bart-abs-2409-1947-lr-0.0003-bs-2-maxep-6 – AI Model by roequitz | AlphaNeural AI

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roequitz/
bart-abs-2409-1947-lr-0.0003-bs-2-maxep-6

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text2text-generation

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sshleifer/distilbart-xsum-12-6

endpoints_compatible

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bart-abs-2409-1947-lr-0.0003-bs-2-maxep-6

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: 7.1441
Rouge/rouge1: 0.3035
Rouge/rouge2: 0.072
Rouge/rougel: 0.2428
Rouge/rougelsum: 0.2429
Bertscore/bertscore-precision: 0.8724
Bertscore/bertscore-recall: 0.8571
Bertscore/bertscore-f1: 0.8646
Meteor: 0.2108
Gen Len: 29.0

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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6
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
0.955	1.0	434	5.7740	0.2485	0.0542	0.1867	0.1867	0.8491	0.8557	0.8523	0.2552	54.0
0.8434	2.0	868	5.6927	0.2599	0.0592	0.202	0.2017	0.8553	0.8626	0.8589	0.2583	46.0
0.5948	3.0	1302	5.8907	0.2473	0.0528	0.1834	0.1833	0.8458	0.856	0.8508	0.2569	54.0
0.4377	4.0	1736	6.5736	0.2591	0.0557	0.2009	0.2012	0.859	0.8605	0.8597	0.2511	48.0
0.3416	5.0	2170	6.8597	0.3035	0.072	0.2428	0.2429	0.8724	0.8571	0.8646	0.2108	29.0
0.268	6.0	2604	7.1441	0.3035	0.072	0.2428	0.2429	0.8724	0.8571	0.8646	0.2108	29.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.0
Datasets 3.0.0
Tokenizers 0.19.1