learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.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: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Micro Precision	Micro Recall	Micro F1	Macro Precision	Macro Recall	Macro F1	Bleu	Rouge1	Rouge2
4.8047	0.2404	50	2.1144	0.1810	0.1332	0.1535	0.0870	0.1541	0.1112	0.6815	0.7396	0.4212
1.7948	0.4808	100	0.8959	0.1842	0.1384	0.1581	0.0894	0.1631	0.1155	0.6860	0.7497	0.4381
0.7301	0.7212	150	0.2402	0.2187	0.0926	0.1301	0.1095	0.0917	0.0998	0.7092	0.7108	0.4486
0.22	0.9615	200	0.0950	0.2541	0.0812	0.1230	0.3683	0.0905	0.1453	0.7932	0.7730	0.4542
0.1005	1.2019	250	0.0699	0.2168	0.1290	0.1618	0.3337	0.1228	0.1796	0.7490	0.7693	0.4552
0.0814	1.4423	300	0.0750	0.1692	0.0468	0.0733	0.1176	0.0407	0.0605	0.8194	0.7906	0.4550
0.075	1.6827	350	0.0681	0.2938	0.0593	0.0987	0.1469	0.0486	0.0731	0.8457	0.8197	0.4467
0.08	1.9231	400	0.0682	0.2427	0.1041	0.1457	0.2116	0.1018	0.1375	0.7777	0.7904	0.4945
0.0712	2.1635	450	0.0682	0.3137	0.0166	0.0316	0.2348	0.0147	0.0277	0.8509	0.7969	0.4711
0.0706	2.4038	500	0.0632	0.3026	0.0239	0.0444	0.2769	0.0211	0.0392	0.8550	0.8220	0.5130
0.0677	2.6442	550	0.0642	0.1622	0.0062	0.0120	0.1548	0.0055	0.0105	0.8520	0.8070	0.4974
0.0664	2.8846	600	0.0604	0.3846	0.0052	0.0103	0.4167	0.0050	0.0098	0.8548	0.8162	0.5217
0.0657	3.125	650	0.0613	0.2763	0.0219	0.0405	0.2929	0.0218	0.0405	0.8583	0.8304	0.5329
0.0634	3.3654	700	0.0608	0.1786	0.0052	0.0101	0.1548	0.0047	0.0092	0.8513	0.8091	0.5055
0.0627	3.6058	750	0.0568	0.3265	0.0166	0.0317	0.3129	0.0147	0.0280	0.8463	0.8097	0.5113
0.0595	3.8462	800	0.0572	0.1	0.0010	0.0021	0.05	0.0007	0.0014	0.8508	0.8198	0.5226
0.0603	4.0865	850	0.0562	0.2381	0.0156	0.0293	0.2455	0.0153	0.0288	0.8528	0.8172	0.5253
0.0588	4.3269	900	0.0565	0.2955	0.0135	0.0259	0.3193	0.0132	0.0253	0.8559	0.8178	0.5243
0.06	4.5673	950	0.0565	0.2931	0.0177	0.0334	0.3056	0.0167	0.0317	0.8559	0.8159	0.5255
0.0614	4.8077	1000	0.0560	0.2456	0.0146	0.0275	0.2301	0.0132	0.0251	0.8555	0.8184	0.5282

Framework versions

Transformers 4.51.1
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0

tlam25/bart_finetuned_clarify_aspects

bart_finetuned_clarify_aspects

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tlam25/
bart_finetuned_clarify_aspects