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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_1 |
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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_1 |
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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: 0.2310 |
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- Precision: 0.82 |
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- Recall: 0.8119 |
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- F1: 0.8159 |
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- Accuracy: 0.9642 |
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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 | 2.56 | 100 | 0.4044 | 0.5 | 0.0198 | 0.0381 | 0.8884 | |
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| No log | 5.13 | 200 | 0.2363 | 0.7571 | 0.5248 | 0.6199 | 0.9328 | |
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| No log | 7.69 | 300 | 0.1817 | 0.7083 | 0.6733 | 0.6904 | 0.9447 | |
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| No log | 10.26 | 400 | 0.1606 | 0.7551 | 0.7327 | 0.7437 | 0.9523 | |
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| 0.2439 | 12.82 | 500 | 0.1592 | 0.79 | 0.7822 | 0.7861 | 0.9577 | |
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| 0.2439 | 15.38 | 600 | 0.1676 | 0.8144 | 0.7822 | 0.7980 | 0.9621 | |
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| 0.2439 | 17.95 | 700 | 0.1912 | 0.7980 | 0.7822 | 0.7900 | 0.9588 | |
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| 0.2439 | 20.51 | 800 | 0.1860 | 0.8404 | 0.7822 | 0.8103 | 0.9642 | |
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| 0.2439 | 23.08 | 900 | 0.1990 | 0.7767 | 0.7921 | 0.7843 | 0.9567 | |
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| 0.0312 | 25.64 | 1000 | 0.2126 | 0.8081 | 0.7921 | 0.8000 | 0.9610 | |
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| 0.0312 | 28.21 | 1100 | 0.2105 | 0.8058 | 0.8218 | 0.8137 | 0.9621 | |
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| 0.0312 | 30.77 | 1200 | 0.2127 | 0.8119 | 0.8119 | 0.8119 | 0.9632 | |
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| 0.0312 | 33.33 | 1300 | 0.2308 | 0.81 | 0.8020 | 0.8060 | 0.9621 | |
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| 0.0312 | 35.9 | 1400 | 0.2211 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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| 0.0126 | 38.46 | 1500 | 0.2244 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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| 0.0126 | 41.03 | 1600 | 0.2241 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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| 0.0126 | 43.59 | 1700 | 0.2332 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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| 0.0126 | 46.15 | 1800 | 0.2345 | 0.82 | 0.8119 | 0.8159 | 0.9632 | |
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| 0.0126 | 48.72 | 1900 | 0.2318 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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| 0.0069 | 51.28 | 2000 | 0.2310 | 0.82 | 0.8119 | 0.8159 | 0.9642 | |
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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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