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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: LayoutLMv3_5_entities |
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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_5_entities |
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This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1966 |
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- Precision: 0.8679 |
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- Recall: 0.8519 |
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- F1: 0.8598 |
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- Accuracy: 0.9772 |
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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: 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 | 2.56 | 100 | 0.1209 | 0.8602 | 0.7407 | 0.7960 | 0.9666 | |
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| No log | 5.13 | 200 | 0.1267 | 0.8365 | 0.8056 | 0.8208 | 0.9710 | |
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| No log | 7.69 | 300 | 0.1673 | 0.8830 | 0.7685 | 0.8218 | 0.9701 | |
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| No log | 10.26 | 400 | 0.1428 | 0.8911 | 0.8333 | 0.8612 | 0.9745 | |
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| 0.0687 | 12.82 | 500 | 0.1457 | 0.8636 | 0.8796 | 0.8716 | 0.9763 | |
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| 0.0687 | 15.38 | 600 | 0.1854 | 0.9062 | 0.8056 | 0.8529 | 0.9754 | |
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| 0.0687 | 17.95 | 700 | 0.1841 | 0.8835 | 0.8426 | 0.8626 | 0.9772 | |
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| 0.0687 | 20.51 | 800 | 0.1728 | 0.8505 | 0.8426 | 0.8465 | 0.9754 | |
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| 0.0687 | 23.08 | 900 | 0.1986 | 0.8505 | 0.8426 | 0.8465 | 0.9745 | |
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| 0.0038 | 25.64 | 1000 | 0.2087 | 0.8558 | 0.8241 | 0.8396 | 0.9737 | |
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| 0.0038 | 28.21 | 1100 | 0.1949 | 0.8545 | 0.8704 | 0.8624 | 0.9772 | |
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| 0.0038 | 30.77 | 1200 | 0.1954 | 0.8532 | 0.8611 | 0.8571 | 0.9763 | |
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| 0.0038 | 33.33 | 1300 | 0.1912 | 0.8624 | 0.8704 | 0.8664 | 0.9781 | |
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| 0.0038 | 35.9 | 1400 | 0.1926 | 0.8611 | 0.8611 | 0.8611 | 0.9772 | |
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| 0.0003 | 38.46 | 1500 | 0.1969 | 0.8692 | 0.8611 | 0.8651 | 0.9763 | |
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| 0.0003 | 41.03 | 1600 | 0.1979 | 0.8611 | 0.8611 | 0.8611 | 0.9772 | |
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| 0.0003 | 43.59 | 1700 | 0.1976 | 0.8598 | 0.8519 | 0.8558 | 0.9763 | |
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| 0.0003 | 46.15 | 1800 | 0.1979 | 0.8598 | 0.8519 | 0.8558 | 0.9763 | |
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| 0.0003 | 48.72 | 1900 | 0.1979 | 0.8679 | 0.8519 | 0.8598 | 0.9772 | |
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| 0.0001 | 51.28 | 2000 | 0.1966 | 0.8679 | 0.8519 | 0.8598 | 0.9772 | |
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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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