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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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model-index: |
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- name: layoutlmv3-base-ner |
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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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# layoutlmv3-base-ner |
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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: 1.3110 |
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- Footer: {'precision': 0.9177158273381295, 'recall': 0.8951754385964912, 'f1': 0.9063055062166964, 'number': 2280} |
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- Header: {'precision': 0.5789971617786187, 'recall': 0.6435331230283912, 'f1': 0.6095617529880478, 'number': 951} |
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- Able: {'precision': 0.15821771611526148, 'recall': 0.4848732624693377, 'f1': 0.23858378595855967, 'number': 1223} |
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- Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825} |
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- Ext: {'precision': 0.25493653032440056, 'recall': 0.40928389470704785, 'f1': 0.3141770776751765, 'number': 3533} |
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- Icture: {'precision': 0.013513513513513514, 'recall': 0.018092105263157895, 'f1': 0.01547116736990155, 'number': 608} |
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- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
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- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145} |
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- Ormula: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360} |
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- Overall Precision: 0.3480 |
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- Overall Recall: 0.4682 |
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- Overall F1: 0.3992 |
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- Overall Accuracy: 0.7076 |
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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: 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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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.451 | 1.0 | 500 | 1.4545 | {'precision': 0.7658186562296151, 'recall': 0.5149122807017544, 'f1': 0.6157880933648046, 'number': 2280} | {'precision': 1.0, 'recall': 0.0010515247108307045, 'f1': 0.0021008403361344537, 'number': 951} | {'precision': 0.11016949152542373, 'recall': 0.3507767784137367, 'f1': 0.16767637287473128, 'number': 1223} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825} | {'precision': 0.20891744548286603, 'recall': 0.30370789697141237, 'f1': 0.24754873687853268, 'number': 3533} | {'precision': 0.018442622950819672, 'recall': 0.029605263157894735, 'f1': 0.022727272727272728, 'number': 608} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360} | 0.2335 | 0.2683 | 0.2497 | 0.6695 | |
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| 0.2521 | 2.0 | 1000 | 1.3110 | {'precision': 0.9177158273381295, 'recall': 0.8951754385964912, 'f1': 0.9063055062166964, 'number': 2280} | {'precision': 0.5789971617786187, 'recall': 0.6435331230283912, 'f1': 0.6095617529880478, 'number': 951} | {'precision': 0.15821771611526148, 'recall': 0.4848732624693377, 'f1': 0.23858378595855967, 'number': 1223} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825} | {'precision': 0.25493653032440056, 'recall': 0.40928389470704785, 'f1': 0.3141770776751765, 'number': 3533} | {'precision': 0.013513513513513514, 'recall': 0.018092105263157895, 'f1': 0.01547116736990155, 'number': 608} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360} | 0.3480 | 0.4682 | 0.3992 | 0.7076 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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