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HueyNemud
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icdar23-entrydetector_labelledtext_breaks_indents_left_diff_right_ref

Token Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
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@@ -13,19 +13,19 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2515
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- - Act: {'precision': 0.8046783625730994, 'recall': 0.9017038007863696, 'f1': 0.8504326328800988, 'number': 1526}
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- - Cardinal: {'precision': 0.9451428571428572, 'recall': 0.9538638985005767, 'f1': 0.9494833524684271, 'number': 2601}
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- - Ebegin: {'precision': 0.9940431868950111, 'recall': 0.9910913140311804, 'f1': 0.9925650557620818, 'number': 2694}
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- - Eend: {'precision': 0.9988751406074241, 'recall': 0.9859363434492968, 'f1': 0.9923635686347551, 'number': 2702}
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- - Ft: {'precision': 0.2, 'recall': 0.2857142857142857, 'f1': 0.23529411764705882, 'number': 21}
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- - Loc: {'precision': 0.9071332436069987, 'recall': 0.935072142064373, 'f1': 0.9208908320808854, 'number': 3604}
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- - Per: {'precision': 0.9300651354130957, 'recall': 0.9345504650361695, 'f1': 0.9323024054982818, 'number': 2903}
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- - Titre: {'precision': 0.5234042553191489, 'recall': 0.82, 'f1': 0.6389610389610388, 'number': 150}
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- - Overall Precision: 0.9287
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- - Overall Recall: 0.9507
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- - Overall F1: 0.9396
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- - Overall Accuracy: 0.9459
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  ## Model description
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@@ -50,37 +50,49 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - training_steps: 7500
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.07 | 300 | 0.2219 | 0.8682 | 0.9280 | 0.8971 | 0.9525 |
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- | 0.4302 | 0.14 | 600 | 0.1580 | 0.9429 | 0.9488 | 0.9458 | 0.9658 |
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- | 0.4302 | 0.21 | 900 | 0.1575 | 0.9398 | 0.9429 | 0.9413 | 0.9597 |
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- | 0.1819 | 0.29 | 1200 | 0.1236 | 0.9447 | 0.9542 | 0.9495 | 0.9686 |
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- | 0.1537 | 0.36 | 1500 | 0.1312 | 0.9543 | 0.9486 | 0.9514 | 0.9670 |
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- | 0.1537 | 0.43 | 1800 | 0.1337 | 0.9487 | 0.9559 | 0.9523 | 0.9679 |
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- | 0.119 | 0.5 | 2100 | 0.1198 | 0.9554 | 0.9556 | 0.9555 | 0.9702 |
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- | 0.119 | 0.57 | 2400 | 0.1128 | 0.9467 | 0.9641 | 0.9553 | 0.9707 |
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- | 0.1098 | 0.64 | 2700 | 0.1215 | 0.9528 | 0.9607 | 0.9567 | 0.9713 |
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- | 0.1118 | 0.72 | 3000 | 0.1099 | 0.9482 | 0.9635 | 0.9558 | 0.9711 |
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- | 0.1118 | 0.79 | 3300 | 0.1140 | 0.9541 | 0.9684 | 0.9612 | 0.9727 |
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- | 0.094 | 0.86 | 3600 | 0.0969 | 0.9581 | 0.9654 | 0.9617 | 0.9748 |
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- | 0.094 | 0.93 | 3900 | 0.1089 | 0.9564 | 0.9664 | 0.9614 | 0.9755 |
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- | 0.0895 | 1.0 | 4200 | 0.1158 | 0.9574 | 0.9662 | 0.9618 | 0.9746 |
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- | 0.0626 | 1.07 | 4500 | 0.1072 | 0.9479 | 0.9709 | 0.9593 | 0.9747 |
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- | 0.0626 | 1.14 | 4800 | 0.1060 | 0.9549 | 0.9682 | 0.9615 | 0.9735 |
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- | 0.0474 | 1.22 | 5100 | 0.1172 | 0.9462 | 0.9718 | 0.9588 | 0.9723 |
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- | 0.0474 | 1.29 | 5400 | 0.1019 | 0.9550 | 0.9698 | 0.9624 | 0.9764 |
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- | 0.0554 | 1.36 | 5700 | 0.1086 | 0.9473 | 0.9700 | 0.9585 | 0.9737 |
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- | 0.0416 | 1.43 | 6000 | 0.1175 | 0.9514 | 0.9714 | 0.9613 | 0.9737 |
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- | 0.0416 | 1.5 | 6300 | 0.1143 | 0.9536 | 0.9718 | 0.9626 | 0.9742 |
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- | 0.0514 | 1.57 | 6600 | 0.1113 | 0.9618 | 0.9679 | 0.9648 | 0.9749 |
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- | 0.0514 | 1.65 | 6900 | 0.1084 | 0.9595 | 0.9709 | 0.9652 | 0.9762 |
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- | 0.0377 | 1.72 | 7200 | 0.1102 | 0.9601 | 0.9706 | 0.9653 | 0.9759 |
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- | 0.0437 | 1.79 | 7500 | 0.1123 | 0.9585 | 0.9710 | 0.9647 | 0.9757 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3015
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+ - Act: {'precision': 0.806146572104019, 'recall': 0.8938401048492791, 'f1': 0.8477315102548166, 'number': 1526}
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+ - Cardinal: {'precision': 0.951349296845306, 'recall': 0.962322183775471, 'f1': 0.9568042813455658, 'number': 2601}
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+ - Ebegin: {'precision': 0.9863870493009566, 'recall': 0.9951744617668894, 'f1': 0.9907612712490761, 'number': 2694}
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+ - Eend: {'precision': 0.9925678186547752, 'recall': 0.9885270170244264, 'f1': 0.9905432968663082, 'number': 2702}
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+ - Ft: {'precision': 0.23076923076923078, 'recall': 0.2857142857142857, 'f1': 0.25531914893617025, 'number': 21}
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+ - Loc: {'precision': 0.9102217414818821, 'recall': 0.9339622641509434, 'f1': 0.9219391947411668, 'number': 3604}
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+ - Per: {'precision': 0.9238871899422358, 'recall': 0.9366172924560799, 'f1': 0.9302086897023606, 'number': 2903}
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+ - Titre: {'precision': 0.5961538461538461, 'recall': 0.8266666666666667, 'f1': 0.6927374301675977, 'number': 150}
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+ - Overall Precision: 0.9294
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+ - Overall Recall: 0.9527
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+ - Overall F1: 0.9409
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+ - Overall Accuracy: 0.9452
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - training_steps: 15000
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.07 | 300 | 0.2069 | 0.8798 | 0.9303 | 0.9044 | 0.9571 |
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+ | 0.4341 | 0.14 | 600 | 0.1650 | 0.9456 | 0.9487 | 0.9471 | 0.9658 |
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+ | 0.4341 | 0.21 | 900 | 0.1539 | 0.9370 | 0.9469 | 0.9419 | 0.9644 |
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+ | 0.1993 | 0.29 | 1200 | 0.1280 | 0.9502 | 0.9558 | 0.9530 | 0.9692 |
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+ | 0.1532 | 0.36 | 1500 | 0.1575 | 0.9554 | 0.9507 | 0.9530 | 0.9655 |
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+ | 0.1532 | 0.43 | 1800 | 0.1213 | 0.9403 | 0.9569 | 0.9485 | 0.9670 |
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+ | 0.128 | 0.5 | 2100 | 0.1075 | 0.9538 | 0.9600 | 0.9569 | 0.9745 |
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+ | 0.128 | 0.57 | 2400 | 0.1351 | 0.9485 | 0.9655 | 0.9569 | 0.9696 |
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+ | 0.1095 | 0.64 | 2700 | 0.1384 | 0.9446 | 0.9600 | 0.9522 | 0.9678 |
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+ | 0.1308 | 0.72 | 3000 | 0.1082 | 0.9509 | 0.9617 | 0.9563 | 0.9731 |
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+ | 0.1308 | 0.79 | 3300 | 0.1246 | 0.9546 | 0.9643 | 0.9594 | 0.9712 |
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+ | 0.1007 | 0.86 | 3600 | 0.1290 | 0.9484 | 0.9612 | 0.9547 | 0.9689 |
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+ | 0.1007 | 0.93 | 3900 | 0.1185 | 0.9569 | 0.9604 | 0.9586 | 0.9716 |
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+ | 0.0996 | 1.0 | 4200 | 0.1144 | 0.9561 | 0.9639 | 0.9600 | 0.9753 |
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+ | 0.078 | 1.07 | 4500 | 0.1120 | 0.9483 | 0.9669 | 0.9575 | 0.9746 |
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+ | 0.078 | 1.14 | 4800 | 0.1285 | 0.9522 | 0.9659 | 0.9590 | 0.9719 |
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+ | 0.0723 | 1.22 | 5100 | 0.1302 | 0.9413 | 0.9720 | 0.9565 | 0.9703 |
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+ | 0.0723 | 1.29 | 5400 | 0.1171 | 0.9553 | 0.9687 | 0.9619 | 0.9735 |
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+ | 0.0728 | 1.36 | 5700 | 0.1256 | 0.9475 | 0.9690 | 0.9581 | 0.9733 |
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+ | 0.0538 | 1.43 | 6000 | 0.1169 | 0.9505 | 0.9694 | 0.9599 | 0.9745 |
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+ | 0.0538 | 1.5 | 6300 | 0.1125 | 0.9470 | 0.9712 | 0.9590 | 0.9742 |
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+ | 0.062 | 1.57 | 6600 | 0.1096 | 0.9592 | 0.9675 | 0.9633 | 0.9761 |
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+ | 0.062 | 1.65 | 6900 | 0.1258 | 0.9624 | 0.9638 | 0.9631 | 0.9753 |
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+ | 0.0515 | 1.72 | 7200 | 0.1256 | 0.9586 | 0.9683 | 0.9634 | 0.9733 |
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+ | 0.0561 | 1.79 | 7500 | 0.1411 | 0.9559 | 0.9685 | 0.9622 | 0.9727 |
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+ | 0.0561 | 1.86 | 7800 | 0.1152 | 0.9581 | 0.9672 | 0.9626 | 0.9749 |
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+ | 0.0566 | 1.93 | 8100 | 0.1196 | 0.9618 | 0.9714 | 0.9666 | 0.9768 |
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+ | 0.0566 | 2.0 | 8400 | 0.1868 | 0.8886 | 0.9154 | 0.9018 | 0.9529 |
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+ | 0.1759 | 2.07 | 8700 | 0.1458 | 0.9463 | 0.9643 | 0.9552 | 0.9730 |
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+ | 0.0494 | 2.15 | 9000 | 0.1440 | 0.9543 | 0.9657 | 0.9599 | 0.9750 |
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+ | 0.0494 | 2.22 | 9300 | 0.1382 | 0.9646 | 0.9680 | 0.9663 | 0.9752 |
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+ | 0.0532 | 2.29 | 9600 | 0.1284 | 0.9635 | 0.9712 | 0.9673 | 0.9749 |
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+ | 0.0532 | 2.36 | 9900 | 0.1495 | 0.9624 | 0.9712 | 0.9668 | 0.9745 |
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+ | 0.0223 | 2.43 | 10200 | 0.1203 | 0.9600 | 0.9726 | 0.9662 | 0.9757 |
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+ | 0.0275 | 2.5 | 10500 | 0.1318 | 0.9645 | 0.9694 | 0.9670 | 0.9753 |
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+ | 0.0275 | 2.58 | 10800 | 0.1224 | 0.9623 | 0.9709 | 0.9666 | 0.9756 |
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+ | 0.026 | 2.65 | 11100 | 0.1241 | 0.9633 | 0.9713 | 0.9673 | 0.9756 |
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  ### Framework versions