| | --- |
| | license: cc-by-nc-sa-4.0 |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: layoutlmv3-base-ner |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # layoutlmv3-base-ner |
| |
|
| | This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.4071 |
| | - Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} |
| | - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} |
| | - Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} |
| | - Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} |
| | - Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} |
| | - Icture: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} |
| | - Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} |
| | - Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} |
| | - Overall Precision: 0.0 |
| | - Overall Recall: 0.0 |
| | - Overall F1: 0.0 |
| | - Overall Accuracy: 0.6399 |
| |
|
| | ## 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: 4 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:----------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
| | | 1.1724 | 1.0 | 1950 | 1.4537 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 | |
| | | 1.2004 | 2.0 | 3900 | 1.4094 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 | |
| | | 1.2026 | 3.0 | 5850 | 1.4038 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 | |
| | | 1.2107 | 4.0 | 7800 | 1.4217 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 | |
| | | 1.1836 | 5.0 | 9750 | 1.4071 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.26.0 |
| | - Pytorch 1.12.1 |
| | - Datasets 2.9.0 |
| | - Tokenizers 0.13.2 |
| |
|
