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bart-large-xsum-finetuned-en-sum – AI Model by october-sd | AlphaNeural AI
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bart-large-xsum-finetuned-en-sum
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transformers
tensorboard
safetensors
bart
text2text-generation
summarization
generated_from_trainer
facebook/bart-large-xsum
finetune
mit
endpoints_compatible
us
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bart-large-xsum-finetuned-en-sum
This model is a fine-tuned version of
facebook/bart-large-xsum
on the None dataset. It achieves the following results on the evaluation set:
Loss: 1.5989
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-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
No log
0.99
126
1.6037
No log
1.98
252
1.5989
Framework versions
Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.15.0
Tokenizers 0.15.2
