bart-base-lora

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6884
Accuracy: 0.8172
Precision: 0.8132
Recall: 0.8172
Precision Macro: 0.7584
Recall Macro: 0.7412
Macro Fpr: 0.0164
Weighted Fpr: 0.0157
Weighted Specificity: 0.9755
Macro Specificity: 0.9862
Weighted Sensitivity: 0.8172
Macro Sensitivity: 0.7412
F1 Micro: 0.8172
F1 Macro: 0.7417
F1 Weighted: 0.8124

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
No log	1.0	160	0.9525	0.7157	0.6788	0.7157	0.3875	0.4416	0.0285	0.0276	0.9642	0.9787	0.7157	0.4416	0.7157	0.3958	0.6835
No log	2.0	321	0.7733	0.7413	0.7296	0.7413	0.4491	0.4687	0.0252	0.0243	0.9668	0.9805	0.7413	0.4687	0.7413	0.4337	0.7231
No log	3.0	482	0.7105	0.7738	0.7631	0.7738	0.5565	0.5408	0.0212	0.0205	0.9725	0.9831	0.7738	0.5408	0.7738	0.5271	0.7611
1.08	4.0	643	0.7539	0.7576	0.7584	0.7576	0.5791	0.5613	0.0234	0.0223	0.9681	0.9817	0.7576	0.5613	0.7576	0.5497	0.7438
1.08	5.0	803	0.6978	0.7831	0.7900	0.7831	0.7410	0.6492	0.0203	0.0194	0.9710	0.9836	0.7831	0.6492	0.7831	0.6354	0.7703
1.08	6.0	964	0.5920	0.8156	0.8053	0.8156	0.7051	0.6889	0.0166	0.0159	0.9746	0.9860	0.8156	0.6889	0.8156	0.6860	0.8088
0.5581	7.0	1125	0.6231	0.8187	0.8178	0.8187	0.7627	0.7425	0.0162	0.0156	0.9766	0.9864	0.8187	0.7425	0.8187	0.7393	0.8147
0.5581	8.0	1286	0.6291	0.8141	0.8134	0.8141	0.7636	0.7307	0.0167	0.0160	0.9758	0.9860	0.8141	0.7307	0.8141	0.7329	0.8089
0.5581	9.0	1446	0.6226	0.8242	0.8212	0.8242	0.7666	0.7340	0.0158	0.0150	0.9760	0.9867	0.8242	0.7340	0.8242	0.7365	0.8191
0.3924	10.0	1607	0.6728	0.8110	0.8123	0.8110	0.7418	0.7289	0.0170	0.0164	0.9762	0.9858	0.8110	0.7289	0.8110	0.7240	0.8048
0.3924	11.0	1768	0.6805	0.8095	0.8123	0.8095	0.7390	0.7303	0.0173	0.0165	0.9752	0.9856	0.8095	0.7303	0.8095	0.7263	0.8026
0.3924	12.0	1929	0.6710	0.8133	0.8137	0.8133	0.7396	0.7306	0.0168	0.0161	0.9759	0.9859	0.8133	0.7306	0.8133	0.7284	0.8090
0.2929	13.0	2089	0.6740	0.8187	0.8170	0.8187	0.7644	0.7360	0.0162	0.0156	0.9761	0.9863	0.8187	0.7360	0.8187	0.7368	0.8151
0.2929	14.0	2250	0.6823	0.8180	0.8159	0.8180	0.7657	0.7336	0.0164	0.0156	0.9753	0.9862	0.8180	0.7336	0.8180	0.7361	0.8137
0.2929	14.93	2400	0.6884	0.8172	0.8132	0.8172	0.7584	0.7412	0.0164	0.0157	0.9755	0.9862	0.8172	0.7412	0.8172	0.7417	0.8124

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.19.0
Tokenizers 0.15.1

xshubhamx
/

bart-large-lora

bart-base-lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for xshubhamx/bart-large-lora

Evaluation results