End of training

README.md

@@ -0,0 +1,80 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- layoutlm_resume_data
+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 layoutlm_resume_data dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0076
+- Address: {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 24}
+- Email: {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27}
+- Name: {'precision': 0.926829268292683, 'recall': 0.9743589743589743, 'f1': 0.9500000000000001, 'number': 39}
+- Phone: {'precision': 0.8378378378378378, 'recall': 0.8611111111111112, 'f1': 0.8493150684931507, 'number': 36}
+- Overall Precision: 0.8769
+- Overall Recall: 0.9048
+- Overall F1: 0.8906
+- Overall Accuracy: 0.9989
+
+## 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 | Address                                                                                                      | Email                                                                                                   | Name                                                                                                     | Phone                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.9           | 1.0   | 15   | 0.2150          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 24}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}                                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 36}                                                | 0.0               | 0.0            | 0.0        | 0.9757           |
+| 0.1382        | 2.0   | 30   | 0.0885          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 24}                                                   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}                                               | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 36}                                                | 0.0               | 0.0            | 0.0        | 0.9757           |
+| 0.0714        | 3.0   | 45   | 0.0499          | {'precision': 0.02040816326530612, 'recall': 0.041666666666666664, 'f1': 0.027397260273972598, 'number': 24} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 27}                                              | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 39}                                               | {'precision': 0.6666666666666666, 'recall': 0.16666666666666666, 'f1': 0.26666666666666666, 'number': 36} | 0.1207            | 0.0556         | 0.0761     | 0.9863           |
+| 0.0469        | 4.0   | 60   | 0.0345          | {'precision': 0.14285714285714285, 'recall': 0.25, 'f1': 0.18181818181818182, 'number': 24}                  | {'precision': 0.875, 'recall': 0.25925925925925924, 'f1': 0.39999999999999997, 'number': 27}            | {'precision': 0.34146341463414637, 'recall': 0.358974358974359, 'f1': 0.35000000000000003, 'number': 39} | {'precision': 0.5454545454545454, 'recall': 0.6666666666666666, 'f1': 0.6, 'number': 36}                  | 0.3778            | 0.4048         | 0.3908     | 0.9910           |
+| 0.0327        | 5.0   | 75   | 0.0232          | {'precision': 0.07692307692307693, 'recall': 0.08333333333333333, 'f1': 0.08, 'number': 24}                  | {'precision': 0.7, 'recall': 0.7777777777777778, 'f1': 0.7368421052631577, 'number': 27}                | {'precision': 0.8333333333333334, 'recall': 0.8974358974358975, 'f1': 0.8641975308641975, 'number': 39}  | {'precision': 0.5, 'recall': 0.8055555555555556, 'f1': 0.6170212765957447, 'number': 36}                  | 0.5577            | 0.6905         | 0.6170     | 0.9943           |
+| 0.0224        | 6.0   | 90   | 0.0168          | {'precision': 0.16, 'recall': 0.16666666666666666, 'f1': 0.16326530612244897, 'number': 24}                  | {'precision': 0.6363636363636364, 'recall': 0.7777777777777778, 'f1': 0.7000000000000001, 'number': 27} | {'precision': 0.875, 'recall': 0.8974358974358975, 'f1': 0.8860759493670887, 'number': 39}               | {'precision': 0.5576923076923077, 'recall': 0.8055555555555556, 'f1': 0.6590909090909091, 'number': 36}   | 0.5933            | 0.7063         | 0.6449     | 0.9961           |
+| 0.0163        | 7.0   | 105  | 0.0119          | {'precision': 0.84, 'recall': 0.875, 'f1': 0.8571428571428572, 'number': 24}                                 | {'precision': 0.7857142857142857, 'recall': 0.8148148148148148, 'f1': 0.7999999999999999, 'number': 27} | {'precision': 0.9024390243902439, 'recall': 0.9487179487179487, 'f1': 0.9249999999999999, 'number': 39}  | {'precision': 0.7073170731707317, 'recall': 0.8055555555555556, 'f1': 0.7532467532467532, 'number': 36}   | 0.8074            | 0.8651         | 0.8352     | 0.9981           |
+| 0.012         | 8.0   | 120  | 0.0100          | {'precision': 0.84, 'recall': 0.875, 'f1': 0.8571428571428572, 'number': 24}                                 | {'precision': 0.7931034482758621, 'recall': 0.8518518518518519, 'f1': 0.8214285714285715, 'number': 27} | {'precision': 0.8809523809523809, 'recall': 0.9487179487179487, 'f1': 0.9135802469135802, 'number': 39}  | {'precision': 0.7948717948717948, 'recall': 0.8611111111111112, 'f1': 0.8266666666666667, 'number': 36}   | 0.8296            | 0.8889         | 0.8582     | 0.9981           |
+| 0.0097        | 9.0   | 135  | 0.0093          | {'precision': 0.8333333333333334, 'recall': 0.8333333333333334, 'f1': 0.8333333333333334, 'number': 24}      | {'precision': 0.7931034482758621, 'recall': 0.8518518518518519, 'f1': 0.8214285714285715, 'number': 27} | {'precision': 0.9024390243902439, 'recall': 0.9487179487179487, 'f1': 0.9249999999999999, 'number': 39}  | {'precision': 0.6904761904761905, 'recall': 0.8055555555555556, 'f1': 0.7435897435897436, 'number': 36}   | 0.8015            | 0.8651         | 0.8321     | 0.9984           |
+| 0.0077        | 10.0  | 150  | 0.0079          | {'precision': 0.88, 'recall': 0.9166666666666666, 'f1': 0.8979591836734694, 'number': 24}                    | {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27} | {'precision': 0.925, 'recall': 0.9487179487179487, 'f1': 0.9367088607594937, 'number': 39}               | {'precision': 0.7948717948717948, 'recall': 0.8611111111111112, 'f1': 0.8266666666666667, 'number': 36}   | 0.8561            | 0.8968         | 0.8760     | 0.9987           |
+| 0.0069        | 11.0  | 165  | 0.0084          | {'precision': 0.7916666666666666, 'recall': 0.7916666666666666, 'f1': 0.7916666666666666, 'number': 24}      | {'precision': 0.7931034482758621, 'recall': 0.8518518518518519, 'f1': 0.8214285714285715, 'number': 27} | {'precision': 0.926829268292683, 'recall': 0.9743589743589743, 'f1': 0.9500000000000001, 'number': 39}   | {'precision': 0.7948717948717948, 'recall': 0.8611111111111112, 'f1': 0.8266666666666667, 'number': 36}   | 0.8346            | 0.8810         | 0.8571     | 0.9986           |
+| 0.0066        | 12.0  | 180  | 0.0079          | {'precision': 0.8333333333333334, 'recall': 0.8333333333333334, 'f1': 0.8333333333333334, 'number': 24}      | {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27} | {'precision': 0.9047619047619048, 'recall': 0.9743589743589743, 'f1': 0.9382716049382716, 'number': 39}  | {'precision': 0.8157894736842105, 'recall': 0.8611111111111112, 'f1': 0.8378378378378377, 'number': 36}   | 0.8485            | 0.8889         | 0.8682     | 0.9986           |
+| 0.0058        | 13.0  | 195  | 0.0079          | {'precision': 0.84, 'recall': 0.875, 'f1': 0.8571428571428572, 'number': 24}                                 | {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27} | {'precision': 0.9047619047619048, 'recall': 0.9743589743589743, 'f1': 0.9382716049382716, 'number': 39}  | {'precision': 0.8378378378378378, 'recall': 0.8611111111111112, 'f1': 0.8493150684931507, 'number': 36}   | 0.8561            | 0.8968         | 0.8760     | 0.9989           |
+| 0.0054        | 14.0  | 210  | 0.0077          | {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 24}      | {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27} | {'precision': 0.926829268292683, 'recall': 0.9743589743589743, 'f1': 0.9500000000000001, 'number': 39}   | {'precision': 0.8378378378378378, 'recall': 0.8611111111111112, 'f1': 0.8493150684931507, 'number': 36}   | 0.8769            | 0.9048         | 0.8906     | 0.9989           |
+| 0.0053        | 15.0  | 225  | 0.0076          | {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 24}      | {'precision': 0.8214285714285714, 'recall': 0.8518518518518519, 'f1': 0.8363636363636364, 'number': 27} | {'precision': 0.926829268292683, 'recall': 0.9743589743589743, 'f1': 0.9500000000000001, 'number': 39}   | {'precision': 0.8378378378378378, 'recall': 0.8611111111111112, 'f1': 0.8493150684931507, 'number': 36}   | 0.8769            | 0.9048         | 0.8906     | 0.9989           |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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