File size: 6,897 Bytes
89a613b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
tags:
- generated_from_trainer
model-index:
- name: layoutlm-custom
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. -->
# 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
|