layoutlmv3-finetuned-500
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README.md
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---
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library_name: transformers
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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-500
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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-500
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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: 0.2173
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- Precision: 0.6567
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- Recall: 0.7311
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- F1: 0.6919
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- Accuracy: 0.9491
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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: 5
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- eval_batch_size: 5
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- seed: 42
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- optimizer: Use OptimizerNames.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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- training_steps: 2500
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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 | 2.5 | 250 | 0.6612 | 0.1340 | 0.1849 | 0.1554 | 0.8477 |
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| 0.8157 | 5.0 | 500 | 0.4681 | 0.3127 | 0.3809 | 0.3435 | 0.8876 |
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| 0.8157 | 7.5 | 750 | 0.3601 | 0.3973 | 0.5049 | 0.4447 | 0.9125 |
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| 0.3587 | 10.0 | 1000 | 0.2979 | 0.5004 | 0.5945 | 0.5434 | 0.9268 |
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| 0.3587 | 12.5 | 1250 | 0.2673 | 0.5958 | 0.6660 | 0.6289 | 0.9386 |
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| 0.2313 | 15.0 | 1500 | 0.2444 | 0.6228 | 0.7041 | 0.6610 | 0.9437 |
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| 0.2313 | 17.5 | 1750 | 0.2317 | 0.6353 | 0.7185 | 0.6744 | 0.9453 |
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| 0.1717 | 20.0 | 2000 | 0.2224 | 0.6527 | 0.7227 | 0.6859 | 0.9485 |
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| 0.1717 | 22.5 | 2250 | 0.2191 | 0.6580 | 0.7255 | 0.6901 | 0.9481 |
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| 0.145 | 25.0 | 2500 | 0.2173 | 0.6567 | 0.7311 | 0.6919 | 0.9491 |
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.5.1+cu124
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- Datasets 3.3.2
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- Tokenizers 0.21.0
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