End of training

README.md

@@ -0,0 +1,81 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+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: 1.0554
+- Answer: {'precision': 0.4105691056910569, 'recall': 0.49938195302843014, 'f1': 0.45064138315672064, 'number': 809}
+- Header: {'precision': 0.36470588235294116, 'recall': 0.2605042016806723, 'f1': 0.30392156862745096, 'number': 119}
+- Question: {'precision': 0.48371104815864024, 'recall': 0.6413145539906103, 'f1': 0.551473556721841, 'number': 1065}
+- Overall Precision: 0.4506
+- Overall Recall: 0.5610
+- Overall F1: 0.4998
+- Overall Accuracy: 0.6149
+
+## 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 | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7702        | 1.0   | 10   | 1.5768          | {'precision': 0.040923399790136414, 'recall': 0.048207663782447466, 'f1': 0.04426787741203178, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.38510301109350237, 'recall': 0.22816901408450704, 'f1': 0.28655660377358494, 'number': 1065} | 0.1780            | 0.1415         | 0.1577     | 0.3540           |
+| 1.4963        | 2.0   | 20   | 1.4062          | {'precision': 0.1941294196130754, 'recall': 0.35970333745364647, 'f1': 0.2521663778162912, 'number': 809}     | {'precision': 0.03571428571428571, 'recall': 0.01680672268907563, 'f1': 0.022857142857142857, 'number': 119} | {'precision': 0.28505291005291006, 'recall': 0.40469483568075115, 'f1': 0.33449747768723326, 'number': 1065} | 0.2361            | 0.3633         | 0.2862     | 0.4204           |
+| 1.2983        | 3.0   | 30   | 1.2020          | {'precision': 0.23365122615803816, 'recall': 0.42398022249690975, 'f1': 0.3012736056214317, 'number': 809}    | {'precision': 0.13846153846153847, 'recall': 0.07563025210084033, 'f1': 0.09782608695652173, 'number': 119}  | {'precision': 0.3307776560788609, 'recall': 0.5671361502347417, 'f1': 0.4178484953303355, 'number': 1065}    | 0.2846            | 0.4797         | 0.3572     | 0.4806           |
+| 1.1603        | 4.0   | 40   | 1.1227          | {'precision': 0.2243436754176611, 'recall': 0.34857849196538937, 'f1': 0.2729912875121007, 'number': 809}     | {'precision': 0.2222222222222222, 'recall': 0.18487394957983194, 'f1': 0.2018348623853211, 'number': 119}    | {'precision': 0.35071846726982436, 'recall': 0.6187793427230047, 'f1': 0.4476902173913044, 'number': 1065}   | 0.2977            | 0.4832         | 0.3684     | 0.5265           |
+| 1.0771        | 5.0   | 50   | 1.0953          | {'precision': 0.2655198204936425, 'recall': 0.4388133498145859, 'f1': 0.33084808946877914, 'number': 809}     | {'precision': 0.26666666666666666, 'recall': 0.20168067226890757, 'f1': 0.22966507177033493, 'number': 119}  | {'precision': 0.3632745878339966, 'recall': 0.6, 'f1': 0.45254957507082155, 'number': 1065}                  | 0.3195            | 0.5108         | 0.3931     | 0.5453           |
+| 1.0102        | 6.0   | 60   | 1.0388          | {'precision': 0.30492285084496695, 'recall': 0.5129789864029666, 'f1': 0.3824884792626728, 'number': 809}     | {'precision': 0.3283582089552239, 'recall': 0.18487394957983194, 'f1': 0.2365591397849462, 'number': 119}    | {'precision': 0.4519543973941368, 'recall': 0.5211267605633803, 'f1': 0.4840819886611426, 'number': 1065}    | 0.3735            | 0.4977         | 0.4268     | 0.5839           |
+| 0.9312        | 7.0   | 70   | 1.0265          | {'precision': 0.32556131260794474, 'recall': 0.46600741656365885, 'f1': 0.3833248601931876, 'number': 809}    | {'precision': 0.2828282828282828, 'recall': 0.23529411764705882, 'f1': 0.25688073394495414, 'number': 119}   | {'precision': 0.47326007326007324, 'recall': 0.6065727699530516, 'f1': 0.5316872427983539, 'number': 1065}   | 0.4008            | 0.5273         | 0.4555     | 0.5969           |
+| 0.8732        | 8.0   | 80   | 1.0508          | {'precision': 0.33681073025335323, 'recall': 0.5587144622991347, 'f1': 0.4202696420269642, 'number': 809}     | {'precision': 0.3561643835616438, 'recall': 0.2184873949579832, 'f1': 0.2708333333333333, 'number': 119}     | {'precision': 0.4556126192223037, 'recall': 0.5830985915492958, 'f1': 0.5115321252059308, 'number': 1065}    | 0.3956            | 0.5514         | 0.4607     | 0.5947           |
+| 0.808         | 9.0   | 90   | 1.0282          | {'precision': 0.36807511737089205, 'recall': 0.484548825710754, 'f1': 0.41835645677694777, 'number': 809}     | {'precision': 0.3058823529411765, 'recall': 0.2184873949579832, 'f1': 0.2549019607843137, 'number': 119}     | {'precision': 0.46965317919075145, 'recall': 0.6103286384976526, 'f1': 0.5308289097590853, 'number': 1065}   | 0.4215            | 0.5359         | 0.4718     | 0.6085           |
+| 0.7928        | 10.0  | 100  | 1.0475          | {'precision': 0.38683498647430115, 'recall': 0.5302843016069221, 'f1': 0.44734098018769547, 'number': 809}    | {'precision': 0.36363636363636365, 'recall': 0.23529411764705882, 'f1': 0.2857142857142857, 'number': 119}   | {'precision': 0.49149922720247297, 'recall': 0.5971830985915493, 'f1': 0.5392115303094531, 'number': 1065}   | 0.4407            | 0.5484         | 0.4887     | 0.6054           |
+| 0.7164        | 11.0  | 110  | 1.0310          | {'precision': 0.38894472361809046, 'recall': 0.4783683559950556, 'f1': 0.4290465631929047, 'number': 809}     | {'precision': 0.38961038961038963, 'recall': 0.25210084033613445, 'f1': 0.30612244897959184, 'number': 119}  | {'precision': 0.4831223628691983, 'recall': 0.6450704225352113, 'f1': 0.5524728588661038, 'number': 1065}    | 0.4427            | 0.5539         | 0.4921     | 0.6149           |
+| 0.707         | 12.0  | 120  | 1.0295          | {'precision': 0.40441176470588236, 'recall': 0.4758961681087763, 'f1': 0.43725156161272005, 'number': 809}    | {'precision': 0.3655913978494624, 'recall': 0.2857142857142857, 'f1': 0.32075471698113206, 'number': 119}    | {'precision': 0.4713031735313977, 'recall': 0.6553990610328638, 'f1': 0.5483110761979575, 'number': 1065}    | 0.4422            | 0.5605         | 0.4944     | 0.6172           |
+| 0.6765        | 13.0  | 130  | 1.0494          | {'precision': 0.4107485604606526, 'recall': 0.5290482076637825, 'f1': 0.46245272825499734, 'number': 809}     | {'precision': 0.4305555555555556, 'recall': 0.2605042016806723, 'f1': 0.324607329842932, 'number': 119}      | {'precision': 0.4879825200291333, 'recall': 0.6291079812206573, 'f1': 0.5496308449548811, 'number': 1065}    | 0.4540            | 0.5665         | 0.5040     | 0.6156           |
+| 0.6489        | 14.0  | 140  | 1.0557          | {'precision': 0.4165009940357853, 'recall': 0.5179233621755254, 'f1': 0.4617079889807163, 'number': 809}      | {'precision': 0.4, 'recall': 0.2689075630252101, 'f1': 0.32160804020100503, 'number': 119}                   | {'precision': 0.4891304347826087, 'recall': 0.6338028169014085, 'f1': 0.5521472392638037, 'number': 1065}    | 0.4566            | 0.5650         | 0.5050     | 0.6142           |
+| 0.6397        | 15.0  | 150  | 1.0554          | {'precision': 0.4105691056910569, 'recall': 0.49938195302843014, 'f1': 0.45064138315672064, 'number': 809}    | {'precision': 0.36470588235294116, 'recall': 0.2605042016806723, 'f1': 0.30392156862745096, 'number': 119}   | {'precision': 0.48371104815864024, 'recall': 0.6413145539906103, 'f1': 0.551473556721841, 'number': 1065}    | 0.4506            | 0.5610         | 0.4998     | 0.6149           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1712841890.90d122649371.3714.0

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preprocessor_config.json

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special_tokens_map.json

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tokenizer_config.json

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+}