learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1307	1.0	130	1.0262	0.4962
1.0927	2.0	260	1.7590	0.2632
1.0507	3.0	390	2.1327	0.5188
1.0081	4.0	520	1.3239	0.5714
0.9565	5.0	650	1.1102	0.5489
0.7963	6.0	780	1.3624	0.6917
0.6663	7.0	910	5.5153	0.5564
0.6336	8.0	1040	5.0001	0.5940
0.5852	9.0	1170	9.5447	0.5489
0.5467	10.0	1300	6.4452	0.5714
0.5318	11.0	1430	12.3394	0.5038
0.5177	12.0	1560	3.9932	0.5940
0.4431	13.0	1690	1.7703	0.6541
0.4164	14.0	1820	4.0616	0.5038
0.4392	15.0	1950	1.3017	0.7744
0.4356	16.0	2080	1.1080	0.7293
0.3853	17.0	2210	5.3221	0.5789
0.3911	18.0	2340	1.3064	0.7143
0.3493	19.0	2470	8.7354	0.5564
0.3017	20.0	2600	1.4359	0.6466
0.3532	21.0	2730	5.5559	0.6241
0.2868	22.0	2860	3.5486	0.5263
0.3125	23.0	2990	7.4942	0.6617
0.326	24.0	3120	3.8914	0.7143
0.2561	25.0	3250	2.8141	0.6692
0.2923	26.0	3380	6.7704	0.6090
0.2311	27.0	3510	1.8806	0.7293
0.2274	28.0	3640	2.3829	0.6316
0.2481	29.0	3770	3.2873	0.5940
0.2612	30.0	3900	1.7361	0.7368
0.2541	31.0	4030	6.3135	0.6241
0.1998	32.0	4160	4.0907	0.6842
0.2628	33.0	4290	4.6728	0.7068
0.2515	34.0	4420	3.1405	0.6617
0.2352	35.0	4550	3.2859	0.7519
0.242	36.0	4680	0.8856	0.7594
0.2095	37.0	4810	5.4219	0.6692
0.2173	38.0	4940	9.1599	0.6842
0.1865	39.0	5070	2.9133	0.7293
0.2539	40.0	5200	10.3407	0.6241
0.2512	41.0	5330	4.8001	0.7218
0.2304	42.0	5460	8.2643	0.6767
0.1761	43.0	5590	6.4020	0.6466
0.1947	44.0	5720	2.3283	0.7293
0.2011	45.0	5850	3.2135	0.6842
0.1789	46.0	5980	2.5271	0.7218
0.1217	47.0	6110	3.5338	0.7218
0.197	48.0	6240	2.8379	0.7669
0.1378	49.0	6370	6.7036	0.6767
0.1641	50.0	6500	5.3123	0.6692
0.171	51.0	6630	29.0727	0.5489
0.1694	52.0	6760	3.9145	0.7669
0.1694	53.0	6890	20.2058	0.6015
0.0983	54.0	7020	3.0154	0.7444
0.0983	55.0	7150	4.5036	0.6992
0.1116	56.0	7280	15.8594	0.5564
0.1467	57.0	7410	1.9574	0.7744
0.1161	58.0	7540	7.0993	0.5940
0.1424	59.0	7670	5.0006	0.7368
0.0921	60.0	7800	10.6072	0.6015
0.1014	61.0	7930	3.9741	0.7368
0.1456	62.0	8060	2.6188	0.7744
0.2115	63.0	8190	5.3006	0.6617
0.1167	64.0	8320	3.2966	0.6992
0.0746	65.0	8450	2.1400	0.7594
0.0694	66.0	8580	5.7985	0.6767
0.0515	67.0	8710	3.5244	0.6767
0.0513	68.0	8840	4.2358	0.6917
0.1511	69.0	8970	6.8578	0.6541
0.1871	70.0	9100	12.4745	0.6617
0.114	71.0	9230	2.7450	0.7594
0.0438	72.0	9360	5.2159	0.6842
0.054	73.0	9490	3.8337	0.7143
0.1645	74.0	9620	12.4765	0.5789
0.0655	75.0	9750	3.4949	0.7143
0.0676	76.0	9880	3.7470	0.7293
0.1427	77.0	10010	9.8213	0.6316
0.099	78.0	10140	14.3845	0.6015
0.0943	79.0	10270	3.3007	0.7895
0.0971	80.0	10400	4.5807	0.6917
0.1338	81.0	10530	7.8281	0.6692
0.0494	82.0	10660	10.0532	0.6617
0.0384	83.0	10790	3.4354	0.7820
0.0781	84.0	10920	7.8234	0.6316
0.1122	85.0	11050	5.1243	0.7068
0.0965	86.0	11180	7.5119	0.6617
0.1852	87.0	11310	11.2423	0.6015
0.0512	88.0	11440	2.3147	0.7744
0.0456	89.0	11570	2.9752	0.7744
0.0479	90.0	11700	17.1507	0.6241
0.04	91.0	11830	2.8366	0.7068
0.1437	92.0	11960	16.1989	0.5789
0.0256	93.0	12090	3.2687	0.6917
0.0178	94.0	12220	3.8819	0.7068
0.0356	95.0	12350	2.6739	0.6992
0.1282	96.0	12480	8.0099	0.6466
0.0544	97.0	12610	11.1235	0.6466
0.0502	98.0	12740	4.4413	0.6241
0.0398	99.0	12870	26.8311	0.5188
0.1161	100.0	13000	9.7330	0.6466

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

alem-147/poisoned-baseline2

poisoned-baseline2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alem-147/
poisoned-baseline2