layoutlmv3-finetuned-full
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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-large
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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-full
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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-full
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This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0613
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- Precision: 0.9339
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- Recall: 0.9517
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- F1: 0.9427
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- Accuracy: 0.9888
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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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- gradient_accumulation_steps: 3
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- total_train_batch_size: 6
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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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- mixed_precision_training: Native AMP
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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 | 0.5201 | 250 | 0.3041 | 0.4864 | 0.5643 | 0.5225 | 0.9219 |
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| 0.4848 | 1.0416 | 500 | 0.1620 | 0.7495 | 0.8031 | 0.7753 | 0.9652 |
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| 0.4848 | 1.5617 | 750 | 0.1195 | 0.8386 | 0.8662 | 0.8522 | 0.9745 |
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| 0.1555 | 2.0832 | 1000 | 0.0996 | 0.8764 | 0.9025 | 0.8892 | 0.9790 |
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| 0.1555 | 2.6033 | 1250 | 0.0765 | 0.8984 | 0.9285 | 0.9132 | 0.9828 |
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| 0.0941 | 3.1248 | 1500 | 0.0662 | 0.9207 | 0.9387 | 0.9296 | 0.9864 |
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| 0.0941 | 3.6449 | 1750 | 0.0658 | 0.9361 | 0.9452 | 0.9406 | 0.9875 |
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| 0.0643 | 4.1664 | 2000 | 0.0630 | 0.9317 | 0.9508 | 0.9411 | 0.9886 |
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| 0.0643 | 4.6865 | 2250 | 0.0589 | 0.9338 | 0.9503 | 0.9420 | 0.9892 |
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| 0.0503 | 5.2080 | 2500 | 0.0613 | 0.9339 | 0.9517 | 0.9427 | 0.9888 |
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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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