bart-abs-2409-1947-lr-0.0003-bs-8-maxep-10 – AI Model by roequitz | AlphaNeural AI

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roequitz/
bart-abs-2409-1947-lr-0.0003-bs-8-maxep-10

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text2text-generation

generated_from_trainer

sshleifer/distilbart-xsum-12-6

endpoints_compatible

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bart-abs-2409-1947-lr-0.0003-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: 7.5485
Rouge/rouge1: 0.2722
Rouge/rouge2: 0.0714
Rouge/rougel: 0.2029
Rouge/rougelsum: 0.2031
Bertscore/bertscore-precision: 0.8612
Bertscore/bertscore-recall: 0.8618
Bertscore/bertscore-f1: 0.8615
Meteor: 0.2582
Gen Len: 44.0

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: 0.0003
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
0.2977	1.0	109	7.2454	0.268	0.0433	0.2049	0.205	0.8657	0.8613	0.8635	0.2212	35.0
0.3147	2.0	218	7.0403	0.2684	0.0598	0.2092	0.2091	0.8595	0.8599	0.8596	0.2363	41.0
0.5099	3.0	327	7.0211	0.2684	0.0598	0.2092	0.2091	0.8595	0.8599	0.8596	0.2363	41.0
0.2802	4.0	436	7.1107	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2652	5.0	545	7.1987	0.2622	0.0744	0.2001	0.1997	0.8634	0.8616	0.8625	0.1982	32.0
0.255	6.0	654	7.1719	0.2591	0.0557	0.2009	0.2012	0.859	0.8605	0.8597	0.2511	47.0
0.2379	7.0	763	7.1521	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2375	8.0	872	7.3633	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.219	9.0	981	7.4382	0.3089	0.0693	0.2341	0.2343	0.8686	0.8645	0.8665	0.2205	36.0
0.2091	10.0	1090	7.5485	0.2722	0.0714	0.2029	0.2031	0.8612	0.8618	0.8615	0.2582	44.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1