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Fine-tuned Construction Receipt Model

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  1. README.md +17 -8
  2. model.safetensors +1 -1
  3. tokenizer.json +1 -3
  4. training_args.bin +2 -2
README.md CHANGED
@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [DanSarm/receipt-core-model](https://huggingface.co/DanSarm/receipt-core-model) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.8093
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  ## Model description
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@@ -35,22 +35,31 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
 
 
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | No log | 1.0 | 5 | 3.4726 |
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- | No log | 2.0 | 10 | 2.9864 |
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- | No log | 3.0 | 15 | 2.8093 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [DanSarm/receipt-core-model](https://huggingface.co/DanSarm/receipt-core-model) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2568
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | 2.911 | 1.0 | 2 | 2.5178 |
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+ | 2.0298 | 2.0 | 4 | 2.0128 |
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+ | 1.7239 | 3.0 | 6 | 1.7704 |
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+ | 1.5708 | 4.0 | 8 | 1.6107 |
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+ | 1.4319 | 5.0 | 10 | 1.5010 |
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+ | 1.3463 | 6.0 | 12 | 1.4165 |
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+ | 1.262 | 7.0 | 14 | 1.3495 |
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+ | 1.2137 | 8.0 | 16 | 1.3015 |
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+ | 1.1761 | 9.0 | 18 | 1.2705 |
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+ | 1.1369 | 10.0 | 20 | 1.2568 |
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  ### Framework versions
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