End of training

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-funsd
+  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-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7158
+- Answer: {'precision': 0.7149220489977728, 'recall': 0.7935723114956736, 'f1': 0.7521968365553603, 'number': 809}
+- Header: {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}
+- Question: {'precision': 0.7892857142857143, 'recall': 0.8300469483568075, 'f1': 0.8091533180778031, 'number': 1065}
+- Overall Precision: 0.7327
+- Overall Recall: 0.7827
+- Overall F1: 0.7569
+- Overall Accuracy: 0.8108
+
+## 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: 16
+- 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 | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                   | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8132        | 1.0   | 10   | 1.6191          | {'precision': 0.015122873345935728, 'recall': 0.019777503090234856, 'f1': 0.01713979646491698, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1685508735868448, 'recall': 0.1539906103286385, 'f1': 0.16094210009813542, 'number': 1065} | 0.0886            | 0.0903         | 0.0895     | 0.3534           |
+| 1.4783        | 2.0   | 20   | 1.2483          | {'precision': 0.12857142857142856, 'recall': 0.12237330037082818, 'f1': 0.1253958201393287, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4541955350269438, 'recall': 0.5539906103286385, 'f1': 0.4991539763113368, 'number': 1065}  | 0.3330            | 0.3457         | 0.3392     | 0.5682           |
+| 1.1072        | 3.0   | 30   | 0.9718          | {'precision': 0.42777155655095184, 'recall': 0.4721878862793572, 'f1': 0.4488836662749706, 'number': 809}     | {'precision': 0.04, 'recall': 0.008403361344537815, 'f1': 0.01388888888888889, 'number': 119}               | {'precision': 0.6266205704407951, 'recall': 0.6807511737089202, 'f1': 0.6525652565256526, 'number': 1065}  | 0.5340            | 0.5559         | 0.5447     | 0.7070           |
+| 0.8444        | 4.0   | 40   | 0.7957          | {'precision': 0.6296296296296297, 'recall': 0.7354758961681088, 'f1': 0.6784492588369442, 'number': 809}      | {'precision': 0.19230769230769232, 'recall': 0.08403361344537816, 'f1': 0.11695906432748539, 'number': 119} | {'precision': 0.6831168831168831, 'recall': 0.7408450704225352, 'f1': 0.7108108108108109, 'number': 1065}  | 0.6478            | 0.6994         | 0.6726     | 0.7651           |
+| 0.6845        | 5.0   | 50   | 0.7443          | {'precision': 0.6530612244897959, 'recall': 0.7515451174289246, 'f1': 0.6988505747126437, 'number': 809}      | {'precision': 0.23684210526315788, 'recall': 0.15126050420168066, 'f1': 0.1846153846153846, 'number': 119}  | {'precision': 0.7318181818181818, 'recall': 0.755868544600939, 'f1': 0.74364896073903, 'number': 1065}     | 0.6792            | 0.7180         | 0.6980     | 0.7736           |
+| 0.5597        | 6.0   | 60   | 0.6918          | {'precision': 0.6673706441393875, 'recall': 0.7812113720642769, 'f1': 0.7198177676537586, 'number': 809}      | {'precision': 0.2857142857142857, 'recall': 0.15126050420168066, 'f1': 0.1978021978021978, 'number': 119}   | {'precision': 0.7344150298889838, 'recall': 0.8075117370892019, 'f1': 0.7692307692307693, 'number': 1065}  | 0.6923            | 0.7577         | 0.7235     | 0.7933           |
+| 0.4929        | 7.0   | 70   | 0.6803          | {'precision': 0.6694825765575502, 'recall': 0.7836835599505563, 'f1': 0.7220956719817767, 'number': 809}      | {'precision': 0.21818181818181817, 'recall': 0.20168067226890757, 'f1': 0.2096069868995633, 'number': 119}  | {'precision': 0.7467134092900964, 'recall': 0.8, 'f1': 0.772438803263826, 'number': 1065}                  | 0.6870            | 0.7577         | 0.7206     | 0.7945           |
+| 0.4447        | 8.0   | 80   | 0.6814          | {'precision': 0.6866158868335147, 'recall': 0.7799752781211372, 'f1': 0.7303240740740741, 'number': 809}      | {'precision': 0.26506024096385544, 'recall': 0.18487394957983194, 'f1': 0.21782178217821785, 'number': 119} | {'precision': 0.7810283687943262, 'recall': 0.8272300469483568, 'f1': 0.8034655722754217, 'number': 1065}  | 0.7202            | 0.7697         | 0.7441     | 0.8024           |
+| 0.3953        | 9.0   | 90   | 0.6739          | {'precision': 0.7015765765765766, 'recall': 0.7700865265760197, 'f1': 0.7342368886269888, 'number': 809}      | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119}   | {'precision': 0.7753496503496503, 'recall': 0.8328638497652582, 'f1': 0.8030783159800814, 'number': 1065}  | 0.7193            | 0.7742         | 0.7458     | 0.8115           |
+| 0.3538        | 10.0  | 100  | 0.6853          | {'precision': 0.7081497797356828, 'recall': 0.7948084054388134, 'f1': 0.7489807804309844, 'number': 809}      | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119}  | {'precision': 0.7804878048780488, 'recall': 0.8413145539906103, 'f1': 0.8097605061003164, 'number': 1065}  | 0.7288            | 0.7888         | 0.7576     | 0.8152           |
+| 0.3262        | 11.0  | 110  | 0.6948          | {'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809}      | {'precision': 0.35051546391752575, 'recall': 0.2857142857142857, 'f1': 0.3148148148148148, 'number': 119}   | {'precision': 0.8032638259292838, 'recall': 0.831924882629108, 'f1': 0.8173431734317343, 'number': 1065}   | 0.7404            | 0.7842         | 0.7617     | 0.8129           |
+| 0.3094        | 12.0  | 120  | 0.6989          | {'precision': 0.7128603104212861, 'recall': 0.7948084054388134, 'f1': 0.7516072472238456, 'number': 809}      | {'precision': 0.3333333333333333, 'recall': 0.29411764705882354, 'f1': 0.3125, 'number': 119}               | {'precision': 0.7987364620938628, 'recall': 0.8309859154929577, 'f1': 0.8145421076852278, 'number': 1065}  | 0.7390            | 0.7842         | 0.7610     | 0.8138           |
+| 0.2941        | 13.0  | 130  | 0.7134          | {'precision': 0.7239819004524887, 'recall': 0.7911001236093943, 'f1': 0.7560543414057885, 'number': 809}      | {'precision': 0.32710280373831774, 'recall': 0.29411764705882354, 'f1': 0.3097345132743363, 'number': 119}  | {'precision': 0.7998204667863554, 'recall': 0.8366197183098592, 'f1': 0.8178063331803579, 'number': 1065}  | 0.7439            | 0.7858         | 0.7643     | 0.8115           |
+| 0.2813        | 14.0  | 140  | 0.7138          | {'precision': 0.7106710671067107, 'recall': 0.7985166872682324, 'f1': 0.7520372526193247, 'number': 809}      | {'precision': 0.3119266055045872, 'recall': 0.2857142857142857, 'f1': 0.2982456140350877, 'number': 119}    | {'precision': 0.7935656836461126, 'recall': 0.8338028169014085, 'f1': 0.8131868131868133, 'number': 1065}  | 0.7337            | 0.7868         | 0.7593     | 0.8109           |
+| 0.2812        | 15.0  | 150  | 0.7158          | {'precision': 0.7149220489977728, 'recall': 0.7935723114956736, 'f1': 0.7521968365553603, 'number': 809}      | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}    | {'precision': 0.7892857142857143, 'recall': 0.8300469483568075, 'f1': 0.8091533180778031, 'number': 1065}  | 0.7327            | 0.7827         | 0.7569     | 0.8108           |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1

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