bart-abs-1509-0313-lr-3e-05-bs-2-maxep-10

roequitz/bart-abs-1509-0313-lr-3e-05-bs-2-maxep-10

bart-abs-1509-0313-lr-3e-05-bs-2-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: 26ec202c

Base models

sshleifer/distilbart-xsum-12-6

Model card

From the Hugging Face model README

bart-abs-1509-0313-lr-3e-05-bs-2-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: 4.0180
Rouge/rouge1: 0.4724
Rouge/rouge2: 0.2094
Rouge/rougel: 0.3964
Rouge/rougelsum: 0.3976
Bertscore/bertscore-precision: 0.8964
Bertscore/bertscore-recall: 0.8932
Bertscore/bertscore-f1: 0.8947
Meteor: 0.4217
Gen Len: 36.8818

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: 2
eval_batch_size: 2
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
0.6813	1.0	434	2.4588	0.4636	0.2101	0.3907	0.3921	0.8975	0.8904	0.8937	0.4099	35.0727
0.6702	2.0	868	2.5377	0.4448	0.1862	0.3725	0.3735	0.8942	0.8887	0.8913	0.3825	35.7273
0.4591	3.0	1302	2.8762	0.4533	0.1916	0.3767	0.3778	0.8961	0.8897	0.8928	0.3911	35.2091
0.3165	4.0	1736	3.2129	0.4519	0.1976	0.3803	0.3806	0.8936	0.891	0.8922	0.4023	37.6364
0.2222	5.0	2170	3.4971	0.47	0.2049	0.392	0.3924	0.8959	0.8926	0.8941	0.4107	36.5545
0.1596	6.0	2604	3.6405	0.4607	0.2101	0.3853	0.3879	0.8943	0.8908	0.8924	0.4021	37.2273
0.1166	7.0	3038	3.7827	0.4759	0.2191	0.4086	0.4106	0.8988	0.8928	0.8956	0.4173	35.5727
0.0891	8.0	3472	3.9388	0.4677	0.2047	0.3905	0.3925	0.8933	0.8927	0.8929	0.417	38.7
0.0695	9.0	3906	3.9583	0.4775	0.2116	0.4032	0.4051	0.8981	0.8931	0.8955	0.4228	36.3182
0.0592	10.0	4340	4.0180	0.4724	0.2094	0.3964	0.3976	0.8964	0.8932	0.8947	0.4217	36.8818

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