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mbart-finetuning – AI Model by navyanani | AlphaNeural AI
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navyanani
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mbart-finetuning
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transformers
safetensors
mbart
text2text-generation
generated_from_trainer
facebook/mbart-large-50
finetune
mit
endpoints_compatible
us
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mbart-finetuning
This model is a fine-tuned version of
facebook/mbart-large-50
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 7.1769
Bleu: 0.6204
Rouge1: 0.0165
Rouge2: 0.0048
Rougel: 0.0166
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: 8
eval_batch_size: 8
seed: 42
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: 3
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Bleu
Rouge1
Rouge2
Rougel
0.2852
1.0
6250
5.5122
0.6518
0.0256
0.0069
0.0251
0.1242
2.0
12500
6.1501
0.7109
0.0197
0.0049
0.0199
0.0578
3.0
18750
7.1769
0.6204
0.0165
0.0048
0.0166
Framework versions
Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1
