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: 0.6888
+- Answer: {'precision': 0.7152391546162402, 'recall': 0.7948084054388134, 'f1': 0.752927400468384, 'number': 809}
+- Header: {'precision': 0.32592592592592595, 'recall': 0.3697478991596639, 'f1': 0.3464566929133859, 'number': 119}
+- Question: {'precision': 0.7731239092495636, 'recall': 0.831924882629108, 'f1': 0.8014473089099955, 'number': 1065}
+- Overall Precision: 0.7216
+- Overall Recall: 0.7893
+- Overall F1: 0.7539
+- Overall Accuracy: 0.8064
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7613        | 1.0   | 10   | 1.5881          | {'precision': 0.032716927453769556, 'recall': 0.02843016069221261, 'f1': 0.030423280423280425, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.2751159196290572, 'recall': 0.1671361502347418, 'f1': 0.20794392523364488, 'number': 1065} | 0.1489            | 0.1009         | 0.1203     | 0.3664           |
+| 1.4369        | 2.0   | 20   | 1.2249          | {'precision': 0.16204986149584488, 'recall': 0.1446229913473424, 'f1': 0.15284128020901372, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.46102819237147596, 'recall': 0.5220657276995305, 'f1': 0.4896521356230735, 'number': 1065} | 0.3491            | 0.3377         | 0.3433     | 0.5911           |
+| 1.0517        | 3.0   | 30   | 0.9409          | {'precision': 0.49002493765586036, 'recall': 0.4857849196538937, 'f1': 0.4878957169459963, 'number': 809}     | {'precision': 0.03333333333333333, 'recall': 0.008403361344537815, 'f1': 0.013422818791946308, 'number': 119} | {'precision': 0.6160409556313993, 'recall': 0.6779342723004694, 'f1': 0.6455073759499329, 'number': 1065}  | 0.5569            | 0.5600         | 0.5584     | 0.7085           |
+| 0.8128        | 4.0   | 40   | 0.7827          | {'precision': 0.6324152542372882, 'recall': 0.7379480840543882, 'f1': 0.6811180832857958, 'number': 809}      | {'precision': 0.14084507042253522, 'recall': 0.08403361344537816, 'f1': 0.10526315789473685, 'number': 119}   | {'precision': 0.6759825327510917, 'recall': 0.7267605633802817, 'f1': 0.7004524886877828, 'number': 1065}  | 0.6394            | 0.6929         | 0.6651     | 0.7571           |
+| 0.6648        | 5.0   | 50   | 0.7231          | {'precision': 0.6456521739130435, 'recall': 0.7342398022249691, 'f1': 0.6871023713128976, 'number': 809}      | {'precision': 0.24050632911392406, 'recall': 0.15966386554621848, 'f1': 0.1919191919191919, 'number': 119}    | {'precision': 0.695364238410596, 'recall': 0.7887323943661971, 'f1': 0.7391113066432027, 'number': 1065}   | 0.6584            | 0.7291         | 0.6919     | 0.7700           |
+| 0.5437        | 6.0   | 60   | 0.6741          | {'precision': 0.6892039258451472, 'recall': 0.7812113720642769, 'f1': 0.7323290845886442, 'number': 809}      | {'precision': 0.2376237623762376, 'recall': 0.20168067226890757, 'f1': 0.2181818181818182, 'number': 119}     | {'precision': 0.6918238993710691, 'recall': 0.8262910798122066, 'f1': 0.7531022678647838, 'number': 1065}  | 0.6707            | 0.7707         | 0.7173     | 0.7898           |
+| 0.4719        | 7.0   | 70   | 0.6655          | {'precision': 0.7017738359201774, 'recall': 0.7824474660074165, 'f1': 0.7399181765049679, 'number': 809}      | {'precision': 0.30927835051546393, 'recall': 0.25210084033613445, 'f1': 0.2777777777777778, 'number': 119}    | {'precision': 0.7549956559513467, 'recall': 0.815962441314554, 'f1': 0.7842960288808664, 'number': 1065}   | 0.7126            | 0.7687         | 0.7396     | 0.7963           |
+| 0.4287        | 8.0   | 80   | 0.6544          | {'precision': 0.701212789415656, 'recall': 0.7861557478368356, 'f1': 0.7412587412587412, 'number': 809}       | {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119}    | {'precision': 0.7593073593073593, 'recall': 0.8234741784037559, 'f1': 0.7900900900900901, 'number': 1065}  | 0.7124            | 0.7767         | 0.7432     | 0.8015           |
+| 0.3842        | 9.0   | 90   | 0.6613          | {'precision': 0.7128378378378378, 'recall': 0.7824474660074165, 'f1': 0.7460223924572775, 'number': 809}      | {'precision': 0.3162393162393162, 'recall': 0.31092436974789917, 'f1': 0.3135593220338983, 'number': 119}     | {'precision': 0.7614917606244579, 'recall': 0.8244131455399061, 'f1': 0.7917042380522993, 'number': 1065}  | 0.7173            | 0.7767         | 0.7458     | 0.8046           |
+| 0.344         | 10.0  | 100  | 0.6669          | {'precision': 0.7030567685589519, 'recall': 0.796044499381953, 'f1': 0.7466666666666666, 'number': 809}       | {'precision': 0.31851851851851853, 'recall': 0.36134453781512604, 'f1': 0.33858267716535434, 'number': 119}   | {'precision': 0.7571801566579635, 'recall': 0.8169014084507042, 'f1': 0.7859078590785908, 'number': 1065}  | 0.7077            | 0.7812         | 0.7427     | 0.8048           |
+| 0.305         | 11.0  | 110  | 0.6761          | {'precision': 0.7134894091415831, 'recall': 0.7911001236093943, 'f1': 0.7502930832356389, 'number': 809}      | {'precision': 0.3391304347826087, 'recall': 0.3277310924369748, 'f1': 0.3333333333333333, 'number': 119}      | {'precision': 0.781387181738367, 'recall': 0.8356807511737089, 'f1': 0.8076225045372051, 'number': 1065}   | 0.7294            | 0.7873         | 0.7572     | 0.8061           |
+| 0.2952        | 12.0  | 120  | 0.6823          | {'precision': 0.7130144605116796, 'recall': 0.792336217552534, 'f1': 0.7505854800936768, 'number': 809}       | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}              | {'precision': 0.7775831873905429, 'recall': 0.8338028169014085, 'f1': 0.8047122791119167, 'number': 1065}  | 0.7238            | 0.7878         | 0.7544     | 0.8066           |
+| 0.2746        | 13.0  | 130  | 0.6829          | {'precision': 0.7212189616252822, 'recall': 0.7898640296662547, 'f1': 0.7539823008849559, 'number': 809}      | {'precision': 0.34959349593495936, 'recall': 0.36134453781512604, 'f1': 0.35537190082644626, 'number': 119}   | {'precision': 0.7833775419982316, 'recall': 0.831924882629108, 'f1': 0.8069216757741348, 'number': 1065}   | 0.7327            | 0.7868         | 0.7588     | 0.8092           |
+| 0.2632        | 14.0  | 140  | 0.6901          | {'precision': 0.7150776053215078, 'recall': 0.7972805933250927, 'f1': 0.7539450613676213, 'number': 809}      | {'precision': 0.3333333333333333, 'recall': 0.3865546218487395, 'f1': 0.357976653696498, 'number': 119}       | {'precision': 0.7746478873239436, 'recall': 0.8262910798122066, 'f1': 0.7996365288505224, 'number': 1065}  | 0.7220            | 0.7883         | 0.7537     | 0.8066           |
+| 0.2575        | 15.0  | 150  | 0.6888          | {'precision': 0.7152391546162402, 'recall': 0.7948084054388134, 'f1': 0.752927400468384, 'number': 809}       | {'precision': 0.32592592592592595, 'recall': 0.3697478991596639, 'f1': 0.3464566929133859, 'number': 119}     | {'precision': 0.7731239092495636, 'recall': 0.831924882629108, 'f1': 0.8014473089099955, 'number': 1065}   | 0.7216            | 0.7893         | 0.7539     | 0.8064           |
+
+
+### Framework versions
+
+- Transformers 4.27.2
+- Pytorch 1.13.1+cpu
+- Datasets 2.11.0
+- Tokenizers 0.11.0

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