File size: 5,874 Bytes
e8c1d1c dd1b6df e8c1d1c dd1b6df e8c1d1c dd1b6df e8c1d1c |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
tags:
- generated_from_trainer
model-index:
- name: icdar23-entrydetector_labelledtext_breaks_indents_left_diff_right_ref
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. -->
# icdar23-entrydetector_labelledtext_breaks_indents_left_diff_right_ref
This model is a fine-tuned version of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3015
- Act: {'precision': 0.806146572104019, 'recall': 0.8938401048492791, 'f1': 0.8477315102548166, 'number': 1526}
- Cardinal: {'precision': 0.951349296845306, 'recall': 0.962322183775471, 'f1': 0.9568042813455658, 'number': 2601}
- Ebegin: {'precision': 0.9863870493009566, 'recall': 0.9951744617668894, 'f1': 0.9907612712490761, 'number': 2694}
- Eend: {'precision': 0.9925678186547752, 'recall': 0.9885270170244264, 'f1': 0.9905432968663082, 'number': 2702}
- Ft: {'precision': 0.23076923076923078, 'recall': 0.2857142857142857, 'f1': 0.25531914893617025, 'number': 21}
- Loc: {'precision': 0.9102217414818821, 'recall': 0.9339622641509434, 'f1': 0.9219391947411668, 'number': 3604}
- Per: {'precision': 0.9238871899422358, 'recall': 0.9366172924560799, 'f1': 0.9302086897023606, 'number': 2903}
- Titre: {'precision': 0.5961538461538461, 'recall': 0.8266666666666667, 'f1': 0.6927374301675977, 'number': 150}
- Overall Precision: 0.9294
- Overall Recall: 0.9527
- Overall F1: 0.9409
- Overall Accuracy: 0.9452
## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 15000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.07 | 300 | 0.2069 | 0.8798 | 0.9303 | 0.9044 | 0.9571 |
| 0.4341 | 0.14 | 600 | 0.1650 | 0.9456 | 0.9487 | 0.9471 | 0.9658 |
| 0.4341 | 0.21 | 900 | 0.1539 | 0.9370 | 0.9469 | 0.9419 | 0.9644 |
| 0.1993 | 0.29 | 1200 | 0.1280 | 0.9502 | 0.9558 | 0.9530 | 0.9692 |
| 0.1532 | 0.36 | 1500 | 0.1575 | 0.9554 | 0.9507 | 0.9530 | 0.9655 |
| 0.1532 | 0.43 | 1800 | 0.1213 | 0.9403 | 0.9569 | 0.9485 | 0.9670 |
| 0.128 | 0.5 | 2100 | 0.1075 | 0.9538 | 0.9600 | 0.9569 | 0.9745 |
| 0.128 | 0.57 | 2400 | 0.1351 | 0.9485 | 0.9655 | 0.9569 | 0.9696 |
| 0.1095 | 0.64 | 2700 | 0.1384 | 0.9446 | 0.9600 | 0.9522 | 0.9678 |
| 0.1308 | 0.72 | 3000 | 0.1082 | 0.9509 | 0.9617 | 0.9563 | 0.9731 |
| 0.1308 | 0.79 | 3300 | 0.1246 | 0.9546 | 0.9643 | 0.9594 | 0.9712 |
| 0.1007 | 0.86 | 3600 | 0.1290 | 0.9484 | 0.9612 | 0.9547 | 0.9689 |
| 0.1007 | 0.93 | 3900 | 0.1185 | 0.9569 | 0.9604 | 0.9586 | 0.9716 |
| 0.0996 | 1.0 | 4200 | 0.1144 | 0.9561 | 0.9639 | 0.9600 | 0.9753 |
| 0.078 | 1.07 | 4500 | 0.1120 | 0.9483 | 0.9669 | 0.9575 | 0.9746 |
| 0.078 | 1.14 | 4800 | 0.1285 | 0.9522 | 0.9659 | 0.9590 | 0.9719 |
| 0.0723 | 1.22 | 5100 | 0.1302 | 0.9413 | 0.9720 | 0.9565 | 0.9703 |
| 0.0723 | 1.29 | 5400 | 0.1171 | 0.9553 | 0.9687 | 0.9619 | 0.9735 |
| 0.0728 | 1.36 | 5700 | 0.1256 | 0.9475 | 0.9690 | 0.9581 | 0.9733 |
| 0.0538 | 1.43 | 6000 | 0.1169 | 0.9505 | 0.9694 | 0.9599 | 0.9745 |
| 0.0538 | 1.5 | 6300 | 0.1125 | 0.9470 | 0.9712 | 0.9590 | 0.9742 |
| 0.062 | 1.57 | 6600 | 0.1096 | 0.9592 | 0.9675 | 0.9633 | 0.9761 |
| 0.062 | 1.65 | 6900 | 0.1258 | 0.9624 | 0.9638 | 0.9631 | 0.9753 |
| 0.0515 | 1.72 | 7200 | 0.1256 | 0.9586 | 0.9683 | 0.9634 | 0.9733 |
| 0.0561 | 1.79 | 7500 | 0.1411 | 0.9559 | 0.9685 | 0.9622 | 0.9727 |
| 0.0561 | 1.86 | 7800 | 0.1152 | 0.9581 | 0.9672 | 0.9626 | 0.9749 |
| 0.0566 | 1.93 | 8100 | 0.1196 | 0.9618 | 0.9714 | 0.9666 | 0.9768 |
| 0.0566 | 2.0 | 8400 | 0.1868 | 0.8886 | 0.9154 | 0.9018 | 0.9529 |
| 0.1759 | 2.07 | 8700 | 0.1458 | 0.9463 | 0.9643 | 0.9552 | 0.9730 |
| 0.0494 | 2.15 | 9000 | 0.1440 | 0.9543 | 0.9657 | 0.9599 | 0.9750 |
| 0.0494 | 2.22 | 9300 | 0.1382 | 0.9646 | 0.9680 | 0.9663 | 0.9752 |
| 0.0532 | 2.29 | 9600 | 0.1284 | 0.9635 | 0.9712 | 0.9673 | 0.9749 |
| 0.0532 | 2.36 | 9900 | 0.1495 | 0.9624 | 0.9712 | 0.9668 | 0.9745 |
| 0.0223 | 2.43 | 10200 | 0.1203 | 0.9600 | 0.9726 | 0.9662 | 0.9757 |
| 0.0275 | 2.5 | 10500 | 0.1318 | 0.9645 | 0.9694 | 0.9670 | 0.9753 |
| 0.0275 | 2.58 | 10800 | 0.1224 | 0.9623 | 0.9709 | 0.9666 | 0.9756 |
| 0.026 | 2.65 | 11100 | 0.1241 | 0.9633 | 0.9713 | 0.9673 | 0.9756 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|