| | --- |
| | library_name: transformers |
| | license: cc-by-nc-sa-4.0 |
| | base_model: microsoft/layoutlmv3-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - layoutlmv3 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: layoutlm-CC-7 |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: layoutlmv3 |
| | type: layoutlmv3 |
| | config: FormsDataset |
| | split: test |
| | args: FormsDataset |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.12529002320185614 |
| | - name: Recall |
| | type: recall |
| | value: 0.20224719101123595 |
| | - name: F1 |
| | type: f1 |
| | value: 0.15472779369627507 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.19654427645788336 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # layoutlm-CC-7 |
| |
|
| | This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 4.1612 |
| | - Precision: 0.1253 |
| | - Recall: 0.2022 |
| | - F1: 0.1547 |
| | - Accuracy: 0.1965 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 15 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 4.8141 | 1.0 | 1 | 4.7205 | 0.0921 | 0.1311 | 0.1082 | 0.0821 | |
| | | 4.7028 | 2.0 | 2 | 4.6365 | 0.1414 | 0.2022 | 0.1664 | 0.1425 | |
| | | 4.6011 | 3.0 | 3 | 4.5617 | 0.1230 | 0.2022 | 0.1530 | 0.1274 | |
| | | 4.5126 | 4.0 | 4 | 4.4931 | 0.1174 | 0.2022 | 0.1486 | 0.1231 | |
| | | 4.4376 | 5.0 | 5 | 4.4390 | 0.1166 | 0.2022 | 0.1479 | 0.1166 | |
| | | 4.3778 | 6.0 | 6 | 4.3926 | 0.1166 | 0.2022 | 0.1479 | 0.1188 | |
| | | 4.3224 | 7.0 | 7 | 4.3454 | 0.1166 | 0.2022 | 0.1479 | 0.1210 | |
| | | 4.2658 | 8.0 | 8 | 4.3058 | 0.1166 | 0.2022 | 0.1479 | 0.1253 | |
| | | 4.2182 | 9.0 | 9 | 4.2708 | 0.1179 | 0.2022 | 0.1490 | 0.1425 | |
| | | 4.1796 | 10.0 | 10 | 4.2415 | 0.1208 | 0.2022 | 0.1513 | 0.1641 | |
| | | 4.1423 | 11.0 | 11 | 4.2165 | 0.1222 | 0.2022 | 0.1523 | 0.1728 | |
| | | 4.1197 | 12.0 | 12 | 4.1951 | 0.1230 | 0.2022 | 0.1530 | 0.1793 | |
| | | 4.0976 | 13.0 | 13 | 4.1782 | 0.1241 | 0.2022 | 0.1538 | 0.1922 | |
| | | 4.0801 | 14.0 | 14 | 4.1669 | 0.1253 | 0.2022 | 0.1547 | 0.1965 | |
| | | 4.0627 | 15.0 | 15 | 4.1612 | 0.1253 | 0.2022 | 0.1547 | 0.1965 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.47.0.dev0 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.1.0 |
| | - Tokenizers 0.20.3 |
| |
|
