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--- |
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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: Output_LayoutLMv3_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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# Output_LayoutLMv3_1 |
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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: 0.2963 |
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- Precision: 0.8017 |
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- Recall: 0.8407 |
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- F1: 0.8207 |
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- Accuracy: 0.9724 |
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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: 3000 |
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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 | 2.27 | 100 | 0.1310 | 0.7254 | 0.7832 | 0.7532 | 0.9619 | |
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| No log | 4.55 | 200 | 0.1070 | 0.8333 | 0.8407 | 0.8370 | 0.9733 | |
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| No log | 6.82 | 300 | 0.1617 | 0.8421 | 0.8496 | 0.8458 | 0.9752 | |
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| No log | 9.09 | 400 | 0.1895 | 0.8145 | 0.7965 | 0.8054 | 0.9705 | |
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| 0.074 | 11.36 | 500 | 0.1689 | 0.8018 | 0.7876 | 0.7946 | 0.9686 | |
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| 0.074 | 13.64 | 600 | 0.1659 | 0.8291 | 0.8584 | 0.8435 | 0.9771 | |
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| 0.074 | 15.91 | 700 | 0.2043 | 0.8077 | 0.8363 | 0.8217 | 0.9705 | |
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| 0.074 | 18.18 | 800 | 0.2111 | 0.8151 | 0.8584 | 0.8362 | 0.9743 | |
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| 0.074 | 20.45 | 900 | 0.1938 | 0.8390 | 0.8761 | 0.8571 | 0.9762 | |
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| 0.0072 | 22.73 | 1000 | 0.1802 | 0.8319 | 0.8540 | 0.8428 | 0.98 | |
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| 0.0072 | 25.0 | 1100 | 0.2604 | 0.7940 | 0.8186 | 0.8061 | 0.9695 | |
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| 0.0072 | 27.27 | 1200 | 0.2550 | 0.8025 | 0.8451 | 0.8233 | 0.9733 | |
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| 0.0072 | 29.55 | 1300 | 0.2632 | 0.8077 | 0.8363 | 0.8217 | 0.9705 | |
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| 0.0072 | 31.82 | 1400 | 0.2865 | 0.8155 | 0.8407 | 0.8279 | 0.9714 | |
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| 0.0018 | 34.09 | 1500 | 0.2554 | 0.8253 | 0.8363 | 0.8308 | 0.9743 | |
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| 0.0018 | 36.36 | 1600 | 0.2763 | 0.8101 | 0.8496 | 0.8294 | 0.9743 | |
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| 0.0018 | 38.64 | 1700 | 0.2541 | 0.8197 | 0.8451 | 0.8322 | 0.9743 | |
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| 0.0018 | 40.91 | 1800 | 0.2785 | 0.8025 | 0.8628 | 0.8316 | 0.9724 | |
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| 0.0018 | 43.18 | 1900 | 0.2760 | 0.8059 | 0.8451 | 0.8251 | 0.9733 | |
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| 0.001 | 45.45 | 2000 | 0.2956 | 0.8155 | 0.8407 | 0.8279 | 0.9733 | |
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| 0.001 | 47.73 | 2100 | 0.2998 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.001 | 50.0 | 2200 | 0.3007 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.001 | 52.27 | 2300 | 0.3000 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.001 | 54.55 | 2400 | 0.3031 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.0002 | 56.82 | 2500 | 0.3174 | 0.7950 | 0.8407 | 0.8172 | 0.9714 | |
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| 0.0002 | 59.09 | 2600 | 0.3061 | 0.7950 | 0.8407 | 0.8172 | 0.9714 | |
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| 0.0002 | 61.36 | 2700 | 0.3059 | 0.7908 | 0.8363 | 0.8129 | 0.9705 | |
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| 0.0002 | 63.64 | 2800 | 0.2988 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.0002 | 65.91 | 2900 | 0.2972 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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| 0.0001 | 68.18 | 3000 | 0.2963 | 0.8017 | 0.8407 | 0.8207 | 0.9724 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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