layoutlmv3-finetuned-FUNSD
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README.md
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---
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license: cc-by-nc-sa-4.0
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base_model: microsoft/layoutlmv3-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: layoutlmv3-finetuned-FUNSD
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# layoutlmv3-finetuned-FUNSD
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This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6088
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- Precision: 0.9024
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- Recall: 0.9190
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- F1: 0.9107
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- Accuracy: 0.8544
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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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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- training_steps: 1000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.33 | 100 | 0.6659 | 0.7835 | 0.8217 | 0.8021 | 0.7825 |
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| No log | 2.67 | 200 | 0.5631 | 0.8229 | 0.8912 | 0.8557 | 0.7903 |
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| No log | 4.0 | 300 | 0.4653 | 0.8470 | 0.8992 | 0.8723 | 0.8389 |
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| No log | 5.33 | 400 | 0.5080 | 0.8526 | 0.9081 | 0.8795 | 0.8324 |
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| 0.5612 | 6.67 | 500 | 0.5200 | 0.8733 | 0.9036 | 0.8882 | 0.8429 |
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| 0.5612 | 8.0 | 600 | 0.5480 | 0.8878 | 0.9160 | 0.9017 | 0.8531 |
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| 0.5612 | 9.33 | 700 | 0.5655 | 0.8894 | 0.9146 | 0.9018 | 0.8521 |
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| 0.5612 | 10.67 | 800 | 0.5971 | 0.8943 | 0.9160 | 0.9050 | 0.8514 |
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| 0.5612 | 12.0 | 900 | 0.5873 | 0.9022 | 0.9215 | 0.9118 | 0.8583 |
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| 0.1425 | 13.33 | 1000 | 0.6088 | 0.9024 | 0.9190 | 0.9107 | 0.8544 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.1
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