bart-abs-2409-1947-lr-3e-06-bs-8-maxep-10 – AI Model by roequitz | AlphaNeural AI

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roequitz/
bart-abs-2409-1947-lr-3e-06-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-3e-06-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: 7.8363
Rouge/rouge1: 0.3097
Rouge/rouge2: 0.0856
Rouge/rougel: 0.2463
Rouge/rougelsum: 0.2464
Bertscore/bertscore-precision: 0.8589
Bertscore/bertscore-recall: 0.8656
Bertscore/bertscore-f1: 0.8622
Meteor: 0.2246
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: 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
0.1784	1.0	109	7.8164	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.1816	2.0	218	7.8349	0.2722	0.0714	0.2029	0.2031	0.8612	0.8618	0.8615	0.2582	44.0
0.1813	3.0	327	7.8471	0.2677	0.0541	0.2002	0.2007	0.8757	0.864	0.8697	0.25	37.0
0.1798	4.0	436	7.8598	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2	5.0	545	7.8568	0.2722	0.0714	0.2029	0.2031	0.8612	0.8618	0.8615	0.2582	44.0
0.2047	6.0	654	7.8458	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2066	7.0	763	7.8409	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2144	8.0	872	7.8382	0.2646	0.0434	0.203	0.2031	0.867	0.8616	0.8642	0.2187	36.0
0.2062	9.0	981	7.8368	0.2646	0.0434	0.203	0.2031	0.867	0.8616	0.8642	0.2187	36.0
0.2056	10.0	1090	7.8363	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1