bart-abs-1509-0313-lr-3e-06-bs-4-maxep-10

roequitz/bart-abs-1509-0313-lr-3e-06-bs-4-maxep-10

bart-abs-1509-0313-lr-3e-06-bs-4-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. --

Downloads · 30 days

14% of all-time downloads

All-time downloads

Public

Parameters

306M

1.2 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors1.2 GB · 100%

At a glance

License: apache-2.0
Model type: bart
Access: Public
Created: Sep 15, 2024
Updated: Sep 15, 2024
SHA: 460433d5

Base models

sshleifer/distilbart-xsum-12-6

Model card

From the Hugging Face model README

bart-abs-1509-0313-lr-3e-06-bs-4-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: 6.5518
Rouge/rouge1: 0.3111
Rouge/rouge2: 0.0793
Rouge/rougel: 0.2212
Rouge/rougelsum: 0.2213
Bertscore/bertscore-precision: 0.8659
Bertscore/bertscore-recall: 0.864
Bertscore/bertscore-f1: 0.8649
Meteor: 0.228
Gen Len: 36.0

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-06
train_batch_size: 4
eval_batch_size: 4
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
0.4698	1.0	217	6.0332	0.2584	0.0526	0.1868	0.1869	0.8466	0.8559	0.8512	0.2654	55.0
0.4866	2.0	434	6.1644	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.4497	3.0	651	6.2268	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.4248	4.0	868	6.3031	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.4054	5.0	1085	6.4024	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.3937	6.0	1302	6.4675	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.3833	7.0	1519	6.5040	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.3761	8.0	1736	6.5270	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.374	9.0	1953	6.5454	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0
0.3686	10.0	2170	6.5518	0.3111	0.0793	0.2212	0.2213	0.8659	0.864	0.8649	0.228	36.0

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