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HFS26/
bart-cnn-samsum-peft-trained

Name: bart-cnn-samsum-peft-trained
Availability: InStock

HFS26/bart-cnn-samsum-finetune-model

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bart-cnn-samsum-peft-trained

This model is a fine-tuned version of HFS26/bart-cnn-samsum-finetune-model on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1363

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
0.0971	1.0	19	0.1364
0.1004	2.0	38	0.1364
0.0919	3.0	57	0.1363
0.0938	4.0	76	0.1363
0.1085	5.0	95	0.1363

Framework versions

PEFT 0.18.1
Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.2
Tokenizers 0.22.2

HFS26/bart-cnn-samsum-peft-trained

bart-cnn-samsum-peft-trained

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

HFS26/
bart-cnn-samsum-peft-trained