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BART-Finetuned-sum-AP – AI Model by NazzX1 | AlphaNeural AI
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BART-Finetuned-sum-AP
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transformers
tensorboard
safetensors
bart
text2text-generation
generated_from_trainer
facebook/bart-large-cnn
finetune
mit
endpoints_compatible
us
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BART-Finetuned-sum-AP
This model is a fine-tuned version of
facebook/bart-large-cnn
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.3444
Rouge1: 0.5646
Rouge2: 0.2825
Rougel: 0.3940
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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Rouge1
Rouge2
Rougel
0.3909
1.0
1600
0.3634
0.5494
0.2700
0.3799
0.3026
2.0
3200
0.3444
0.5646
0.2825
0.3940
Framework versions
Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1
