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2024-01-10_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp

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layoutlmv3
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+ ---
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+ license: cc-by-nc-sa-4.0
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+ base_model: microsoft/layoutlmv3-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: 2024-01-10_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp
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+ results: []
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+ ---
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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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+
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+ # 2024-01-10_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2492
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+ - Accuracy: 0.7825
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+ - Exit 0 Accuracy: 0.2825
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+ - Exit 1 Accuracy: 0.48
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+ - Exit 2 Accuracy: 0.6675
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+ - Exit 3 Accuracy: 0.7675
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+ - Exit 4 Accuracy: 0.7825
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 24
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+ - total_train_batch_size: 48
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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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+ - num_epochs: 60
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
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+ | No log | 0.96 | 16 | 2.6704 | 0.1675 | 0.065 | 0.0875 | 0.0625 | 0.0625 | 0.105 |
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+ | No log | 1.98 | 33 | 2.4843 | 0.2425 | 0.1075 | 0.1425 | 0.0625 | 0.0625 | 0.17 |
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+ | No log | 3.0 | 50 | 2.3121 | 0.295 | 0.1325 | 0.1375 | 0.0625 | 0.0625 | 0.2575 |
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+ | No log | 3.96 | 66 | 2.0499 | 0.4025 | 0.145 | 0.11 | 0.0625 | 0.0625 | 0.31 |
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+ | No log | 4.98 | 83 | 1.7740 | 0.5425 | 0.14 | 0.1225 | 0.0625 | 0.0625 | 0.4175 |
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+ | No log | 6.0 | 100 | 1.4803 | 0.6225 | 0.1525 | 0.1125 | 0.0625 | 0.0625 | 0.5075 |
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+ | No log | 6.96 | 116 | 1.3264 | 0.6625 | 0.1625 | 0.1375 | 0.0675 | 0.0625 | 0.5775 |
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+ | No log | 7.98 | 133 | 1.1949 | 0.705 | 0.1725 | 0.13 | 0.0775 | 0.0625 | 0.6225 |
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+ | No log | 9.0 | 150 | 1.0490 | 0.73 | 0.1675 | 0.15 | 0.1175 | 0.0625 | 0.6875 |
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+ | No log | 9.96 | 166 | 0.9819 | 0.7375 | 0.185 | 0.1825 | 0.1225 | 0.0625 | 0.685 |
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+ | No log | 10.98 | 183 | 0.9539 | 0.74 | 0.1825 | 0.18 | 0.155 | 0.0625 | 0.695 |
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+ | No log | 12.0 | 200 | 0.8850 | 0.7675 | 0.195 | 0.2275 | 0.1925 | 0.07 | 0.7475 |
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+ | No log | 12.96 | 216 | 0.8869 | 0.75 | 0.1925 | 0.225 | 0.3 | 0.1125 | 0.75 |
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+ | No log | 13.98 | 233 | 0.9250 | 0.7475 | 0.2025 | 0.255 | 0.325 | 0.12 | 0.75 |
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+ | No log | 15.0 | 250 | 0.8685 | 0.7875 | 0.215 | 0.18 | 0.315 | 0.14 | 0.78 |
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+ | No log | 15.96 | 266 | 0.8504 | 0.7875 | 0.2375 | 0.2225 | 0.405 | 0.405 | 0.79 |
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+ | No log | 16.98 | 283 | 0.9215 | 0.7725 | 0.235 | 0.1975 | 0.355 | 0.5075 | 0.775 |
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+ | No log | 18.0 | 300 | 0.9816 | 0.7575 | 0.2575 | 0.2325 | 0.405 | 0.5825 | 0.7575 |
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+ | No log | 18.96 | 316 | 0.9900 | 0.755 | 0.255 | 0.2075 | 0.4525 | 0.5925 | 0.7675 |
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+ | No log | 19.98 | 333 | 0.9651 | 0.78 | 0.255 | 0.245 | 0.485 | 0.595 | 0.7825 |
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+ | No log | 21.0 | 350 | 1.0235 | 0.7525 | 0.24 | 0.3 | 0.465 | 0.6825 | 0.7625 |
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+ | No log | 21.96 | 366 | 1.0137 | 0.7875 | 0.24 | 0.345 | 0.4775 | 0.7275 | 0.785 |
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+ | No log | 22.98 | 383 | 1.0876 | 0.765 | 0.235 | 0.345 | 0.4675 | 0.74 | 0.765 |
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+ | No log | 24.0 | 400 | 1.0696 | 0.77 | 0.2525 | 0.3025 | 0.52 | 0.755 | 0.775 |
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+ | No log | 24.96 | 416 | 1.0440 | 0.775 | 0.2525 | 0.285 | 0.49 | 0.77 | 0.7725 |
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+ | No log | 25.98 | 433 | 1.0962 | 0.76 | 0.255 | 0.2825 | 0.4975 | 0.775 | 0.7625 |
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+ | No log | 27.0 | 450 | 1.1214 | 0.7725 | 0.275 | 0.3525 | 0.515 | 0.7775 | 0.7725 |
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+ | No log | 27.96 | 466 | 1.1593 | 0.7775 | 0.27 | 0.325 | 0.52 | 0.7625 | 0.7775 |
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+ | No log | 28.98 | 483 | 1.1341 | 0.7625 | 0.2725 | 0.3925 | 0.545 | 0.7625 | 0.7625 |
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+ | 0.5624 | 30.0 | 500 | 1.1682 | 0.7675 | 0.2775 | 0.415 | 0.5875 | 0.76 | 0.77 |
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+ | 0.5624 | 30.96 | 516 | 1.1978 | 0.7725 | 0.26 | 0.415 | 0.585 | 0.7675 | 0.77 |
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+ | 0.5624 | 31.98 | 533 | 1.1051 | 0.78 | 0.26 | 0.4275 | 0.59 | 0.7775 | 0.78 |
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+ | 0.5624 | 33.0 | 550 | 1.0934 | 0.78 | 0.25 | 0.41 | 0.5825 | 0.775 | 0.7825 |
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+ | 0.5624 | 33.96 | 566 | 1.1564 | 0.7825 | 0.26 | 0.395 | 0.5925 | 0.7775 | 0.78 |
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+ | 0.5624 | 34.98 | 583 | 1.1605 | 0.7825 | 0.2775 | 0.4425 | 0.615 | 0.77 | 0.785 |
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+ | 0.5624 | 36.0 | 600 | 1.1793 | 0.775 | 0.2825 | 0.4325 | 0.6 | 0.7775 | 0.775 |
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+ | 0.5624 | 36.96 | 616 | 1.1635 | 0.785 | 0.29 | 0.4375 | 0.61 | 0.7625 | 0.7825 |
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+ | 0.5624 | 37.98 | 633 | 1.1591 | 0.775 | 0.28 | 0.4375 | 0.615 | 0.765 | 0.7775 |
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+ | 0.5624 | 39.0 | 650 | 1.1568 | 0.7775 | 0.2875 | 0.455 | 0.625 | 0.765 | 0.7775 |
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+ | 0.5624 | 39.96 | 666 | 1.1686 | 0.78 | 0.2825 | 0.4375 | 0.6275 | 0.7675 | 0.78 |
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+ | 0.5624 | 40.98 | 683 | 1.1720 | 0.785 | 0.275 | 0.45 | 0.6275 | 0.77 | 0.785 |
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+ | 0.5624 | 42.0 | 700 | 1.1977 | 0.785 | 0.2775 | 0.4425 | 0.6375 | 0.76 | 0.785 |
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+ | 0.5624 | 42.96 | 716 | 1.2252 | 0.7825 | 0.275 | 0.4575 | 0.6325 | 0.755 | 0.785 |
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+ | 0.5624 | 43.98 | 733 | 1.2122 | 0.79 | 0.28 | 0.4625 | 0.64 | 0.76 | 0.79 |
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+ | 0.5624 | 45.0 | 750 | 1.2193 | 0.78 | 0.2875 | 0.4625 | 0.6525 | 0.7675 | 0.775 |
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+ | 0.5624 | 45.96 | 766 | 1.2197 | 0.7825 | 0.285 | 0.46 | 0.66 | 0.755 | 0.7775 |
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+ | 0.5624 | 46.98 | 783 | 1.1791 | 0.785 | 0.2825 | 0.47 | 0.6475 | 0.7625 | 0.785 |
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+ | 0.5624 | 48.0 | 800 | 1.1879 | 0.79 | 0.2825 | 0.47 | 0.655 | 0.7625 | 0.7925 |
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+ | 0.5624 | 48.96 | 816 | 1.1847 | 0.795 | 0.285 | 0.4725 | 0.6525 | 0.7625 | 0.7975 |
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+ | 0.5624 | 49.98 | 833 | 1.1964 | 0.7925 | 0.2825 | 0.48 | 0.665 | 0.765 | 0.785 |
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+ | 0.5624 | 51.0 | 850 | 1.2254 | 0.7825 | 0.285 | 0.47 | 0.665 | 0.7675 | 0.7825 |
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+ | 0.5624 | 51.96 | 866 | 1.2455 | 0.7875 | 0.285 | 0.4775 | 0.665 | 0.7625 | 0.785 |
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+ | 0.5624 | 52.98 | 883 | 1.2492 | 0.7875 | 0.2825 | 0.48 | 0.6675 | 0.7625 | 0.7875 |
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+ | 0.5624 | 54.0 | 900 | 1.2459 | 0.785 | 0.285 | 0.47 | 0.67 | 0.77 | 0.785 |
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+ | 0.5624 | 54.96 | 916 | 1.2453 | 0.7825 | 0.2825 | 0.475 | 0.665 | 0.7675 | 0.7825 |
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+ | 0.5624 | 55.98 | 933 | 1.2505 | 0.785 | 0.28 | 0.4775 | 0.665 | 0.7625 | 0.785 |
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+ | 0.5624 | 57.0 | 950 | 1.2494 | 0.7825 | 0.2825 | 0.48 | 0.6675 | 0.765 | 0.7825 |
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+ | 0.5624 | 57.6 | 960 | 1.2492 | 0.7825 | 0.2825 | 0.48 | 0.6675 | 0.7675 | 0.7825 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3
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