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: 0.6675
+- Answer: {'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809}
+- Header: {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}
+- Question: {'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065}
+- Overall Precision: 0.7174
+- Overall Recall: 0.7847
+- Overall F1: 0.7496
+- Overall Accuracy: 0.8194
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7901        | 1.0   | 10   | 1.6070          | {'precision': 0.019525801952580194, 'recall': 0.0173053152039555, 'f1': 0.018348623853211007, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2396486825595985, 'recall': 0.17934272300469484, 'f1': 0.20515574650912996, 'number': 1065} | 0.1354            | 0.1029         | 0.1169     | 0.3392           |
+| 1.4547        | 2.0   | 20   | 1.2498          | {'precision': 0.21739130434782608, 'recall': 0.22249690976514216, 'f1': 0.21991447770311545, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.464573268921095, 'recall': 0.5417840375586854, 'f1': 0.5002167316861725, 'number': 1065}    | 0.3655            | 0.3798         | 0.3725     | 0.5784           |
+| 1.0779        | 3.0   | 30   | 0.9620          | {'precision': 0.46195652173913043, 'recall': 0.42027194066749074, 'f1': 0.4401294498381877, 'number': 809}   | {'precision': 0.05405405405405406, 'recall': 0.01680672268907563, 'f1': 0.02564102564102564, 'number': 119} | {'precision': 0.631484794275492, 'recall': 0.6629107981220658, 'f1': 0.6468163078332569, 'number': 1065}    | 0.5542            | 0.5258         | 0.5396     | 0.6890           |
+| 0.8184        | 4.0   | 40   | 0.7715          | {'precision': 0.624868282402529, 'recall': 0.7330037082818294, 'f1': 0.6746302616609784, 'number': 809}      | {'precision': 0.1875, 'recall': 0.10084033613445378, 'f1': 0.1311475409836066, 'number': 119}               | {'precision': 0.6657963446475196, 'recall': 0.7183098591549296, 'f1': 0.6910569105691057, 'number': 1065}   | 0.6337            | 0.6874         | 0.6594     | 0.7596           |
+| 0.6687        | 5.0   | 50   | 0.6994          | {'precision': 0.6322778345250255, 'recall': 0.765142150803461, 'f1': 0.692393736017897, 'number': 809}       | {'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119}  | {'precision': 0.7097902097902098, 'recall': 0.7624413145539906, 'f1': 0.7351742870076958, 'number': 1065}   | 0.6593            | 0.7301         | 0.6929     | 0.7815           |
+| 0.5553        | 6.0   | 60   | 0.6586          | {'precision': 0.6430769230769231, 'recall': 0.7750309023485785, 'f1': 0.702914798206278, 'number': 809}      | {'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119}                | {'precision': 0.70863599677159, 'recall': 0.8244131455399061, 'f1': 0.7621527777777778, 'number': 1065}     | 0.6674            | 0.7682         | 0.7143     | 0.7961           |
+| 0.4897        | 7.0   | 70   | 0.6659          | {'precision': 0.6706521739130434, 'recall': 0.7626699629171817, 'f1': 0.7137073452862926, 'number': 809}     | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}  | {'precision': 0.7519788918205804, 'recall': 0.8028169014084507, 'f1': 0.7765667574931879, 'number': 1065}   | 0.6930            | 0.7531         | 0.7218     | 0.7944           |
+| 0.4407        | 8.0   | 80   | 0.6417          | {'precision': 0.6666666666666666, 'recall': 0.7688504326328801, 'f1': 0.7141216991963261, 'number': 809}     | {'precision': 0.2692307692307692, 'recall': 0.23529411764705882, 'f1': 0.25112107623318386, 'number': 119}  | {'precision': 0.7383966244725738, 'recall': 0.8215962441314554, 'f1': 0.7777777777777778, 'number': 1065}   | 0.6863            | 0.7652         | 0.7236     | 0.8050           |
+| 0.3954        | 9.0   | 90   | 0.6419          | {'precision': 0.6933333333333334, 'recall': 0.7713226205191595, 'f1': 0.7302516091281451, 'number': 809}     | {'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119}   | {'precision': 0.7418273260687342, 'recall': 0.8309859154929577, 'f1': 0.7838795394154118, 'number': 1065}   | 0.6954            | 0.7742         | 0.7327     | 0.8089           |
+| 0.3554        | 10.0  | 100  | 0.6524          | {'precision': 0.6996625421822272, 'recall': 0.7688504326328801, 'f1': 0.7326266195524146, 'number': 809}     | {'precision': 0.2578125, 'recall': 0.2773109243697479, 'f1': 0.26720647773279355, 'number': 119}            | {'precision': 0.7448979591836735, 'recall': 0.8225352112676056, 'f1': 0.781793842034806, 'number': 1065}    | 0.6981            | 0.7682         | 0.7315     | 0.8105           |
+| 0.3193        | 11.0  | 110  | 0.6687          | {'precision': 0.6944444444444444, 'recall': 0.7725587144622992, 'f1': 0.7314218841427736, 'number': 809}     | {'precision': 0.3076923076923077, 'recall': 0.2689075630252101, 'f1': 0.28699551569506726, 'number': 119}   | {'precision': 0.7702349869451697, 'recall': 0.8309859154929577, 'f1': 0.7994579945799458, 'number': 1065}   | 0.7162            | 0.7737         | 0.7438     | 0.8105           |
+| 0.3077        | 12.0  | 120  | 0.6657          | {'precision': 0.7019650655021834, 'recall': 0.7948084054388134, 'f1': 0.7455072463768115, 'number': 809}     | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119}              | {'precision': 0.7712532865907099, 'recall': 0.8262910798122066, 'f1': 0.7978241160471442, 'number': 1065}   | 0.7183            | 0.7817         | 0.7487     | 0.8127           |
+| 0.2875        | 13.0  | 130  | 0.6820          | {'precision': 0.6990950226244343, 'recall': 0.7639060568603214, 'f1': 0.7300649734199646, 'number': 809}     | {'precision': 0.2608695652173913, 'recall': 0.3025210084033613, 'f1': 0.28015564202334625, 'number': 119}   | {'precision': 0.7584415584415585, 'recall': 0.8225352112676056, 'f1': 0.7891891891891892, 'number': 1065}   | 0.7028            | 0.7677         | 0.7338     | 0.8094           |
+| 0.2763        | 14.0  | 140  | 0.6680          | {'precision': 0.7062706270627063, 'recall': 0.7935723114956736, 'f1': 0.7473806752037252, 'number': 809}     | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119}  | {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065}   | 0.7150            | 0.7817         | 0.7469     | 0.8181           |
+| 0.2776        | 15.0  | 150  | 0.6675          | {'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809}      | {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}   | {'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065}    | 0.7174            | 0.7847         | 0.7496     | 0.8194           |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

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