bart-base-lora

xshubhamx/bart-base-lora

bart-base-lora is a machine learning model from xshubhamx. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

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

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Public

Updated Apr 21, 2024

Repo size

602 MB

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Hugging Face

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At a glance

License: apache-2.0
Access: Public
Created: Apr 21, 2024
Updated: Apr 21, 2024
SHA: 9d2f03fd

Base models

facebook/bart-base

Model card

From the Hugging Face model README

bart-base-lora

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

Loss: 0.6655
Accuracy: 0.7963
Precision: 0.7841
Recall: 0.7963
Precision Macro: 0.5968
Recall Macro: 0.6325
Macro Fpr: 0.0186
Weighted Fpr: 0.0179
Weighted Specificity: 0.9749
Macro Specificity: 0.9847
Weighted Sensitivity: 0.7963
Macro Sensitivity: 0.6325
F1 Micro: 0.7963
F1 Macro: 0.6074
F1 Weighted: 0.7859

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	1.2642	0.6313	0.5477	0.6313	0.3009	0.3127	0.0428	0.0400	0.9351	0.9711	0.6313	0.3127	0.6313	0.2941	0.5769
No log	2.0	321	0.8962	0.7119	0.6939	0.7119	0.3937	0.4525	0.0285	0.0281	0.9669	0.9786	0.7119	0.4525	0.7119	0.4107	0.6960
No log	3.0	482	0.8204	0.7196	0.6953	0.7196	0.3974	0.4468	0.0278	0.0271	0.9653	0.9790	0.7196	0.4468	0.7196	0.3998	0.6885
1.2731	4.0	643	0.7519	0.7436	0.7186	0.7436	0.4131	0.4673	0.0244	0.0240	0.9695	0.9809	0.7436	0.4673	0.7436	0.4272	0.7248
1.2731	5.0	803	0.7364	0.7475	0.7524	0.7475	0.6132	0.5050	0.0243	0.0236	0.9679	0.9810	0.7475	0.5050	0.7475	0.4905	0.7286
1.2731	6.0	964	0.7273	0.7514	0.7423	0.7514	0.5784	0.5258	0.0237	0.0231	0.9699	0.9814	0.7514	0.5258	0.7514	0.5150	0.7311
0.7243	7.0	1125	0.6993	0.7645	0.7478	0.7645	0.5498	0.5565	0.0222	0.0215	0.9721	0.9824	0.7645	0.5565	0.7645	0.5453	0.7538
0.7243	8.0	1286	0.6952	0.7769	0.7639	0.7769	0.5682	0.5888	0.0207	0.0201	0.9731	0.9833	0.7769	0.5888	0.7769	0.5700	0.7649
0.7243	9.0	1446	0.6759	0.7823	0.7708	0.7823	0.5764	0.5877	0.0201	0.0195	0.9739	0.9838	0.7823	0.5877	0.7823	0.5699	0.7697
0.6098	10.0	1607	0.6705	0.7847	0.7720	0.7847	0.5899	0.6176	0.0199	0.0192	0.9732	0.9839	0.7847	0.6176	0.7847	0.5935	0.7724
0.6098	11.0	1768	0.6794	0.7909	0.7737	0.7909	0.5882	0.6237	0.0193	0.0185	0.9736	0.9843	0.7909	0.6237	0.7909	0.5988	0.7773
0.6098	12.0	1929	0.6836	0.7909	0.7816	0.7909	0.5973	0.6285	0.0192	0.0185	0.9742	0.9843	0.7909	0.6285	0.7909	0.6034	0.7802
0.5239	13.0	2089	0.6508	0.7932	0.7783	0.7932	0.5965	0.6273	0.0189	0.0183	0.9738	0.9845	0.7932	0.6273	0.7932	0.6046	0.7821
0.5239	14.0	2250	0.6588	0.7963	0.7823	0.7963	0.5957	0.6290	0.0186	0.0179	0.9746	0.9847	0.7963	0.6290	0.7963	0.6055	0.7852
0.5239	14.93	2400	0.6655	0.7963	0.7841	0.7963	0.5968	0.6325	0.0186	0.0179	0.9749	0.9847	0.7963	0.6325	0.7963	0.6074	0.7859

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1

License: apache-2.0
Created: Apr 21, 2024
Updated: Apr 21, 2024