bart-xsum – AI Model by ryusangwon | AlphaNeural AI

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ryusangwon/
bart-xsum

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text2text-generation

generated_from_trainer

endpoints_compatible

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model

This model is a fine-tuned version of facebook/bart-large on the xsum dataset. It achieves the following results on the evaluation set:

Loss: 1.5537

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.9566	0.08	500	1.7765
1.9288	0.16	1000	1.7549
1.8561	0.24	1500	1.7462
1.7802	0.31	2000	1.6921
1.8444	0.39	2500	1.6699
1.8145	0.47	3000	1.6525
1.7736	0.55	3500	1.6313
1.7259	0.63	4000	1.6234
1.7028	0.71	4500	1.6217
1.7235	0.78	5000	1.5750
1.6534	0.86	5500	1.5749
1.6392	0.94	6000	1.5537

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.10.1
Tokenizers 0.13.2