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ucla-nb-project/
bart-finetuned

Name: bart-finetuned
Availability: InStock

generated_from_trainer

datasets/all_binary_and_xe_ey_fae_counterfactual

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bart-base-finetuned-xe_ey_fae

This model is a fine-tuned version of facebook/bart-base on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set:

Loss: 1.3945
Accuracy: 0.7180

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 100
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.4226	0.06	500	3.8138	0.3628
4.0408	0.12	1000	3.0576	0.4630
3.4979	0.18	1500	2.7016	0.5133
3.1691	0.24	2000	2.4880	0.5431
2.9564	0.3	2500	2.3309	0.5644
2.8078	0.35	3000	2.2320	0.5792
2.6741	0.41	3500	2.1506	0.5924
2.5323	0.47	4000	1.9846	0.6176
2.3678	0.53	4500	1.8813	0.6375
2.25	0.59	5000	1.8100	0.6497
2.1795	0.65	5500	1.7632	0.6579
2.1203	0.71	6000	1.7238	0.6646
2.0764	0.77	6500	1.6856	0.6713
2.026	0.83	7000	1.6569	0.6760
1.9942	0.89	7500	1.6309	0.6803
1.9665	0.95	8000	1.6122	0.6836
1.9395	1.0	8500	1.5913	0.6866
1.9155	1.06	9000	1.5758	0.6895
1.8828	1.12	9500	1.5607	0.6918
1.8721	1.18	10000	1.5422	0.6948
1.8474	1.24	10500	1.5320	0.6964
1.8293	1.3	11000	1.5214	0.6978
1.8129	1.36	11500	1.5102	0.6998
1.8148	1.42	12000	1.5010	0.7013
1.7903	1.48	12500	1.4844	0.7038
1.7815	1.54	13000	1.4823	0.7039
1.7637	1.6	13500	1.4746	0.7052
1.7623	1.66	14000	1.4701	0.7061
1.7402	1.71	14500	1.4598	0.7076
1.7376	1.77	15000	1.4519	0.7090
1.7287	1.83	15500	1.4501	0.7101
1.7273	1.89	16000	1.4409	0.7107
1.7119	1.95	16500	1.4314	0.7125
1.7098	2.01	17000	1.4269	0.7129
1.6978	2.07	17500	1.4275	0.7132
1.698	2.13	18000	1.4218	0.7140
1.6837	2.19	18500	1.4151	0.7147
1.6908	2.25	19000	1.4137	0.7149
1.6902	2.31	19500	1.4085	0.7161
1.6741	2.36	20000	1.4121	0.7154
1.6823	2.42	20500	1.4037	0.7165
1.6692	2.48	21000	1.4039	0.7164
1.6669	2.54	21500	1.4015	0.7172
1.6613	2.6	22000	1.3979	0.7179
1.664	2.66	22500	1.3960	0.7180
1.6615	2.72	23000	1.4012	0.7172
1.6627	2.78	23500	1.3974	0.7178
1.6489	2.84	24000	1.3948	0.7182
1.6429	2.9	24500	1.3921	0.7184
1.6477	2.96	25000	1.3910	0.7182

Framework versions

Transformers 4.36.2
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

ucla-nb-project/bart-finetuned

bart-base-finetuned-xe_ey_fae

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ucla-nb-project/
bart-finetuned