End of training

README.md

@@ -0,0 +1,78 @@
+---
+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: 0.6633
+- Answer: {'precision': 0.7068004459308808, 'recall': 0.7836835599505563, 'f1': 0.7432590855803048, 'number': 809}
+- Header: {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}
+- Question: {'precision': 0.757679180887372, 'recall': 0.8338028169014085, 'f1': 0.7939204291461779, 'number': 1065}
+- Overall Precision: 0.7121
+- Overall Recall: 0.7817
+- Overall F1: 0.7453
+- Overall Accuracy: 0.8174
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8218        | 1.0   | 10   | 1.6340          | {'precision': 0.012857142857142857, 'recall': 0.011124845488257108, 'f1': 0.011928429423459244, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.22849807445442877, 'recall': 0.1671361502347418, 'f1': 0.19305856832971802, 'number': 1065} | 0.1264            | 0.0938         | 0.1077     | 0.3314           |
+| 1.4842        | 2.0   | 20   | 1.2777          | {'precision': 0.18856447688564476, 'recall': 0.1915945611866502, 'f1': 0.19006744328632738, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.44694533762057875, 'recall': 0.5220657276995305, 'f1': 0.48159376353399735, 'number': 1065} | 0.3441            | 0.3567         | 0.3503     | 0.5691           |
+| 1.1045        | 3.0   | 30   | 0.9751          | {'precision': 0.44747612551159616, 'recall': 0.4054388133498146, 'f1': 0.42542153047989617, 'number': 809}     | {'precision': 0.05555555555555555, 'recall': 0.01680672268907563, 'f1': 0.025806451612903226, 'number': 119} | {'precision': 0.6208445642407907, 'recall': 0.6488262910798122, 'f1': 0.6345270890725436, 'number': 1065}   | 0.5425            | 0.5123         | 0.5270     | 0.6860           |
+| 0.833         | 4.0   | 40   | 0.7763          | {'precision': 0.6252609603340292, 'recall': 0.7404202719406675, 'f1': 0.677985285795133, 'number': 809}        | {'precision': 0.1935483870967742, 'recall': 0.10084033613445378, 'f1': 0.13259668508287292, 'number': 119}   | {'precision': 0.6614583333333334, 'recall': 0.7154929577464789, 'f1': 0.6874154262516915, 'number': 1065}   | 0.6321            | 0.6889         | 0.6593     | 0.7559           |
+| 0.6773        | 5.0   | 50   | 0.7051          | {'precision': 0.6295918367346939, 'recall': 0.7626699629171817, 'f1': 0.6897708216880939, 'number': 809}       | {'precision': 0.29069767441860467, 'recall': 0.21008403361344538, 'f1': 0.24390243902439027, 'number': 119}  | {'precision': 0.6980802792321117, 'recall': 0.7511737089201878, 'f1': 0.7236544549977386, 'number': 1065}   | 0.6519            | 0.7235         | 0.6859     | 0.7788           |
+| 0.5627        | 6.0   | 60   | 0.6598          | {'precision': 0.6423432682425488, 'recall': 0.7725587144622992, 'f1': 0.7014590347923682, 'number': 809}       | {'precision': 0.32098765432098764, 'recall': 0.2184873949579832, 'f1': 0.26, 'number': 119}                  | {'precision': 0.7032878909382518, 'recall': 0.8234741784037559, 'f1': 0.7586505190311419, 'number': 1065}   | 0.6641            | 0.7667         | 0.7117     | 0.7947           |
+| 0.4959        | 7.0   | 70   | 0.6625          | {'precision': 0.6652267818574514, 'recall': 0.761433868974042, 'f1': 0.7100864553314121, 'number': 809}        | {'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119}    | {'precision': 0.7452504317789291, 'recall': 0.8103286384976526, 'f1': 0.7764282501124606, 'number': 1065}   | 0.6889            | 0.7566         | 0.7212     | 0.7945           |
+| 0.4473        | 8.0   | 80   | 0.6402          | {'precision': 0.6684491978609626, 'recall': 0.7725587144622992, 'f1': 0.7167431192660552, 'number': 809}       | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119}      | {'precision': 0.7415540540540541, 'recall': 0.8244131455399061, 'f1': 0.7807914628723877, 'number': 1065}   | 0.6883            | 0.7677         | 0.7258     | 0.8046           |
+| 0.3997        | 9.0   | 90   | 0.6381          | {'precision': 0.6879120879120879, 'recall': 0.7737948084054388, 'f1': 0.7283304246655031, 'number': 809}       | {'precision': 0.27350427350427353, 'recall': 0.2689075630252101, 'f1': 0.2711864406779661, 'number': 119}    | {'precision': 0.7418817651956703, 'recall': 0.8366197183098592, 'f1': 0.7864077669902912, 'number': 1065}   | 0.6952            | 0.7772         | 0.7339     | 0.8095           |
+| 0.3597        | 10.0  | 100  | 0.6481          | {'precision': 0.6959910913140311, 'recall': 0.7725587144622992, 'f1': 0.7322788517867603, 'number': 809}       | {'precision': 0.25984251968503935, 'recall': 0.2773109243697479, 'f1': 0.2682926829268293, 'number': 119}    | {'precision': 0.7495769881556683, 'recall': 0.831924882629108, 'f1': 0.7886070315976857, 'number': 1065}    | 0.6996            | 0.7747         | 0.7352     | 0.8094           |
+| 0.3241        | 11.0  | 110  | 0.6649          | {'precision': 0.6960893854748603, 'recall': 0.7700865265760197, 'f1': 0.7312206572769954, 'number': 809}       | {'precision': 0.32075471698113206, 'recall': 0.2857142857142857, 'f1': 0.30222222222222217, 'number': 119}   | {'precision': 0.7689625108979947, 'recall': 0.828169014084507, 'f1': 0.7974683544303798, 'number': 1065}    | 0.7165            | 0.7722         | 0.7433     | 0.8115           |
+| 0.3111        | 12.0  | 120  | 0.6584          | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}       | {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119}    | {'precision': 0.7658833768494343, 'recall': 0.8262910798122066, 'f1': 0.7949412827461607, 'number': 1065}   | 0.7166            | 0.7827         | 0.7482     | 0.8134           |
+| 0.2896        | 13.0  | 130  | 0.6736          | {'precision': 0.7007963594994312, 'recall': 0.761433868974042, 'f1': 0.7298578199052134, 'number': 809}        | {'precision': 0.2536231884057971, 'recall': 0.29411764705882354, 'f1': 0.2723735408560311, 'number': 119}    | {'precision': 0.7527993109388458, 'recall': 0.8206572769953052, 'f1': 0.7852650494159928, 'number': 1065}   | 0.7002            | 0.7652         | 0.7312     | 0.8091           |
+| 0.278         | 14.0  | 140  | 0.6619          | {'precision': 0.7066666666666667, 'recall': 0.7861557478368356, 'f1': 0.7442949093036864, 'number': 809}       | {'precision': 0.30973451327433627, 'recall': 0.29411764705882354, 'f1': 0.3017241379310345, 'number': 119}   | {'precision': 0.7631806395851339, 'recall': 0.8291079812206573, 'f1': 0.7947794779477948, 'number': 1065}   | 0.7161            | 0.7797         | 0.7466     | 0.8172           |
+| 0.2785        | 15.0  | 150  | 0.6633          | {'precision': 0.7068004459308808, 'recall': 0.7836835599505563, 'f1': 0.7432590855803048, 'number': 809}       | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.757679180887372, 'recall': 0.8338028169014085, 'f1': 0.7939204291461779, 'number': 1065}    | 0.7121            | 0.7817         | 0.7453     | 0.8174           |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3

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