End of training

README.md

@@ -0,0 +1,79 @@
+---
+license: mit
+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: 0.6888
+- Answer: {'precision': 0.6959826275787188, 'recall': 0.792336217552534, 'f1': 0.7410404624277457, 'number': 809}
+- Header: {'precision': 0.3629032258064516, 'recall': 0.37815126050420167, 'f1': 0.37037037037037035, 'number': 119}
+- Question: {'precision': 0.7736185383244206, 'recall': 0.8150234741784037, 'f1': 0.7937814357567444, 'number': 1065}
+- Overall Precision: 0.7171
+- Overall Recall: 0.7797
+- Overall F1: 0.7471
+- Overall Accuracy: 0.8084
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8101        | 1.0   | 10   | 1.5789          | {'precision': 0.01434878587196468, 'recall': 0.016069221260815822, 'f1': 0.015160349854227406, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.1038107752956636, 'recall': 0.07417840375586854, 'f1': 0.08652792990142387, 'number': 1065} | 0.0552            | 0.0462         | 0.0503     | 0.3845           |
+| 1.4764        | 2.0   | 20   | 1.2528          | {'precision': 0.16216216216216217, 'recall': 0.14833127317676142, 'f1': 0.15493867010974824, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.452970297029703, 'recall': 0.5154929577464789, 'f1': 0.48221343873517786, 'number': 1065}   | 0.3427            | 0.3357         | 0.3392     | 0.5948           |
+| 1.106         | 3.0   | 30   | 0.9703          | {'precision': 0.49557522123893805, 'recall': 0.553770086526576, 'f1': 0.5230589608873321, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.6458527493010252, 'recall': 0.6507042253521127, 'f1': 0.6482694106641721, 'number': 1065}   | 0.5679            | 0.5725         | 0.5702     | 0.7117           |
+| 0.8412        | 4.0   | 40   | 0.7859          | {'precision': 0.6176165803108808, 'recall': 0.7367119901112484, 'f1': 0.6719278466741826, 'number': 809}      | {'precision': 0.19642857142857142, 'recall': 0.09243697478991597, 'f1': 0.12571428571428572, 'number': 119} | {'precision': 0.7102272727272727, 'recall': 0.704225352112676, 'f1': 0.7072135785007072, 'number': 1065}    | 0.6533            | 0.6809         | 0.6668     | 0.7606           |
+| 0.6772        | 5.0   | 50   | 0.7168          | {'precision': 0.6395582329317269, 'recall': 0.7873918417799752, 'f1': 0.7058171745152354, 'number': 809}      | {'precision': 0.17475728155339806, 'recall': 0.15126050420168066, 'f1': 0.16216216216216217, 'number': 119} | {'precision': 0.730072463768116, 'recall': 0.7568075117370892, 'f1': 0.7431996311664361, 'number': 1065}    | 0.6632            | 0.7331         | 0.6964     | 0.7834           |
+| 0.571         | 6.0   | 60   | 0.6881          | {'precision': 0.6596638655462185, 'recall': 0.7762669962917181, 'f1': 0.7132311186825667, 'number': 809}      | {'precision': 0.2345679012345679, 'recall': 0.15966386554621848, 'f1': 0.18999999999999997, 'number': 119}  | {'precision': 0.7076923076923077, 'recall': 0.8206572769953052, 'f1': 0.7600000000000001, 'number': 1065}   | 0.6706            | 0.7632         | 0.7139     | 0.7930           |
+| 0.5021        | 7.0   | 70   | 0.6724          | {'precision': 0.6694736842105263, 'recall': 0.7861557478368356, 'f1': 0.7231381466742467, 'number': 809}      | {'precision': 0.2542372881355932, 'recall': 0.25210084033613445, 'f1': 0.25316455696202533, 'number': 119}  | {'precision': 0.7303754266211604, 'recall': 0.8037558685446009, 'f1': 0.765310683951721, 'number': 1065}    | 0.6795            | 0.7637         | 0.7191     | 0.7968           |
+| 0.454         | 8.0   | 80   | 0.6567          | {'precision': 0.6835306781485468, 'recall': 0.7849196538936959, 'f1': 0.7307249712313003, 'number': 809}      | {'precision': 0.35051546391752575, 'recall': 0.2857142857142857, 'f1': 0.3148148148148148, 'number': 119}   | {'precision': 0.7604259094942325, 'recall': 0.8046948356807512, 'f1': 0.781934306569343, 'number': 1065}    | 0.7088            | 0.7657         | 0.7361     | 0.8040           |
+| 0.4011        | 9.0   | 90   | 0.6651          | {'precision': 0.6748140276301806, 'recall': 0.7849196538936959, 'f1': 0.7257142857142858, 'number': 809}      | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}   | {'precision': 0.7592592592592593, 'recall': 0.8084507042253521, 'f1': 0.7830832196452934, 'number': 1065}   | 0.6970            | 0.7697         | 0.7315     | 0.8006           |
+| 0.3604        | 10.0  | 100  | 0.6693          | {'precision': 0.6716259298618491, 'recall': 0.7812113720642769, 'f1': 0.7222857142857143, 'number': 809}      | {'precision': 0.32432432432432434, 'recall': 0.3025210084033613, 'f1': 0.31304347826086953, 'number': 119}  | {'precision': 0.7441077441077442, 'recall': 0.8300469483568075, 'f1': 0.7847314691522416, 'number': 1065}   | 0.6929            | 0.7787         | 0.7333     | 0.7999           |
+| 0.3269        | 11.0  | 110  | 0.6750          | {'precision': 0.6823027718550106, 'recall': 0.7911001236093943, 'f1': 0.7326846021751574, 'number': 809}      | {'precision': 0.3783783783783784, 'recall': 0.35294117647058826, 'f1': 0.3652173913043478, 'number': 119}   | {'precision': 0.7705357142857143, 'recall': 0.8103286384976526, 'f1': 0.7899313501144164, 'number': 1065}   | 0.7123            | 0.7752         | 0.7424     | 0.8068           |
+| 0.3069        | 12.0  | 120  | 0.6782          | {'precision': 0.6866310160427808, 'recall': 0.7935723114956736, 'f1': 0.7362385321100916, 'number': 809}      | {'precision': 0.3865546218487395, 'recall': 0.3865546218487395, 'f1': 0.38655462184873957, 'number': 119}   | {'precision': 0.7771739130434783, 'recall': 0.8056338028169014, 'f1': 0.7911479944674966, 'number': 1065}   | 0.7164            | 0.7757         | 0.7449     | 0.8062           |
+| 0.293         | 13.0  | 130  | 0.6901          | {'precision': 0.6992316136114161, 'recall': 0.7873918417799752, 'f1': 0.7406976744186047, 'number': 809}      | {'precision': 0.3983050847457627, 'recall': 0.3949579831932773, 'f1': 0.39662447257383965, 'number': 119}   | {'precision': 0.775089605734767, 'recall': 0.812206572769953, 'f1': 0.7932141219624025, 'number': 1065}     | 0.7221            | 0.7772         | 0.7487     | 0.8057           |
+| 0.2775        | 14.0  | 140  | 0.6842          | {'precision': 0.6945337620578779, 'recall': 0.8009888751545118, 'f1': 0.7439724454649829, 'number': 809}      | {'precision': 0.36363636363636365, 'recall': 0.3697478991596639, 'f1': 0.3666666666666667, 'number': 119}   | {'precision': 0.7723214285714286, 'recall': 0.812206572769953, 'f1': 0.7917620137299771, 'number': 1065}    | 0.7162            | 0.7812         | 0.7473     | 0.8068           |
+| 0.2724        | 15.0  | 150  | 0.6888          | {'precision': 0.6959826275787188, 'recall': 0.792336217552534, 'f1': 0.7410404624277457, 'number': 809}       | {'precision': 0.3629032258064516, 'recall': 0.37815126050420167, 'f1': 0.37037037037037035, 'number': 119}  | {'precision': 0.7736185383244206, 'recall': 0.8150234741784037, 'f1': 0.7937814357567444, 'number': 1065}   | 0.7171            | 0.7797         | 0.7471     | 0.8084           |
+
+
+### Framework versions
+
+- Transformers 4.30.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.13.3

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