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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: Output_LayoutLMv3_v6 |
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results: [] |
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datasets: |
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- Noureddinesa/LayoutLmv3_v1 |
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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_v6 |
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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: 0.1308 |
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- Precision: 0.7788 |
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- Recall: 0.8 |
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- F1: 0.7892 |
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- Accuracy: 0.9637 |
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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: 3e-07 |
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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 | 1.61 | 100 | 0.5057 | 0.0 | 0.0 | 0.0 | 0.8962 | |
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| No log | 3.23 | 200 | 0.3746 | 0.0 | 0.0 | 0.0 | 0.8962 | |
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| No log | 4.84 | 300 | 0.2979 | 0.2143 | 0.0273 | 0.0484 | 0.9014 | |
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| No log | 6.45 | 400 | 0.2474 | 0.4444 | 0.1455 | 0.2192 | 0.9135 | |
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| 0.4794 | 8.06 | 500 | 0.2189 | 0.5 | 0.3 | 0.3750 | 0.9291 | |
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| 0.4794 | 9.68 | 600 | 0.2031 | 0.5301 | 0.4 | 0.4560 | 0.9325 | |
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| 0.4794 | 11.29 | 700 | 0.1916 | 0.6 | 0.4636 | 0.5231 | 0.9377 | |
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| 0.4794 | 12.9 | 800 | 0.1788 | 0.6364 | 0.5727 | 0.6029 | 0.9412 | |
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| 0.4794 | 14.52 | 900 | 0.1728 | 0.6796 | 0.6364 | 0.6573 | 0.9464 | |
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| 0.184 | 16.13 | 1000 | 0.1644 | 0.7010 | 0.6182 | 0.6570 | 0.9481 | |
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| 0.184 | 17.74 | 1100 | 0.1593 | 0.75 | 0.7091 | 0.7290 | 0.9567 | |
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| 0.184 | 19.35 | 1200 | 0.1520 | 0.7714 | 0.7364 | 0.7535 | 0.9602 | |
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| 0.184 | 20.97 | 1300 | 0.1420 | 0.7778 | 0.7636 | 0.7706 | 0.9619 | |
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| 0.184 | 22.58 | 1400 | 0.1427 | 0.7925 | 0.7636 | 0.7778 | 0.9637 | |
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| 0.1278 | 24.19 | 1500 | 0.1361 | 0.7727 | 0.7727 | 0.7727 | 0.9619 | |
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| 0.1278 | 25.81 | 1600 | 0.1342 | 0.8019 | 0.7727 | 0.7870 | 0.9654 | |
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| 0.1278 | 27.42 | 1700 | 0.1310 | 0.8056 | 0.7909 | 0.7982 | 0.9671 | |
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| 0.1278 | 29.03 | 1800 | 0.1290 | 0.7857 | 0.8 | 0.7928 | 0.9654 | |
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| 0.1278 | 30.65 | 1900 | 0.1268 | 0.7946 | 0.8091 | 0.8018 | 0.9671 | |
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| 0.0999 | 32.26 | 2000 | 0.1229 | 0.7768 | 0.7909 | 0.7838 | 0.9637 | |
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| 0.0999 | 33.87 | 2100 | 0.1305 | 0.8056 | 0.7909 | 0.7982 | 0.9654 | |
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| 0.0999 | 35.48 | 2200 | 0.1349 | 0.8241 | 0.8091 | 0.8165 | 0.9689 | |
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| 0.0999 | 37.1 | 2300 | 0.1327 | 0.8018 | 0.8091 | 0.8054 | 0.9654 | |
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| 0.0999 | 38.71 | 2400 | 0.1289 | 0.8018 | 0.8091 | 0.8054 | 0.9654 | |
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| 0.0833 | 40.32 | 2500 | 0.1274 | 0.8018 | 0.8091 | 0.8054 | 0.9654 | |
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| 0.0833 | 41.94 | 2600 | 0.1279 | 0.8018 | 0.8091 | 0.8054 | 0.9654 | |
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| 0.0833 | 43.55 | 2700 | 0.1295 | 0.8018 | 0.8091 | 0.8054 | 0.9654 | |
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| 0.0833 | 45.16 | 2800 | 0.1306 | 0.7788 | 0.8 | 0.7892 | 0.9637 | |
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| 0.0833 | 46.77 | 2900 | 0.1312 | 0.7788 | 0.8 | 0.7892 | 0.9637 | |
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| 0.0749 | 48.39 | 3000 | 0.1308 | 0.7788 | 0.8 | 0.7892 | 0.9637 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.13.3 |
