BART_1st_STAGE_SUMMARIZER_v3 – AI Model by jgibb | AlphaNeural AI

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jgibb/
BART_1st_STAGE_SUMMARIZER_v3

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text2text-generation

generated_from_trainer

facebook/bart-large

endpoints_compatible

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BART_1st_STAGE_SUMMARIZER_v3

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

Loss: 1.0858
Rouge1: 0.7512
Rouge2: 0.5341
Rougel: 0.6975
Rougelsum: 0.702
Wer: 0.3693

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: 2e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Wer
No log	0.21	250	1.3647	0.7195	0.4836	0.6589	0.6639	0.4174
1.954	0.42	500	1.2505	0.7301	0.4985	0.6698	0.6744	0.406
1.954	0.63	750	1.2094	0.7341	0.5051	0.6764	0.6799	0.3973
1.3635	0.84	1000	1.1771	0.7387	0.5144	0.6837	0.688	0.3902
1.3635	1.05	1250	1.1862	0.7422	0.5177	0.6853	0.6907	0.3892
1.2246	1.26	1500	1.1514	0.7416	0.5182	0.686	0.6905	0.3869
1.2246	1.48	1750	1.1415	0.7448	0.5219	0.6884	0.693	0.3837
1.1592	1.69	2000	1.1235	0.7459	0.5252	0.691	0.6953	0.38
1.1592	1.9	2250	1.1210	0.7481	0.5284	0.6921	0.697	0.3778
1.1084	2.11	2500	1.1074	0.7487	0.5295	0.6939	0.6987	0.376
1.1084	2.32	2750	1.0985	0.7505	0.5317	0.6961	0.7009	0.3738
1.0452	2.53	3000	1.0907	0.7507	0.5324	0.6962	0.7006	0.3718
1.0452	2.74	3250	1.0884	0.7512	0.5339	0.6973	0.7023	0.3702
1.0253	2.95	3500	1.0858	0.7512	0.5341	0.6975	0.702	0.3693

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2