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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_trainer |
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datasets: |
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- funsd |
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model-index: |
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- name: layoutlm-funsd |
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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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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3476 |
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- Answer: {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809} |
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- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
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- Question: {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065} |
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- Overall Precision: 0.2307 |
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- Overall Recall: 0.3628 |
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- Overall F1: 0.2820 |
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- Overall Accuracy: 0.4351 |
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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-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.7432 | 1.0 | 10 | 1.5651 | {'precision': 0.03228782287822878, 'recall': 0.04326328800988875, 'f1': 0.036978341257263604, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18964259664478483, 'recall': 0.24413145539906103, 'f1': 0.2134646962233169, 'number': 1065} | 0.1202 | 0.1480 | 0.1326 | 0.3666 | |
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| 1.5478 | 2.0 | 20 | 1.4279 | {'precision': 0.13696715583508037, 'recall': 0.242274412855377, 'f1': 0.17500000000000002, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.25, 'recall': 0.3652582159624413, 'f1': 0.29683326974437235, 'number': 1065} | 0.1958 | 0.2935 | 0.2349 | 0.4085 | |
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| 1.4112 | 3.0 | 30 | 1.3476 | {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065} | 0.2307 | 0.3628 | 0.2820 | 0.4351 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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