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
1.1079	1.0	130	1.0555	0.5188
1.0487	2.0	260	2.1006	0.3910
1.0065	3.0	390	4.1404	0.3008
0.9758	4.0	520	2.0769	0.5489
0.9558	5.0	650	1.4474	0.5113
0.9116	6.0	780	1.6002	0.6466
0.8887	7.0	910	2.6059	0.5789
0.8736	8.0	1040	1.5122	0.4662
0.8478	9.0	1170	1.7094	0.3910
0.8845	10.0	1300	2.4116	0.5714
0.8223	11.0	1430	2.1748	0.5263
0.8169	12.0	1560	2.7392	0.5865
0.8053	13.0	1690	1.9351	0.4286
0.7562	14.0	1820	1.6459	0.5263
0.7715	15.0	1950	0.9730	0.5714
0.8031	16.0	2080	1.8118	0.5940
0.797	17.0	2210	2.0251	0.5639
0.7489	18.0	2340	1.6305	0.4662
0.7661	19.0	2470	0.9456	0.6165
0.6743	20.0	2600	1.1777	0.5789
0.7162	21.0	2730	1.9899	0.5489
0.6952	22.0	2860	2.1572	0.5188
0.6998	23.0	2990	3.6954	0.4962
0.7048	24.0	3120	1.4983	0.5489
0.668	25.0	3250	1.4684	0.6090
0.6539	26.0	3380	1.5490	0.6015
0.6404	27.0	3510	1.0373	0.6090
0.6337	28.0	3640	0.8090	0.6767
0.6422	29.0	3770	2.0051	0.5263
0.6487	30.0	3900	1.0576	0.5714
0.5979	31.0	4030	2.6454	0.5414
0.629	32.0	4160	1.6747	0.4962
0.6262	33.0	4290	2.3917	0.5188
0.6286	34.0	4420	1.1679	0.5113
0.6048	35.0	4550	1.8266	0.6391
0.603	36.0	4680	0.7241	0.6842
0.5939	37.0	4810	3.3023	0.5338
0.5756	38.0	4940	1.7101	0.6316
0.558	39.0	5070	2.0204	0.3835
0.5721	40.0	5200	1.5391	0.6316
0.5838	41.0	5330	2.9189	0.4887
0.563	42.0	5460	2.1778	0.6241
0.5788	43.0	5590	3.7351	0.4135
0.5361	44.0	5720	0.8738	0.6541
0.5897	45.0	5850	1.7730	0.5865
0.5299	46.0	5980	1.2070	0.6316
0.5215	47.0	6110	1.1173	0.6316
0.5385	48.0	6240	1.5332	0.6241
0.5397	49.0	6370	2.5272	0.5714
0.5233	50.0	6500	1.8423	0.6165
0.5571	51.0	6630	1.4039	0.6391
0.5377	52.0	6760	1.5045	0.5338
0.4985	53.0	6890	3.8733	0.4962
0.476	54.0	7020	1.3020	0.5113
0.5115	55.0	7150	2.1457	0.5865
0.5097	56.0	7280	3.9787	0.5414
0.5148	57.0	7410	0.9982	0.6466
0.4669	58.0	7540	8.1125	0.3308
0.5279	59.0	7670	5.7709	0.5263
0.4673	60.0	7800	4.8501	0.5414
0.4956	61.0	7930	1.4053	0.5940
0.4959	62.0	8060	0.9127	0.5865
0.4881	63.0	8190	5.8092	0.5038
0.4928	64.0	8320	0.8439	0.6090
0.4519	65.0	8450	1.4800	0.5489
0.4833	66.0	8580	2.2109	0.5639
0.4582	67.0	8710	1.2669	0.5940
0.4616	68.0	8840	1.0607	0.6316
0.4803	69.0	8970	2.4072	0.4436
0.521	70.0	9100	6.1593	0.4812
0.4558	71.0	9230	1.0987	0.6391
0.4408	72.0	9360	1.2993	0.6466
0.4813	73.0	9490	0.9748	0.5714
0.4842	74.0	9620	4.6767	0.4812
0.4388	75.0	9750	4.1866	0.4662
0.4701	76.0	9880	2.3781	0.5564
0.4382	77.0	10010	1.8863	0.6165
0.4433	78.0	10140	3.5844	0.5789
0.4586	79.0	10270	3.0186	0.5940
0.4295	80.0	10400	3.8892	0.4662
0.5058	81.0	10530	12.1759	0.4962
0.435	82.0	10660	5.5538	0.6090
0.4462	83.0	10790	2.1082	0.5865
0.4602	84.0	10920	3.4000	0.6241
0.4575	85.0	11050	9.2871	0.5038
0.4461	86.0	11180	4.2447	0.5113
0.5138	87.0	11310	4.6263	0.5789
0.4321	88.0	11440	3.6092	0.4135
0.4572	89.0	11570	1.6996	0.6391
0.4329	90.0	11700	4.1432	0.5639
0.4427	91.0	11830	2.6578	0.4286
0.4536	92.0	11960	3.0237	0.5489
0.4072	93.0	12090	1.6931	0.4586
0.4225	94.0	12220	2.9963	0.4812
0.4277	95.0	12350	1.2454	0.5865
0.4753	96.0	12480	5.3971	0.5940
0.4367	97.0	12610	3.2193	0.6015
0.4375	98.0	12740	1.1401	0.6541
0.4197	99.0	12870	1.6494	0.5714
0.4517	100.0	13000	3.1656	0.5940

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

alem-147/poisoned-baseline

poisoned-baseline

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alem-147/
poisoned-baseline