bart-abs-2409-1947-lr-0.0003-bs-4-maxep-10 – AI Model by roequitz | AlphaNeural AI

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roequitz/
bart-abs-2409-1947-lr-0.0003-bs-4-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-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: 7.1633
Rouge/rouge1: 0.2439
Rouge/rouge2: 0.0504
Rouge/rougel: 0.2065
Rouge/rougelsum: 0.2067
Bertscore/bertscore-precision: 0.8544
Bertscore/bertscore-recall: 0.8581
Bertscore/bertscore-f1: 0.8562
Meteor: 0.229
Gen Len: 45.0

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: 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
3.3364	1.0	217	3.8952	0.2911	0.0832	0.2351	0.2361	0.8685	0.8711	0.8697	0.2279	43.0
2.369	2.0	434	4.0594	0.2603	0.0584	0.2204	0.2202	0.871	0.8545	0.8626	0.2129	35.0
1.4708	3.0	651	4.6061	0.2722	0.0714	0.2029	0.2031	0.8612	0.8618	0.8615	0.2582	45.0
0.9251	4.0	868	5.2239	0.2333	0.0475	0.1761	0.1762	0.8431	0.8562	0.8495	0.2342	58.8273
0.6367	5.0	1085	5.8193	0.2622	0.0744	0.2001	0.1997	0.8634	0.8616	0.8625	0.1982	32.0
0.486	6.0	1302	6.2975	0.2591	0.0557	0.2009	0.2012	0.859	0.8605	0.8597	0.2511	48.0091
0.3892	7.0	1519	6.5002	0.2582	0.0781	0.2156	0.2154	0.8771	0.8626	0.8697	0.1855	29.0
0.3152	8.0	1736	6.7352	0.313	0.0882	0.2413	0.2416	0.8789	0.8681	0.8735	0.2252	34.0
0.2751	9.0	1953	6.9970	0.2906	0.0847	0.2272	0.2274	0.8671	0.8567	0.8618	0.1991	27.0
0.24	10.0	2170	7.1633	0.2439	0.0504	0.2065	0.2067	0.8544	0.8581	0.8562	0.229	45.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1