7658	0.8265	0.6383
0.2499	4.0191	11800	0.3630	0.7694	0.8290	0.6485
0.1972	4.0531	11900	0.3681	0.7685	0.8296	0.6519
0.1998	4.0872	12000	0.3935	0.7655	0.8269	0.6502
0.2257	4.1213	12100	0.3908	0.7674	0.8277	0.6443
0.2198	4.1553	12200	0.3981	0.7636	0.8246	0.6468
0.2018	4.1894	12300	0.3936	0.7650	0.8276	0.6426
0.2382	4.2234	12400	0.4085	0.7623	0.8272	0.6281
0.2072	4.2575	12500	0.3963	0.7603	0.8240	0.6383
0.2004	4.2916	12600	0.3962	0.7662	0.8274	0.6485
0.2212	4.3256	12700	0.3936	0.7705	0.8311	0.6562
0.2001	4.3597	12800	0.3930	0.7650	0.8276	0.6426
0.1948	4.3937	12900	0.3996	0.7590	0.8210	0.6434
0.2255	4.4278	13000	0.4015	0.7632	0.8252	0.6468
0.226	4.4619	13100	0.4030	0.7682	0.8299	0.6417
0.2226	4.4959	13200	0.4161	0.7617	0.8237	0.6409
0.2089	4.5300	13300	0.4101	0.7662	0.8284	0.6417
0.1991	4.5640	13400	0.4043	0.7617	0.8233	0.6409
0.1786	4.5981	13500	0.4135	0.7661	0.8291	0.6434
0.2076	4.6322	13600	0.4174	0.7601	0.8231	0.6383
0.1875	4.6662	13700	0.4157	0.7613	0.8234	0.6391
0.2223	4.7003	13800	0.4189	0.7593	0.8224	0.6417
0.1988	4.7343	13900	0.4138	0.7643	0.8268	0.6426
0.1943	4.7684	14000	0.4259	0.7603	0.8248	0.6434
0.2442	4.8025	14100	0.4293	0.7611	0.8245	0.6417
0.2424	4.8365	14200	0.4219	0.7576	0.8217	0.6477
0.2204	4.8706	14300	0.4284	0.7586	0.8241	0.6366
0.2167	4.9046	14400	0.4162	0.7580	0.8216	0.6426
0.215	4.9387	14500	0.4274	0.7655	0.8284	0.6451
0.2165	4.9728	14600	0.4220	0.7564	0.8207	0.6349
0.2572	5.0068	14700	0.4203	0.7561	0.8190	0.6417
0.2207	5.0409	14800	0.4269	0.7568	0.8210	0.6502
0.1654	5.0749	14900	0.4321	0.7625	0.8270	0.6374
0.1936	5.1090	15000	0.4369	0.7587	0.8213	0.6477
0.199	5.1431	15100	0.4299	0.7605	0.8244	0.64
0.1901	5.1771	15200	0.4381	0.7590	0.8235	0.6477
0.1585	5.2112	15300	0.4321	0.7653	0.8292	0.6494
0.2054	5.2452	15400	0.4325	0.7650	0.8278	0.6468
0.1853	5.2793	15500	0.4404	0.7641	0.8266	0.6485
0.189	5.3134	15600	0.4450	0.7577	0.8214	0.6477
0.1882	5.3474	15700	0.4387	0.7585	0.8234	0.6426
0.2048	5.3815	15800	0.4455	0.7557	0.8212	0.6349
0.2267	5.4155	15900	0.4438	0.7572	0.8211	0.6409
0.1734	5.4496	16000	0.4517	0.7593	0.8224	0.6545
0.1916	5.4837	16100	0.4487	0.7564	0.8207	0.6485
0.1681	5.5177	16200	0.4441	0.7598	0.8244	0.6494
0.1869	5.5518	16300	0.4437	0.7621	0.8254	0.6485
0.1592	5.5858	16400	0.4535	0.7592	0.8224	0.6468
0.2072	5.6199	16500	0.4523	0.7608	0.8241	0.6374
0.2078	5.6540	16600	0.4592	0.7604	0.8225	0.6511
0.1727	5.6880	16700	0.4590	0.7655	0.8291	0.6434
0.1844	5.7221	16800	0.4518	0.7596	0.8230	0.6477
0.1885	5.7561	16900	0.4551	0.7553	0.8208	0.6289
0.1391	5.7902	17000	0.4695	0.7581	0.8216	0.6460
0.155	5.8243	17100	0.4711	0.7614	0.8268	0.6434
0.2328	5.8583	17200	0.4710	0.7507	0.8180	0.6426
0.2062	5.8924	17300	0.4657	0.7553	0.8206	0.64
0.2014	5.9264	17400	0.4656	0.7579	0.8244	0.6366
0.2007	5.9605	17500	0.4629	0.7579	0.8216	0.6409
0.2111	5.9946	17600	0.4586	0.7591	0.8232	0.6426
0.1426	6.0286	17700	0.4651	0.7600	0.8246	0.6494
0.154	6.0627	17800	0.4775	0.7599	0.8232	0.6519
0.1698	6.0967	17900	0.4846	0.7576	0.8235	0.6477
0.1449	6.1308	18000	0.4885	0.7605	0.8249	0.6519
0.1337	6.1649	18100	0.4919	0.7556	0.8217	0.6468
0.174	6.1989	18200	0.4928	0.7541	0.8210	0.6289
0.1488	6.2330	18300	0.4893	0.7577	0.8214	0.6434
0.1425	6.2670	18400	0.4937	0.7577	0.8212	0.6477
0.1655	6.3011	18500	0.5016	0.7550	0.8199	0.6460
0.1727	6.3351	18600	0.4984	0.7587	0.8238	0.6332
0.1824	6.3692	18700	0.4906	0.7542	0.8197	0.6391
0.1708	6.4033	18800	0.4899	0.7579	0.8214	0.6477
0.1773	6.4373	18900	0.4912	0.7575	0.8222	0.6340
0.1661	6.4714	19000	0.4940	0.7565	0.8196	0.6468
0.1686	6.5054	19100	0.4914	0.7596	0.8233	0.6570
0.166	6.5395	19200	0.4962	0.7551	0.8204	0.6519
0.1636	6.5736	19300	0.4917	0.7616	0.8263	0.6451
0.1989	6.6076	19400	0.4932	0.7586	0.8239	0.6383
0.1819	6.6417	19500	0.4869	0.7568	0.8218	0.6391
0.1735	6.6757	19600	0.4854	0.7589	0.8235	0.6511
0.1871	6.7098	19700	0.4835	0.7545	0.8206	0.6349
0.1798	6.7439	19800	0.4961	0.7531	0.8178	0.6443
0.1612	6.7779	19900	0.4869	0.7641	0.8286	0.6451
0.1874	6.8120	20000	0.4863	0.7589	0.8235	0.6494
0.1435	6.8460	20100	0.4906	0.7578	0.8219	0.6528
0.1797	6.8801	20200	0.4905	0.7604	0.8245	0.6528
0.1978	6.9142	20300	0.4901	0.7613	0.8264	0.6485
0.1763	6.9482	20400	0.4947	0.7603	0.8242	0.6528
0.1386	6.9823	20500	0.4922	0.7614	0.8251	0.6485
0.1588	7.0163	20600	0.4942	0.7671	0.8291	0.6494
0.1164	7.0504	20700	0.4999	0.7610	0.8250	0.6443
0.1467	7.0845	20800	0.5037	0.7575	0.8232	0.6434
0.1437	7.1185	20900	0.5090	0.7591	0.8239	0.6494
0.1562	7.1526	21000	0.5153	0.7559	0.8211	0.6485
0.159	7.1866	21100	0.5104	0.7562	0.8225	0.6349
0.1539	7.2207	21200	0.5129	0.7573	0.8231	0.6426
0.1643	7.2548	21300	0.5088	0.7531	0.8193	0.6426
0.1495	7.2888	21400	0.5108	0.7518	0.8193	0.6357
0.1557	7.3229	21500	0.5036	0.7607	0.8251	0.6434
0.1389	7.3569	21600	0.5101	0.7650	0.8288	0.6477
0.1645	7.3910	21700	0.5072	0.7577	0.8224	0.6409
0.2106	7.4251	21800	0.5082	0.7563	0.8209	0.6502
0.1472	7.4591	21900	0.5086	0.7595	0.8233	0.6443
0.1441	7.4932	22000	0.5098	0.7653	0.8282	0.6528
0.1255	7.5272	22100	0.5135	0.7648	0.8281	0.6434
0.1584	7.5613	22200	0.5207	0.7613	0.8241	0.6502
0.1685	7.5954	22300	0.5175	0.7603	0.8260	0.64
0.15	7.6294	22400	0.5205	0.7600	0.8241	0.6451
0.1455	7.6635	22500	0.5314	0.7601	0.8241	0.6570
0.1704	7.6975	22600	0.5194	0.7632	0.8262	0.6502
0.1334	7.7316	22700	0.5198	0.7640	0.8272	0.6494
0.1534	7.7657	22800	0.5229	0.7637	0.8275	0.6536
0.1247	7.7997	22900	0.5247	0.7638	0.8265	0.6494
0.1395	7.8338	23000	0.5256	0.7630	0.8273	0.6485
0.1508	7.8678	23100	0.5299	0.7626	0.8267	0.6468
0.1256	7.9019	23200	0.5319	0.7581	0.8230	0.6494
0.1741	7.9360	23300	0.5314	0.7581	0.8228	0.6553
0.1788	7.9700	23400	0.5249	0.7584	0.8245	0.6460
0.1405	8.0041	23500	0.5275	0.7599	0.8252	0.6502
0.1435	8.0381	23600	0.5307	0.7562	0.8223	0.6409
0.1582	8.0722	23700	0.5296	0.7573	0.8231	0.6409
0.1205	8.1063	23800	0.5310	0.7528	0.8205	0.6306
0.1361	8.1403	23900	0.5321	0.7579	0.8221	0.6468
0.1756	8.1744	24000	0.5315	0.7510	0.8181	0.64
0.1539	8.2084	24100	0.5313	0.7529	0.8194	0.6409
0.1026	8.2425	24200	0.5361	0.7585	0.8229	0.6502
0.1551	8.2766	24300	0.5387	0.7570	0.8232	0.6374
0.1333	8.3106	24400	0.5401	0.7549	0.8210	0.6298
0.1312	8.3447	24500	0.5373	0.7516	0.8187	0.6383
0.1214	8.3787	24600	0.5416	0.7540	0.8205	0.6417
0.1432	8.4128	24700	0.5442	0.7544	0.8199	0.6451
0.1688	8.4469	24800	0.5425	0.7536	0.8205	0.6383
0.1389	8.4809	24900	0.5472	0.7569	0.8215	0.6502
0.1084	8.5150	25000	0.5420	0.7543	0.8209	0.6366
0.1398	8.5490	25100	0.5448	0.7504	0.8176	0.6417
0.1217	8.5831	25200	0.5500	0.7547	0.8198	0.6460
0.1276	8.6172	25300	0.5486	0.7557	0.8212	0.6340
0.1218	8.6512	25400	0.5458	0.7555	0.8212	0.6409
0.1175	8.6853	25500	0.5512	0.7553	0.8210	0.6460
0.1582	8.7193	25600	0.5474	0.7550	0.8209	0.6451
0.1138	8.7534	25700	0.5485	0.7548	0.8213	0.6443
0.1138	8.7875	25800	0.5507	0.7541	0.8208	0.6443
0.1084	8.8215	25900	0.5513	0.7557	0.8214	0.6426
0.1115	8.8556	26000	0.5523	0.7590	0.8235	0.6494
0.132	8.8896	26100	0.5534	0.7536	0.8199	0.6434
0.1585	8.9237	26200	0.5508	0.7558	0.8214	0.6477
0.1298	8.9578	26300	0.5496	0.7571	0.8226	0.6485
0.1175	8.9918	26400	0.5506	0.7624	0.8257	0.6528
0.1438	9.0259	26500	0.5495	0.7616	0.8258	0.6519
0.1365	9.0599	26600	0.5533	0.7600	0.8241	0.6511
0.1344	9.0940	26700	0.5539	0.7567	0.8226	0.6443
0.1622	9.1281	26800	0.5520	0.7587	0.8233	0.6494
0.1324	9.1621	26900	0.5525	0.7568	0.8228	0.6434
0.1013	9.1962	27000	0.5521	0.7566	0.8216	0.6494
0.1274	9.2302	27100	0.5515	0.7573	0.8223	0.6519
0.1149	9.2643	27200	0.5510	0.7573	0.8226	0.6460
0.1294	9.2984	27300	0.5527	0.7601	0.8246	0.6519
0.1361	9.3324	27400	0.5520	0.7592	0.8239	0.6519
0.1175	9.3665	27500	0.5526	0.7598	0.8244	0.6519
0.1572	9.4005	27600	0.5513	0.7588	0.8235	0.6502
0.113	9.4346	27700	0.5527	0.7604	0.8247	0.6536
0.1373	9.4687	27800	0.5531	0.7586	0.8239	0.6485
0.124	9.5027	27900	0.5545	0.7597	0.8244	0.6545
0.1418	9.5368	28000	0.5545	0.7581	0.8238	0.6477
0.1256	9.5708	28100	0.5548	0.7599	0.8249	0.6502
0.1307	9.6049	28200	0.5548	0.7596	0.8245	0.6511
0.1051	9.6390	28300	0.5553	0.7604	0.8249	0.6536
0.1165	9.6730	28400	0.5558	0.7584	0.8235	0.6511
0.1249	9.7071	28500	0.5569	0.7603	0.8245	0.6519
0.1382	9.7411	28600	0.5557	0.7609	0.8253	0.6519
0.1356	9.7752	28700	0.5553	0.7604	0.8249	0.6511
0.1138	9.8093	28800	0.5564	0.7585	0.8234	0.6511
0.1428	9.8433	28900	0.5565	0.7566	0.8226	0.6485
0.1373	9.8774	29000	0.5564	0.7570	0.8229	0.6477
0.117	9.9114	29100	0.5561	0.7592	0.8241	0.6494
0.1199	9.9455	29200	0.5559	0.7603	0.8248	0.6502
0.1334	9.9796	29300	0.5558	0.7588	0.8238	0.6511

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

SoDehghan/test-push-to-hf

test-push-to-hf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

SoDehghan/
test-push-to-hf