bart-abs-1509-0313-lr-3e-05-bs-8-maxep-10

roequitz/bart-abs-1509-0313-lr-3e-05-bs-8-maxep-10

bart-abs-1509-0313-lr-3e-05-bs-8-maxep-10 is a machine learning model from roequitz. 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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At a glance

License: apache-2.0
Model type: bart
Access: Public
Created: Sep 15, 2024
Updated: Sep 15, 2024
SHA: e120b9ea

Base models

sshleifer/distilbart-xsum-12-6

Model card

From the Hugging Face model README

bart-abs-1509-0313-lr-3e-05-bs-8-maxep-10

This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.7689
Rouge/rouge1: 0.4647
Rouge/rouge2: 0.2065
Rouge/rougel: 0.3953
Rouge/rougelsum: 0.3967
Bertscore/bertscore-precision: 0.8961
Bertscore/bertscore-recall: 0.8941
Bertscore/bertscore-f1: 0.895
Meteor: 0.4195
Gen Len: 37.9545

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge/rouge1	Rouge/rouge2	Rouge/rougel	Rouge/rougelsum	Bertscore/bertscore-precision	Bertscore/bertscore-recall	Bertscore/bertscore-f1	Meteor	Gen Len
0.332	1.0	109	2.9323	0.4383	0.1841	0.3691	0.3701	0.8915	0.8887	0.8899	0.3951	37.2182
0.3384	2.0	218	3.0419	0.4611	0.2038	0.3901	0.3918	0.8941	0.8913	0.8925	0.4163	37.1909
0.2354	3.0	327	3.2793	0.445	0.1903	0.3776	0.3785	0.8938	0.8895	0.8915	0.394	36.4545
0.1736	4.0	436	3.4093	0.4545	0.2	0.3877	0.3885	0.8939	0.8921	0.8928	0.4094	38.3818
0.1406	5.0	545	3.5183	0.4634	0.2065	0.394	0.3945	0.898	0.8925	0.8951	0.4032	35.5182
0.1108	6.0	654	3.6131	0.4667	0.2075	0.3961	0.3964	0.8966	0.8936	0.8949	0.4155	37.4364
0.0906	7.0	763	3.6935	0.4602	0.2002	0.3892	0.3903	0.8922	0.8944	0.8931	0.4116	39.6
0.0788	8.0	872	3.7280	0.4704	0.212	0.4	0.4018	0.8941	0.8955	0.8946	0.431	39.8727
0.0708	9.0	981	3.7601	0.468	0.2062	0.3979	0.3994	0.8953	0.8948	0.8949	0.4244	38.9727
0.0637	10.0	1090	3.7689	0.4647	0.2065	0.3953	0.3967	0.8961	0.8941	0.895	0.4195	37.9545

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Type: bart
License: apache-2.0
Created: Sep 15, 2024
Updated: Sep 15, 2024