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2024-01-11_one_stage_subgraphs_entropyreg_txt_vision_enc_all_ramp

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layoutlmv3
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README.md ADDED
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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: 2024-01-11_one_stage_subgraphs_entropyreg_txt_vision_enc_all_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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+ # 2024-01-11_one_stage_subgraphs_entropyreg_txt_vision_enc_all_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: 1.7684
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+ - Accuracy: 0.7425
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+ - Exit 0 Accuracy: 0.0625
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+ - Exit 1 Accuracy: 0.05
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+ - Exit 2 Accuracy: 0.08
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+ - Exit 3 Accuracy: 0.12
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+ - Exit 4 Accuracy: 0.1075
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+ - Exit 5 Accuracy: 0.38
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+ - Exit 6 Accuracy: 0.6625
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+ - Exit 7 Accuracy: 0.7225
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+ - Exit 8 Accuracy: 0.7175
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+ - Exit 9 Accuracy: 0.735
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+ - Exit 10 Accuracy: 0.7425
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+ - Exit 11 Accuracy: 0.7425
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+ - Exit 12 Accuracy: 0.7425
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+ - Exit 13 Accuracy: 0.7425
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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 | Exit 5 Accuracy | Exit 6 Accuracy | Exit 7 Accuracy | Exit 8 Accuracy | Exit 9 Accuracy | Exit 10 Accuracy | Exit 11 Accuracy | Exit 12 Accuracy | Exit 13 Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|:----------------:|:----------------:|
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+ | No log | 0.96 | 16 | 2.6864 | 0.1775 | 0.0525 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0725 |
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+ | No log | 1.98 | 33 | 2.5149 | 0.255 | 0.065 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.09 |
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+ | No log | 3.0 | 50 | 2.3270 | 0.3325 | 0.0625 | 0.05 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.085 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.15 |
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+ | No log | 3.96 | 66 | 2.1448 | 0.39 | 0.06 | 0.05 | 0.0625 | 0.08 | 0.0625 | 0.0625 | 0.08 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.13 |
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+ | No log | 4.98 | 83 | 1.9464 | 0.4675 | 0.075 | 0.05 | 0.0625 | 0.07 | 0.0625 | 0.0625 | 0.075 | 0.0725 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.215 |
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+ | No log | 6.0 | 100 | 1.6533 | 0.565 | 0.0675 | 0.05 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.105 | 0.09 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.2575 |
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+ | No log | 6.96 | 116 | 1.4101 | 0.6325 | 0.0675 | 0.05 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.1025 | 0.1175 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.39 |
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+ | No log | 7.98 | 133 | 1.2738 | 0.6725 | 0.0675 | 0.05 | 0.0625 | 0.07 | 0.0625 | 0.0625 | 0.1125 | 0.135 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.425 |
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+ | No log | 9.0 | 150 | 1.1300 | 0.715 | 0.06 | 0.05 | 0.0625 | 0.07 | 0.0625 | 0.0625 | 0.11 | 0.1375 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.525 |
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+ | No log | 9.96 | 166 | 1.0187 | 0.7525 | 0.06 | 0.05 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.12 | 0.1375 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.535 |
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+ | No log | 10.98 | 183 | 0.9542 | 0.74 | 0.06 | 0.05 | 0.0625 | 0.0675 | 0.0625 | 0.0625 | 0.1175 | 0.1525 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.615 |
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+ | No log | 12.0 | 200 | 0.9636 | 0.74 | 0.06 | 0.05 | 0.0625 | 0.07 | 0.0625 | 0.0625 | 0.12 | 0.1675 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.6325 |
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+ | No log | 12.96 | 216 | 0.9379 | 0.7425 | 0.06 | 0.05 | 0.0625 | 0.08 | 0.0625 | 0.0625 | 0.115 | 0.1825 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.645 |
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+ | No log | 13.98 | 233 | 0.9400 | 0.7575 | 0.06 | 0.05 | 0.0625 | 0.08 | 0.0625 | 0.0625 | 0.105 | 0.2075 | 0.0675 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.695 |
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+ | No log | 15.0 | 250 | 0.9432 | 0.7425 | 0.06 | 0.05 | 0.0625 | 0.08 | 0.0625 | 0.0625 | 0.125 | 0.2325 | 0.0725 | 0.0625 | 0.0625 | 0.0625 | 0.08 | 0.75 |
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+ | No log | 15.96 | 266 | 0.9819 | 0.7575 | 0.06 | 0.05 | 0.0625 | 0.08 | 0.0625 | 0.0625 | 0.1175 | 0.28 | 0.1475 | 0.0625 | 0.0625 | 0.0625 | 0.07 | 0.7475 |
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+ | No log | 16.98 | 283 | 1.0413 | 0.7525 | 0.06 | 0.05 | 0.0625 | 0.085 | 0.0625 | 0.0625 | 0.1275 | 0.3125 | 0.2575 | 0.0625 | 0.0625 | 0.0625 | 0.09 | 0.7475 |
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+ | No log | 18.0 | 300 | 0.9949 | 0.78 | 0.0625 | 0.05 | 0.0625 | 0.0875 | 0.0625 | 0.0625 | 0.1625 | 0.3475 | 0.38 | 0.0625 | 0.0625 | 0.0625 | 0.1025 | 0.76 |
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+ | No log | 18.96 | 316 | 1.0809 | 0.7625 | 0.06 | 0.05 | 0.0625 | 0.0825 | 0.0625 | 0.0625 | 0.18 | 0.3725 | 0.5125 | 0.0625 | 0.0625 | 0.0625 | 0.14 | 0.75 |
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+ | No log | 19.98 | 333 | 1.1297 | 0.755 | 0.06 | 0.05 | 0.0625 | 0.0825 | 0.0625 | 0.0625 | 0.21 | 0.425 | 0.61 | 0.1325 | 0.0625 | 0.0625 | 0.22 | 0.7575 |
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+ | No log | 21.0 | 350 | 1.1168 | 0.78 | 0.0625 | 0.05 | 0.0625 | 0.0875 | 0.0625 | 0.0625 | 0.2325 | 0.475 | 0.63 | 0.215 | 0.0625 | 0.0625 | 0.28 | 0.7775 |
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+ | No log | 21.96 | 366 | 1.1737 | 0.76 | 0.06 | 0.05 | 0.0625 | 0.0825 | 0.0625 | 0.0625 | 0.2675 | 0.53 | 0.6625 | 0.38 | 0.0625 | 0.0625 | 0.4025 | 0.7625 |
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+ | No log | 22.98 | 383 | 1.1825 | 0.775 | 0.06 | 0.05 | 0.0625 | 0.0825 | 0.0625 | 0.0625 | 0.3375 | 0.5425 | 0.6925 | 0.505 | 0.1375 | 0.085 | 0.635 | 0.7675 |
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+ | No log | 24.0 | 400 | 1.1897 | 0.77 | 0.06 | 0.05 | 0.0625 | 0.085 | 0.0625 | 0.0625 | 0.37 | 0.585 | 0.6975 | 0.55 | 0.2725 | 0.22 | 0.7525 | 0.7625 |
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+ | No log | 24.96 | 416 | 1.2044 | 0.7625 | 0.07 | 0.05 | 0.0625 | 0.0875 | 0.0625 | 0.0625 | 0.4175 | 0.6225 | 0.7225 | 0.59 | 0.295 | 0.31 | 0.7625 | 0.765 |
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+ | No log | 25.98 | 433 | 1.2994 | 0.7625 | 0.06 | 0.05 | 0.0625 | 0.09 | 0.0625 | 0.0625 | 0.4625 | 0.6325 | 0.7225 | 0.685 | 0.455 | 0.5025 | 0.7525 | 0.7625 |
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+ | No log | 27.0 | 450 | 1.3615 | 0.75 | 0.065 | 0.05 | 0.0625 | 0.09 | 0.0625 | 0.0625 | 0.5 | 0.65 | 0.7175 | 0.695 | 0.5975 | 0.6025 | 0.75 | 0.7575 |
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+ | No log | 27.96 | 466 | 1.3147 | 0.7625 | 0.065 | 0.05 | 0.0625 | 0.0925 | 0.0625 | 0.0625 | 0.515 | 0.6725 | 0.7275 | 0.72 | 0.6525 | 0.675 | 0.755 | 0.765 |
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+ | No log | 28.98 | 483 | 1.3834 | 0.7625 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0625 | 0.53 | 0.6775 | 0.73 | 0.75 | 0.685 | 0.7075 | 0.755 | 0.7625 |
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+ | 1.6198 | 30.0 | 500 | 1.3835 | 0.765 | 0.07 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0625 | 0.56 | 0.69 | 0.735 | 0.7425 | 0.6975 | 0.7075 | 0.7625 | 0.7625 |
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+ | 1.6198 | 30.96 | 516 | 1.3107 | 0.78 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.07 | 0.5575 | 0.6875 | 0.7275 | 0.7475 | 0.7575 | 0.78 | 0.7775 | 0.78 |
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+ | 1.6198 | 31.98 | 533 | 1.4250 | 0.7675 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0725 | 0.56 | 0.6875 | 0.73 | 0.7525 | 0.7425 | 0.7525 | 0.7625 | 0.765 |
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+ | 1.6198 | 33.0 | 550 | 1.4812 | 0.7575 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0775 | 0.58 | 0.7 | 0.725 | 0.7375 | 0.74 | 0.755 | 0.7575 | 0.7575 |
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+ | 1.6198 | 33.96 | 566 | 1.4561 | 0.76 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0775 | 0.5825 | 0.7 | 0.74 | 0.7575 | 0.7525 | 0.76 | 0.755 | 0.7575 |
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+ | 1.6198 | 34.98 | 583 | 1.4261 | 0.765 | 0.0625 | 0.05 | 0.0625 | 0.095 | 0.0625 | 0.0875 | 0.5925 | 0.7025 | 0.735 | 0.75 | 0.755 | 0.7675 | 0.76 | 0.765 |
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+ | 1.6198 | 36.0 | 600 | 1.5167 | 0.7625 | 0.065 | 0.05 | 0.0625 | 0.1 | 0.0625 | 0.095 | 0.6025 | 0.705 | 0.7375 | 0.7475 | 0.7575 | 0.755 | 0.75 | 0.7575 |
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+ | 1.6198 | 36.96 | 616 | 1.5194 | 0.76 | 0.0625 | 0.05 | 0.0625 | 0.11 | 0.0625 | 0.105 | 0.61 | 0.7025 | 0.73 | 0.7475 | 0.7575 | 0.76 | 0.7525 | 0.7575 |
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+ | 1.6198 | 37.98 | 633 | 1.5906 | 0.7525 | 0.0625 | 0.05 | 0.0625 | 0.1 | 0.0625 | 0.1375 | 0.62 | 0.7225 | 0.735 | 0.7475 | 0.7525 | 0.75 | 0.7525 | 0.755 |
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+ | 1.6198 | 39.0 | 650 | 1.5770 | 0.7525 | 0.0625 | 0.05 | 0.0625 | 0.105 | 0.0625 | 0.1625 | 0.625 | 0.7225 | 0.73 | 0.7375 | 0.75 | 0.755 | 0.75 | 0.755 |
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+ | 1.6198 | 39.96 | 666 | 1.6364 | 0.745 | 0.065 | 0.05 | 0.0625 | 0.1025 | 0.0625 | 0.1875 | 0.6225 | 0.715 | 0.735 | 0.7275 | 0.7425 | 0.74 | 0.74 | 0.745 |
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+ | 1.6198 | 40.98 | 683 | 1.5944 | 0.755 | 0.0625 | 0.05 | 0.0625 | 0.1125 | 0.0625 | 0.2125 | 0.64 | 0.72 | 0.735 | 0.745 | 0.76 | 0.7525 | 0.76 | 0.7575 |
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+ | 1.6198 | 42.0 | 700 | 1.6703 | 0.7475 | 0.0625 | 0.05 | 0.0625 | 0.12 | 0.07 | 0.245 | 0.64 | 0.715 | 0.725 | 0.7375 | 0.745 | 0.7475 | 0.7425 | 0.7475 |
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+ | 1.6198 | 42.96 | 716 | 1.6740 | 0.745 | 0.0625 | 0.05 | 0.0625 | 0.12 | 0.07 | 0.25 | 0.6425 | 0.7 | 0.735 | 0.735 | 0.745 | 0.745 | 0.74 | 0.745 |
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+ | 1.6198 | 43.98 | 733 | 1.7231 | 0.74 | 0.0625 | 0.05 | 0.0625 | 0.12 | 0.08 | 0.265 | 0.6525 | 0.7075 | 0.725 | 0.74 | 0.7475 | 0.745 | 0.7375 | 0.7375 |
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+ | 1.6198 | 45.0 | 750 | 1.6939 | 0.7525 | 0.0625 | 0.05 | 0.0625 | 0.12 | 0.0825 | 0.2725 | 0.645 | 0.7125 | 0.725 | 0.7425 | 0.7475 | 0.7425 | 0.7425 | 0.755 |
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+ | 1.6198 | 45.96 | 766 | 1.7421 | 0.745 | 0.0625 | 0.05 | 0.0625 | 0.12 | 0.0875 | 0.2825 | 0.655 | 0.7125 | 0.7325 | 0.7475 | 0.75 | 0.7425 | 0.7425 | 0.745 |
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+ | 1.6198 | 46.98 | 783 | 1.7177 | 0.7425 | 0.06 | 0.05 | 0.065 | 0.12 | 0.0875 | 0.3 | 0.65 | 0.72 | 0.7325 | 0.7425 | 0.7475 | 0.75 | 0.745 | 0.745 |
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+ | 1.6198 | 48.0 | 800 | 1.7315 | 0.75 | 0.0625 | 0.05 | 0.065 | 0.12 | 0.09 | 0.3225 | 0.645 | 0.7225 | 0.725 | 0.7375 | 0.7525 | 0.745 | 0.745 | 0.75 |
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+ | 1.6198 | 48.96 | 816 | 1.7541 | 0.7475 | 0.0625 | 0.05 | 0.065 | 0.12 | 0.0925 | 0.32 | 0.6575 | 0.72 | 0.7325 | 0.7275 | 0.7375 | 0.745 | 0.745 | 0.7475 |
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+ | 1.6198 | 49.98 | 833 | 1.7533 | 0.745 | 0.0625 | 0.05 | 0.07 | 0.12 | 0.0925 | 0.345 | 0.6675 | 0.72 | 0.725 | 0.745 | 0.7525 | 0.7375 | 0.7425 | 0.7425 |
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+ | 1.6198 | 51.0 | 850 | 1.7161 | 0.745 | 0.0625 | 0.05 | 0.075 | 0.12 | 0.0925 | 0.36 | 0.655 | 0.7225 | 0.7275 | 0.7375 | 0.74 | 0.7375 | 0.745 | 0.75 |
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+ | 1.6198 | 51.96 | 866 | 1.7707 | 0.7425 | 0.0625 | 0.05 | 0.0775 | 0.12 | 0.095 | 0.3675 | 0.6625 | 0.72 | 0.725 | 0.7475 | 0.74 | 0.7425 | 0.7375 | 0.7425 |
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+ | 1.6198 | 52.98 | 883 | 1.7398 | 0.7475 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.1025 | 0.365 | 0.665 | 0.7225 | 0.725 | 0.7325 | 0.74 | 0.7425 | 0.7475 | 0.7475 |
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+ | 1.6198 | 54.0 | 900 | 1.7368 | 0.75 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.105 | 0.375 | 0.6625 | 0.7225 | 0.7225 | 0.735 | 0.745 | 0.7425 | 0.745 | 0.7525 |
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+ | 1.6198 | 54.96 | 916 | 1.7665 | 0.745 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.105 | 0.3725 | 0.6625 | 0.7175 | 0.7225 | 0.735 | 0.745 | 0.74 | 0.7425 | 0.745 |
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+ | 1.6198 | 55.98 | 933 | 1.7775 | 0.7475 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.105 | 0.375 | 0.665 | 0.7225 | 0.7225 | 0.735 | 0.7375 | 0.74 | 0.74 | 0.7475 |
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+ | 1.6198 | 57.0 | 950 | 1.7698 | 0.7425 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.1075 | 0.3775 | 0.6625 | 0.72 | 0.7175 | 0.7325 | 0.7425 | 0.7425 | 0.7425 | 0.7425 |
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+ | 1.6198 | 57.6 | 960 | 1.7684 | 0.7425 | 0.0625 | 0.05 | 0.08 | 0.12 | 0.1075 | 0.38 | 0.6625 | 0.7225 | 0.7175 | 0.735 | 0.7425 | 0.7425 | 0.7425 | 0.7425 |
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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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