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bart-large-cnn-xsum – AI Model by ckandrew04 | AlphaNeural AI
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bart-large-cnn-xsum
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transformers
safetensors
bart
text2text-generation
generated_from_trainer
xsum
facebook/bart-large-cnn
finetune
mit
endpoints_compatible
us
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bart-large-cnn-xsum
This model is a fine-tuned version of
facebook/bart-large-cnn
on the xsum dataset. It achieves the following results on the evaluation set:
Loss: 2.0314
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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
1.9458
0.3921
500
1.8663
1.7833
0.7842
1000
1.9308
1.3364
1.1762
1500
1.9378
1.3562
1.5683
2000
1.9538
1.3173
1.9604
2500
1.8672
0.9227
2.3525
3000
2.0590
0.8619
2.7446
3500
2.0314
Framework versions
Transformers 4.46.3
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3
