metadata
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv3-base-ner
results: []
layoutlmv3-base-ner
This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3110
- Footer: {'precision': 0.9177158273381295, 'recall': 0.8951754385964912, 'f1': 0.9063055062166964, 'number': 2280}
- Header: {'precision': 0.5789971617786187, 'recall': 0.6435331230283912, 'f1': 0.6095617529880478, 'number': 951}
- Able: {'precision': 0.15821771611526148, 'recall': 0.4848732624693377, 'f1': 0.23858378595855967, 'number': 1223}
- Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825}
- Ext: {'precision': 0.25493653032440056, 'recall': 0.40928389470704785, 'f1': 0.3141770776751765, 'number': 3533}
- Icture: {'precision': 0.013513513513513514, 'recall': 0.018092105263157895, 'f1': 0.01547116736990155, 'number': 608}
- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145}
- Ormula: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360}
- Overall Precision: 0.3480
- Overall Recall: 0.4682
- Overall F1: 0.3992
- Overall Accuracy: 0.7076
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| 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 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2