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--- |
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license: cc-by-nc-sa-4.0 |
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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: LayoutLM_5 |
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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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# LayoutLM_5 |
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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: 0.3586 |
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- Precision: 0.8344 |
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- Recall: 0.8344 |
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- F1: 0.8344 |
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- Accuracy: 0.9343 |
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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-06 |
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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: 2000 |
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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 | 3.7 | 100 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.7818 | |
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| No log | 7.41 | 200 | 0.6214 | 0.7857 | 0.0728 | 0.1333 | 0.8 | |
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| No log | 11.11 | 300 | 0.4714 | 0.7303 | 0.4305 | 0.5417 | 0.8657 | |
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| No log | 14.81 | 400 | 0.4046 | 0.7955 | 0.6954 | 0.7420 | 0.9189 | |
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| 0.6176 | 18.52 | 500 | 0.3755 | 0.8194 | 0.7815 | 0.8000 | 0.9301 | |
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| 0.6176 | 22.22 | 600 | 0.3611 | 0.7935 | 0.8146 | 0.8039 | 0.9245 | |
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| 0.6176 | 25.93 | 700 | 0.3679 | 0.7848 | 0.8212 | 0.8026 | 0.9245 | |
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| 0.6176 | 29.63 | 800 | 0.3292 | 0.8289 | 0.8344 | 0.8317 | 0.9357 | |
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| 0.6176 | 33.33 | 900 | 0.3408 | 0.8289 | 0.8344 | 0.8317 | 0.9315 | |
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| 0.1555 | 37.04 | 1000 | 0.3479 | 0.8141 | 0.8411 | 0.8274 | 0.9315 | |
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| 0.1555 | 40.74 | 1100 | 0.3491 | 0.8247 | 0.8411 | 0.8328 | 0.9357 | |
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| 0.1555 | 44.44 | 1200 | 0.3704 | 0.7888 | 0.8411 | 0.8141 | 0.9245 | |
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| 0.1555 | 48.15 | 1300 | 0.3591 | 0.8194 | 0.8411 | 0.8301 | 0.9315 | |
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| 0.1555 | 51.85 | 1400 | 0.3420 | 0.8344 | 0.8344 | 0.8344 | 0.9343 | |
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| 0.0746 | 55.56 | 1500 | 0.3546 | 0.8421 | 0.8477 | 0.8449 | 0.9357 | |
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| 0.0746 | 59.26 | 1600 | 0.3442 | 0.8421 | 0.8477 | 0.8449 | 0.9371 | |
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| 0.0746 | 62.96 | 1700 | 0.3687 | 0.8205 | 0.8477 | 0.8339 | 0.9357 | |
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| 0.0746 | 66.67 | 1800 | 0.3743 | 0.8258 | 0.8477 | 0.8366 | 0.9343 | |
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| 0.0746 | 70.37 | 1900 | 0.3626 | 0.8301 | 0.8411 | 0.8355 | 0.9343 | |
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| 0.0502 | 74.07 | 2000 | 0.3586 | 0.8344 | 0.8344 | 0.8344 | 0.9343 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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