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End of training

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  1. README.md +9 -5
  2. model.safetensors +1 -1
README.md CHANGED
@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9991610738255033
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0081
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- - Accuracy: 0.9992
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  ## Model description
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@@ -60,13 +60,17 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 1
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | 0.0126 | 0.9954 | 162 | 0.0081 | 0.9992 |
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 1.0
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0013
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+ - Accuracy: 1.0
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.0167 | 0.9954 | 162 | 0.0135 | 0.9966 |
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+ | 0.0032 | 1.9969 | 325 | 0.0030 | 0.9992 |
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+ | 0.0038 | 2.9985 | 488 | 0.0021 | 0.9992 |
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+ | 0.0022 | 4.0 | 651 | 0.0013 | 1.0 |
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+ | 0.0016 | 4.9770 | 810 | 0.0013 | 1.0 |
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  ### Framework versions
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