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README.md
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tags:
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- generated_from_trainer
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datasets:
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-
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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:
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type:
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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.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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- name: Accuracy
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type: accuracy
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value: 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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# pasha
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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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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