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--- |
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license: mit |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: layoutlm-funsd-tf |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd-tf |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0961 |
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- Validation Loss: 1.5766 |
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- Train Overall Precision: 0.5302 |
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- Train Overall Recall: 0.6121 |
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- Train Overall F1: 0.5682 |
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- Train Overall Accuracy: 0.6392 |
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- Epoch: 31 |
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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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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch | |
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|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:| |
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| 1.7401 | 1.5464 | 0.0930 | 0.1174 | 0.1038 | 0.3843 | 0 | |
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| 1.4833 | 1.3301 | 0.2414 | 0.3964 | 0.3000 | 0.4268 | 1 | |
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| 1.2693 | 1.2622 | 0.2985 | 0.4947 | 0.3724 | 0.4693 | 2 | |
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| 1.1369 | 1.0729 | 0.3617 | 0.4887 | 0.4157 | 0.5902 | 3 | |
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| 1.0364 | 1.1800 | 0.3293 | 0.5073 | 0.3994 | 0.5604 | 4 | |
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| 0.9327 | 1.2033 | 0.3938 | 0.5268 | 0.4507 | 0.5683 | 5 | |
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| 0.8211 | 1.0876 | 0.4192 | 0.5153 | 0.4623 | 0.6004 | 6 | |
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| 0.7265 | 1.0982 | 0.4480 | 0.5334 | 0.4869 | 0.6102 | 7 | |
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| 0.6561 | 1.1134 | 0.4490 | 0.5650 | 0.5003 | 0.6192 | 8 | |
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| 0.5783 | 1.0834 | 0.4764 | 0.5630 | 0.5161 | 0.6317 | 9 | |
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| 0.5160 | 1.1453 | 0.4504 | 0.5494 | 0.4950 | 0.6227 | 10 | |
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| 0.4714 | 1.1865 | 0.4873 | 0.5981 | 0.5371 | 0.6277 | 11 | |
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| 0.4340 | 1.2212 | 0.4972 | 0.5805 | 0.5356 | 0.6318 | 12 | |
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| 0.3990 | 1.2407 | 0.4913 | 0.6212 | 0.5486 | 0.6334 | 13 | |
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| 0.3743 | 1.2597 | 0.5173 | 0.5986 | 0.5550 | 0.6338 | 14 | |
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| 0.3454 | 1.2205 | 0.5157 | 0.6106 | 0.5592 | 0.6406 | 15 | |
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| 0.3276 | 1.3600 | 0.5186 | 0.6001 | 0.5564 | 0.6318 | 16 | |
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| 0.3013 | 1.6473 | 0.4805 | 0.5745 | 0.5233 | 0.5899 | 17 | |
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| 0.3093 | 1.2595 | 0.4957 | 0.5735 | 0.5318 | 0.6389 | 18 | |
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| 0.2577 | 1.4449 | 0.4772 | 0.5675 | 0.5185 | 0.6076 | 19 | |
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| 0.2301 | 1.4514 | 0.4790 | 0.5620 | 0.5172 | 0.6205 | 20 | |
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| 0.2118 | 1.4575 | 0.5255 | 0.5991 | 0.5599 | 0.6305 | 21 | |
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| 0.1845 | 1.4446 | 0.5270 | 0.6076 | 0.5644 | 0.6353 | 22 | |
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| 0.1698 | 1.4538 | 0.5428 | 0.6011 | 0.5705 | 0.6423 | 23 | |
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| 0.1606 | 1.4318 | 0.5131 | 0.5720 | 0.5409 | 0.6361 | 24 | |
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| 0.1538 | 1.4257 | 0.5310 | 0.6061 | 0.5661 | 0.6484 | 25 | |
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| 0.1403 | 1.5233 | 0.5232 | 0.6061 | 0.5616 | 0.6428 | 26 | |
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| 0.1229 | 1.4796 | 0.5547 | 0.6131 | 0.5825 | 0.6471 | 27 | |
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| 0.1225 | 1.5841 | 0.5239 | 0.5946 | 0.5570 | 0.6101 | 28 | |
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| 0.1085 | 1.5432 | 0.5253 | 0.6046 | 0.5622 | 0.6423 | 29 | |
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| 0.1025 | 1.5414 | 0.5176 | 0.5966 | 0.5543 | 0.6312 | 30 | |
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| 0.0961 | 1.5766 | 0.5302 | 0.6121 | 0.5682 | 0.6392 | 31 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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