End of training

README.md

@@ -0,0 +1,79 @@
+---
+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: 0.7960
+- Answer: {'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}
+- Header: {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}
+- Question: {'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065}
+- Overall Precision: 0.7307
+- Overall Recall: 0.7938
+- Overall F1: 0.7609
+- Overall Accuracy: 0.8081
+
+## 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: 6
+- eval_batch_size: 4
+- 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 | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.6386        | 1.0   | 25   | 1.2949          | {'precision': 0.08352668213457076, 'recall': 0.08899876390605686, 'f1': 0.08617594254937162, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.36874571624400276, 'recall': 0.5051643192488263, 'f1': 0.42630744849445323, 'number': 1065} | 0.2628            | 0.3061         | 0.2828     | 0.5116           |
+| 1.0433        | 2.0   | 50   | 0.8005          | {'precision': 0.5965447154471545, 'recall': 0.7255871446229913, 'f1': 0.6547685443390964, 'number': 809}    | {'precision': 0.1111111111111111, 'recall': 0.058823529411764705, 'f1': 0.07692307692307691, 'number': 119} | {'precision': 0.6574487065120428, 'recall': 0.692018779342723, 'f1': 0.6742909423604757, 'number': 1065}    | 0.6139            | 0.6678         | 0.6398     | 0.7293           |
+| 0.6891        | 3.0   | 75   | 0.6695          | {'precision': 0.6335650446871897, 'recall': 0.788627935723115, 'f1': 0.7026431718061674, 'number': 809}     | {'precision': 0.3246753246753247, 'recall': 0.21008403361344538, 'f1': 0.25510204081632654, 'number': 119}  | {'precision': 0.7085862966175195, 'recall': 0.7671361502347418, 'f1': 0.7366997294860236, 'number': 1065}   | 0.6616            | 0.7426         | 0.6998     | 0.7752           |
+| 0.532         | 4.0   | 100  | 0.6270          | {'precision': 0.6573787409700722, 'recall': 0.7873918417799752, 'f1': 0.7165354330708661, 'number': 809}    | {'precision': 0.2361111111111111, 'recall': 0.2857142857142857, 'f1': 0.25855513307984795, 'number': 119}   | {'precision': 0.7153284671532847, 'recall': 0.828169014084507, 'f1': 0.7676240208877285, 'number': 1065}    | 0.6620            | 0.7792         | 0.7158     | 0.7961           |
+| 0.4184        | 5.0   | 125  | 0.6174          | {'precision': 0.6837160751565762, 'recall': 0.8096415327564895, 'f1': 0.7413695529145445, 'number': 809}    | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}    | {'precision': 0.7734657039711191, 'recall': 0.8046948356807512, 'f1': 0.7887712839392544, 'number': 1065}   | 0.7102            | 0.7757         | 0.7415     | 0.8025           |
+| 0.3264        | 6.0   | 150  | 0.6493          | {'precision': 0.6905537459283387, 'recall': 0.7861557478368356, 'f1': 0.7352601156069365, 'number': 809}    | {'precision': 0.310126582278481, 'recall': 0.4117647058823529, 'f1': 0.35379061371841153, 'number': 119}    | {'precision': 0.7713523131672598, 'recall': 0.8140845070422535, 'f1': 0.7921425308359983, 'number': 1065}   | 0.7045            | 0.7787         | 0.7398     | 0.8008           |
+| 0.2661        | 7.0   | 175  | 0.6587          | {'precision': 0.6857440166493236, 'recall': 0.8145859085290482, 'f1': 0.7446327683615819, 'number': 809}    | {'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119}  | {'precision': 0.7720970537261699, 'recall': 0.8366197183098592, 'f1': 0.8030644434429923, 'number': 1065}   | 0.7089            | 0.7993         | 0.7514     | 0.8038           |
+| 0.2246        | 8.0   | 200  | 0.7115          | {'precision': 0.7111356119073869, 'recall': 0.7972805933250927, 'f1': 0.7517482517482517, 'number': 809}    | {'precision': 0.2983425414364641, 'recall': 0.453781512605042, 'f1': 0.36, 'number': 119}                   | {'precision': 0.7891402714932126, 'recall': 0.8187793427230047, 'f1': 0.8036866359447005, 'number': 1065}   | 0.7164            | 0.7883         | 0.7506     | 0.8074           |
+| 0.1928        | 9.0   | 225  | 0.7130          | {'precision': 0.7094668117519043, 'recall': 0.8059332509270705, 'f1': 0.7546296296296295, 'number': 809}    | {'precision': 0.3178294573643411, 'recall': 0.3445378151260504, 'f1': 0.33064516129032256, 'number': 119}   | {'precision': 0.7908025247971145, 'recall': 0.8234741784037559, 'f1': 0.8068077276908925, 'number': 1065}   | 0.7279            | 0.7878         | 0.7566     | 0.8042           |
+| 0.1598        | 10.0  | 250  | 0.7375          | {'precision': 0.7242937853107345, 'recall': 0.792336217552534, 'f1': 0.756788665879575, 'number': 809}      | {'precision': 0.375, 'recall': 0.42857142857142855, 'f1': 0.39999999999999997, 'number': 119}               | {'precision': 0.788858939802336, 'recall': 0.8244131455399061, 'f1': 0.8062442607897153, 'number': 1065}    | 0.7357            | 0.7878         | 0.7608     | 0.8099           |
+| 0.1444        | 11.0  | 275  | 0.7719          | {'precision': 0.7027896995708155, 'recall': 0.8096415327564895, 'f1': 0.7524411257897761, 'number': 809}    | {'precision': 0.34814814814814815, 'recall': 0.3949579831932773, 'f1': 0.3700787401574803, 'number': 119}   | {'precision': 0.7825311942959001, 'recall': 0.8244131455399061, 'f1': 0.8029263831732967, 'number': 1065}   | 0.7218            | 0.7928         | 0.7556     | 0.8008           |
+| 0.1251        | 12.0  | 300  | 0.7758          | {'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809}    | {'precision': 0.38095238095238093, 'recall': 0.40336134453781514, 'f1': 0.39183673469387753, 'number': 119} | {'precision': 0.7880434782608695, 'recall': 0.8169014084507042, 'f1': 0.8022130013831259, 'number': 1065}   | 0.7323            | 0.7878         | 0.7590     | 0.8077           |
+| 0.1124        | 13.0  | 325  | 0.7878          | {'precision': 0.7150776053215078, 'recall': 0.7972805933250927, 'f1': 0.7539450613676213, 'number': 809}    | {'precision': 0.38848920863309355, 'recall': 0.453781512605042, 'f1': 0.4186046511627907, 'number': 119}    | {'precision': 0.7922312556458898, 'recall': 0.8234741784037559, 'f1': 0.8075506445672191, 'number': 1065}   | 0.7337            | 0.7908         | 0.7612     | 0.8094           |
+| 0.1077        | 14.0  | 350  | 0.7945          | {'precision': 0.7136612021857923, 'recall': 0.8071693448702101, 'f1': 0.7575406032482598, 'number': 809}    | {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}  | {'precision': 0.7887197851387645, 'recall': 0.8272300469483568, 'f1': 0.8075160403299725, 'number': 1065}   | 0.7295            | 0.7958         | 0.7612     | 0.8098           |
+| 0.1001        | 15.0  | 375  | 0.7960          | {'precision': 0.7169603524229075, 'recall': 0.8046971569839307, 'f1': 0.7582993593476993, 'number': 809}    | {'precision': 0.36619718309859156, 'recall': 0.4369747899159664, 'f1': 0.39846743295019166, 'number': 119}  | {'precision': 0.7883408071748879, 'recall': 0.8253521126760563, 'f1': 0.8064220183486238, 'number': 1065}   | 0.7307            | 0.7938         | 0.7609     | 0.8081           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.3

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