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update model card README.md

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  1. README.md +13 -13
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@@ -3,7 +3,7 @@ license: cc-by-nc-sa-4.0
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  tags:
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  - generated_from_trainer
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  datasets:
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- - pasha
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  metrics:
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  - precision
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  - recall
@@ -16,24 +16,24 @@ model-index:
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  name: Token Classification
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  type: token-classification
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  dataset:
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- name: pasha
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- type: pasha
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  config: pasha
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  split: test
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  args: pasha
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.984930032292788
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  - name: Recall
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  type: recall
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- value: 0.9888328530259366
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  - name: F1
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  type: f1
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- value: 0.9868775840373899
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  - name: Accuracy
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  type: accuracy
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- value: 0.990115718418515
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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
@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # pasha
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- This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the pasha dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0571
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- - Precision: 0.9849
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- - Recall: 0.9888
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- - F1: 0.9869
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- - Accuracy: 0.9901
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  ## Model description
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  tags:
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  - generated_from_trainer
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  datasets:
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+ - nielsr/funsd-layoutlmv3
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  metrics:
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  - precision
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  - recall
 
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  name: Token Classification
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  type: token-classification
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  dataset:
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+ name: nielsr/funsd-layoutlmv3
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+ type: nielsr/funsd-layoutlmv3
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  config: pasha
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  split: test
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  args: pasha
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.986704994610133
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  - name: Recall
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  type: recall
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+ value: 0.989193083573487
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  - name: F1
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  type: f1
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+ value: 0.9879474725670084
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9905978784956606
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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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  # pasha
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0585
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+ - Precision: 0.9867
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+ - Recall: 0.9892
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+ - F1: 0.9879
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+ - Accuracy: 0.9906
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  ## Model description
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