ainize-kobart-news-eb-finetuned-xsum – AI Model by eunbeee | AlphaNeural AI

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eunbeee/
ainize-kobart-news-eb-finetuned-xsum

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ainize-kobart-news-eb-finetuned-xsum

This model is a fine-tuned version of ainize/kobart-news on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2147
Rouge1: 60.732
Rouge2: 39.1933
Rougel: 60.6507
Rougelsum: 60.6712
Gen Len: 19.3417

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: 2e-05
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: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.0649	1.0	749	0.5502	56.6571	36.5992	56.6185	56.6364	19.2929
0.7103	2.0	1498	0.3904	59.1212	38.3611	59.093	59.1191	19.31
0.4723	3.0	2247	0.2922	60.1133	38.7819	60.0439	60.0572	19.2659
0.3841	4.0	2996	0.2367	60.4405	39.0176	60.366	60.4057	19.3397
0.3091	5.0	3745	0.2147	60.732	39.1933	60.6507	60.6712	19.3417

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1