bart-finetuned

ucla-nb-project/bart-finetuned

bart-finetuned is a machine learning model from ucla-nb-project. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: bart
Access: Public
Created: May 18, 2024
Updated: May 18, 2024
SHA: 52d93541

Base models

facebook/bart-base

Datasets

datasets/all_binary_and_xe_ey_fae_counterfactual

Model card

From the Hugging Face model README

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

Library: transformers
Type: bart
Created: May 18, 2024
Updated: May 18, 2024