
<!-- 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-funsd

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0754
- Ignal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}
- Oise: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.9670

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Ignal                                                                                                        | Oise                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.7198        | 1.0   | 1    | 0.7152          | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052            | 0.0435         | 0.0093     | 0.5024           |
| 0.7121        | 2.0   | 2    | 0.7152          | {'precision': 0.010416666666666666, 'recall': 0.09090909090909091, 'f1': 0.018691588785046728, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0052            | 0.0435         | 0.0093     | 0.5024           |
| 0.7191        | 3.0   | 3    | 0.4802          | {'precision': 0.045454545454545456, 'recall': 0.09090909090909091, 'f1': 0.060606060606060615, 'number': 11} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0222            | 0.0435         | 0.0294     | 0.9245           |
| 0.4799        | 4.0   | 4    | 0.3268          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9646           |
| 0.3263        | 5.0   | 5    | 0.2246          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.2269        | 6.0   | 6    | 0.1598          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.1625        | 7.0   | 7    | 0.1227          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.1246        | 8.0   | 8    | 0.1030          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.1042        | 9.0   | 9    | 0.0937          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.0942        | 10.0  | 10   | 0.0892          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.0888        | 11.0  | 11   | 0.0861          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.0834        | 12.0  | 12   | 0.0832          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.0768        | 13.0  | 13   | 0.0805          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.0745        | 14.0  | 14   | 0.0778          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |
| 0.071         | 15.0  | 15   | 0.0754          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | 0.0               | 0.0            | 0.0        | 0.9670           |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "layoutlm-funsd", "results": []}]}
transformers tensorboard safetensors layoutlm token-classification generated_from_trainer base_model:microsoft/layoutlm-base-uncased license:mit autotrain_compatible endpoints_compatible region:us