finetuned_bart_mnli

CoCoRooXin/finetuned_bart_mnli

finetuned_bart_mnli is a machine learning model from CoCoRooXin. 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 peft. The card lists the license as mit.

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

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Hugging Face

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At a glance

Library: peft
License: mit
Access: Public
Created: Mar 25, 2024
Updated: Mar 25, 2024
SHA: ef2ff0cd

Base models

facebook/bart-large-mnli

Model card

From the Hugging Face model README

results

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

Loss: 0.1726
F1: 0.9412
Precision: 0.9524
Recall: 0.9302
Accuracy: 0.9333

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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall	Accuracy
0.5809	0.14	10	0.3912	0.8671	0.8621	0.8721	0.8467
0.3659	0.28	20	0.3689	0.8790	0.9718	0.8023	0.8733
0.3805	0.42	30	0.2890	0.9133	0.9080	0.9186	0.9
0.4068	0.56	40	0.3249	0.9068	0.9733	0.8488	0.9
0.3183	0.69	50	0.2801	0.9133	0.9080	0.9186	0.9
0.1929	0.83	60	0.2832	0.9123	0.9176	0.9070	0.9
0.2861	0.97	70	0.2883	0.9195	0.9091	0.9302	0.9067
0.209	1.11	80	0.3000	0.9222	0.9506	0.8953	0.9133
0.2192	1.25	90	0.2845	0.9176	0.9286	0.9070	0.9067
0.3116	1.39	100	0.2520	0.9249	0.9195	0.9302	0.9133
0.2512	1.53	110	0.2650	0.9222	0.9506	0.8953	0.9133
0.1774	1.67	120	0.2571	0.9231	0.9398	0.9070	0.9133
0.1126	1.81	130	0.2668	0.9364	0.9310	0.9419	0.9267
0.2379	1.94	140	0.3075	0.9012	0.9605	0.8488	0.8933
0.2753	2.08	150	0.2254	0.9240	0.9294	0.9186	0.9133
0.1727	2.22	160	0.2707	0.9310	0.9205	0.9419	0.92
0.224	2.36	170	0.3118	0.9057	0.9863	0.8372	0.9
0.2056	2.5	180	0.2673	0.9302	0.9302	0.9302	0.92
0.2274	2.64	190	0.2515	0.9302	0.9302	0.9302	0.92
0.1193	2.78	200	0.2250	0.9357	0.9412	0.9302	0.9267
0.2806	2.92	210	0.2268	0.9286	0.9512	0.9070	0.92
0.1272	3.06	220	0.2031	0.9349	0.9518	0.9186	0.9267
0.1879	3.19	230	0.1730	0.9480	0.9425	0.9535	0.94
0.1341	3.33	240	0.1867	0.9419	0.9419	0.9419	0.9333
0.1376	3.47	250	0.2628	0.9341	0.9630	0.9070	0.9267
0.1599	3.61	260	0.2484	0.9405	0.9634	0.9186	0.9333
0.1899	3.75	270	0.1847	0.9480	0.9425	0.9535	0.94
0.0828	3.89	280	0.1869	0.9412	0.9524	0.9302	0.9333
0.1025	4.03	290	0.1876	0.9349	0.9518	0.9186	0.9267
0.118	4.17	300	0.1811	0.9419	0.9419	0.9419	0.9333
0.1475	4.31	310	0.1901	0.9294	0.9405	0.9186	0.92
0.1354	4.44	320	0.1805	0.9357	0.9412	0.9302	0.9267
0.1444	4.58	330	0.1706	0.9540	0.9432	0.9651	0.9467
0.1068	4.72	340	0.1693	0.9480	0.9425	0.9535	0.94
0.0875	4.86	350	0.1715	0.9412	0.9524	0.9302	0.9333
0.0922	5.0	360	0.1726	0.9412	0.9524	0.9302	0.9333

Framework versions

PEFT 0.9.0
Transformers 4.39.1
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Library: peft
License: mit
Created: Mar 25, 2024
Updated: Mar 25, 2024