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# layoutlm-custom

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1583
-  Noise: {'precision': 0.8818897637795275, 'recall': 0.8736349453978159, 'f1': 0.8777429467084641, 'number': 641}
-  Signal: {'precision': 0.861198738170347, 'recall': 0.853125, 'f1': 0.8571428571428572, 'number': 640}
- Overall Precision: 0.8716
- Overall Recall: 0.8634
- Overall F1: 0.8675
- Overall Accuracy: 0.9656

## 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 |  Noise                                                                                                   |  Signal                                                                                         | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3882        | 1.0   | 18   | 0.2617          | {'precision': 0.6654804270462633, 'recall': 0.5834633385335414, 'f1': 0.6217788861180383, 'number': 641} | {'precision': 0.6149732620320856, 'recall': 0.5390625, 'f1': 0.5745212323064114, 'number': 640} | 0.6402            | 0.5613         | 0.5982     | 0.8986           |
| 0.1694        | 2.0   | 36   | 0.1752          | {'precision': 0.7387820512820513, 'recall': 0.719188767550702, 'f1': 0.7288537549407115, 'number': 641}  | {'precision': 0.709470304975923, 'recall': 0.690625, 'f1': 0.6999208234362629, 'number': 640}   | 0.7241            | 0.7049         | 0.7144     | 0.9296           |
| 0.1039        | 3.0   | 54   | 0.1356          | {'precision': 0.7865168539325843, 'recall': 0.7644305772230889, 'f1': 0.7753164556962026, 'number': 641} | {'precision': 0.77491961414791, 'recall': 0.753125, 'f1': 0.7638668779714739, 'number': 640}    | 0.7807            | 0.7588         | 0.7696     | 0.9439           |
| 0.064         | 4.0   | 72   | 0.1342          | {'precision': 0.8220472440944881, 'recall': 0.8143525741029641, 'f1': 0.8181818181818181, 'number': 641} | {'precision': 0.8028391167192429, 'recall': 0.7953125, 'f1': 0.7990580847723705, 'number': 640} | 0.8125            | 0.8048         | 0.8086     | 0.9522           |
| 0.0433        | 5.0   | 90   | 0.1241          | {'precision': 0.8544303797468354, 'recall': 0.8424336973478939, 'f1': 0.8483896307934014, 'number': 641} | {'precision': 0.8320126782884311, 'recall': 0.8203125, 'f1': 0.8261211644374509, 'number': 640} | 0.8432            | 0.8314         | 0.8373     | 0.9601           |
| 0.0293        | 6.0   | 108  | 0.1274          | {'precision': 0.8650793650793651, 'recall': 0.8502340093603744, 'f1': 0.8575924468922109, 'number': 641} | {'precision': 0.8378378378378378, 'recall': 0.8234375, 'f1': 0.830575256107171, 'number': 640}  | 0.8515            | 0.8368         | 0.8441     | 0.9617           |
| 0.0199        | 7.0   | 126  | 0.1372          | {'precision': 0.8722397476340694, 'recall': 0.8627145085803433, 'f1': 0.8674509803921568, 'number': 641} | {'precision': 0.8530805687203792, 'recall': 0.84375, 'f1': 0.8483896307934015, 'number': 640}   | 0.8627            | 0.8532         | 0.8579     | 0.9640           |
| 0.0139        | 8.0   | 144  | 0.1386          | {'precision': 0.8839427662957074, 'recall': 0.8673946957878315, 'f1': 0.8755905511811023, 'number': 641} | {'precision': 0.856687898089172, 'recall': 0.840625, 'f1': 0.8485804416403785, 'number': 640}   | 0.8703            | 0.8540         | 0.8621     | 0.9656           |
| 0.0126        | 9.0   | 162  | 0.1467          | {'precision': 0.8829113924050633, 'recall': 0.8705148205928237, 'f1': 0.8766692851531814, 'number': 641} | {'precision': 0.8541996830427893, 'recall': 0.8421875, 'f1': 0.848151062155783, 'number': 640}  | 0.8686            | 0.8564         | 0.8624     | 0.9654           |
| 0.0114        | 10.0  | 180  | 0.1531          | {'precision': 0.8694968553459119, 'recall': 0.8627145085803433, 'f1': 0.8660924040720438, 'number': 641} | {'precision': 0.8472440944881889, 'recall': 0.840625, 'f1': 0.8439215686274509, 'number': 640}  | 0.8584            | 0.8517         | 0.8550     | 0.9631           |
| 0.0099        | 11.0  | 198  | 0.1581          | {'precision': 0.8703125, 'recall': 0.8689547581903276, 'f1': 0.8696330991412958, 'number': 641}          | {'precision': 0.8450704225352113, 'recall': 0.84375, 'f1': 0.8444096950742768, 'number': 640}   | 0.8577            | 0.8564         | 0.8570     | 0.9634           |
| 0.0064        | 12.0  | 216  | 0.1543          | {'precision': 0.8866141732283465, 'recall': 0.8783151326053042, 'f1': 0.8824451410658307, 'number': 641} | {'precision': 0.8643533123028391, 'recall': 0.85625, 'f1': 0.8602825745682888, 'number': 640}   | 0.8755            | 0.8673         | 0.8714     | 0.9659           |
| 0.0059        | 13.0  | 234  | 0.1628          | {'precision': 0.8732394366197183, 'recall': 0.8705148205928237, 'f1': 0.871875, 'number': 641}           | {'precision': 0.8526645768025078, 'recall': 0.85, 'f1': 0.8513302034428795, 'number': 640}      | 0.8630            | 0.8603         | 0.8616     | 0.9645           |
| 0.0056        | 14.0  | 252  | 0.1587          | {'precision': 0.878740157480315, 'recall': 0.8705148205928237, 'f1': 0.8746081504702194, 'number': 641}  | {'precision': 0.8580441640378549, 'recall': 0.85, 'f1': 0.8540031397174254, 'number': 640}      | 0.8684            | 0.8603         | 0.8643     | 0.9651           |
| 0.005         | 15.0  | 270  | 0.1583          | {'precision': 0.8818897637795275, 'recall': 0.8736349453978159, 'f1': 0.8777429467084641, 'number': 641} | {'precision': 0.861198738170347, 'recall': 0.853125, 'f1': 0.8571428571428572, 'number': 640}   | 0.8716            | 0.8634         | 0.8675     | 0.9656           |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlm-custom", "results": []}]}
transformers tensorboard safetensors layoutlm token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us