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nid-ocr-vit-mbart50-2 – AI Model by kavinh07 | AlphaNeural AI
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nid-ocr-vit-mbart50-2
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transformers
tensorboard
safetensors
vision-encoder-decoder
image-text-to-text
generated_from_trainer
endpoints_compatible
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nid-ocr-vit-mbart50-2
This model is a fine-tuned version of
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 27.6543
eval_cer: 8.3999
eval_wer: 2.2781
eval_runtime: 775.0458
eval_samples_per_second: 0.516
eval_steps_per_second: 0.032
epoch: 0
step: 0
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: 16
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 8
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: cosine
lr_scheduler_warmup_steps: 2000
num_epochs: 1000
Framework versions
Transformers 4.54.1
Pytorch 2.7.1+cu126
Datasets 4.5.0
Tokenizers 0.21.4
