bart-abs-2409-1947-lr-3e-06-bs-2-maxep-6 – AI Model by roequitz | AlphaNeural AI

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roequitz/
bart-abs-2409-1947-lr-3e-06-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-3e-06-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: 7.7377
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: 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
0.2033	1.0	434	7.5938	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2002	2.0	868	7.6593	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.1987	3.0	1302	7.6900	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.1973	4.0	1736	7.7234	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.1975	5.0	2170	7.7388	0.3097	0.0856	0.2463	0.2464	0.8589	0.8656	0.8622	0.2246	36.0
0.2146	6.0	2604	7.7377	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