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am-infoweb/MRR-NER-08-09-Layoutlmv3
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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: MRR-NER-08-09-Layoutlmv3
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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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# MRR-NER-08-09-Layoutlmv3
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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: 0.0175
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- Precision: 0.8367
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- Recall: 0.9111
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- F1: 0.8723
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- Accuracy: 0.9960
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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 | 8.33 | 100 | 0.2585 | 0.1667 | 0.0222 | 0.0392 | 0.9607 |
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| No log | 16.67 | 200 | 0.1281 | 0.4783 | 0.2444 | 0.3235 | 0.9727 |
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| No log | 25.0 | 300 | 0.0821 | 0.3696 | 0.3778 | 0.3736 | 0.9767 |
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| No log | 33.33 | 400 | 0.0493 | 0.5111 | 0.5111 | 0.5111 | 0.9813 |
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| 0.2244 | 41.67 | 500 | 0.0330 | 0.625 | 0.7778 | 0.6931 | 0.9913 |
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| 0.2244 | 50.0 | 600 | 0.0272 | 0.6909 | 0.8444 | 0.7600 | 0.9927 |
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| 0.2244 | 58.33 | 700 | 0.0218 | 0.7843 | 0.8889 | 0.8333 | 0.9953 |
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| 0.2244 | 66.67 | 800 | 0.0190 | 0.7547 | 0.8889 | 0.8163 | 0.9947 |
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| 0.2244 | 75.0 | 900 | 0.0158 | 0.8936 | 0.9333 | 0.9130 | 0.9973 |
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| 0.038 | 83.33 | 1000 | 0.0175 | 0.8367 | 0.9111 | 0.8723 | 0.9960 |
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### Framework versions
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- Transformers 4.34.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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- Tokenizers 0.13.3
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