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BART-SUMMARIZATION-2 – AI Model by mzizo4110 | AlphaNeural AI
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BART-SUMMARIZATION-2
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peft
safetensors
generated_from_trainer
facebook/bart-large-cnn
adapter
mit
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BART-SUMMARIZATION-2
This model is a fine-tuned version of
facebook/bart-large-cnn
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.4067
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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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: 1
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
1.5489
0.1470
250
1.4343
1.5375
0.2940
500
1.4284
1.5258
0.4410
750
1.4237
1.5197
0.5881
1000
1.4175
1.5166
0.7351
1250
1.4130
1.5125
0.8821
1500
1.4067
Framework versions
PEFT 0.14.0
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1
