End of training

README.md

@@ -0,0 +1,80 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-FUNSDxSynthetic-5fold
+  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-FUNSDxSynthetic-5fold
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0137
+- Eader: {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}
+- Nswer: {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256}
+- Uestion: {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}
+- Overall Precision: 0.9850
+- Overall Recall: 0.9867
+- Overall F1: 0.9858
+- Overall Accuracy: 0.9965
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                   | Nswer                                                                                           | Uestion                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0504        | 1.0   | 11   | 0.0183          | {'precision': 0.9722222222222222, 'recall': 0.9859154929577465, 'f1': 0.979020979020979, 'number': 71}  | {'precision': 0.984375, 'recall': 0.984375, 'f1': 0.984375, 'number': 256}                      | {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273}  | 0.9801            | 0.9867         | 0.9834     | 0.9956           |
+| 0.0459        | 2.0   | 22   | 0.0175          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256}                | {'precision': 0.9747292418772563, 'recall': 0.989010989010989, 'f1': 0.9818181818181818, 'number': 273}  | 0.9801            | 0.9867         | 0.9834     | 0.9953           |
+| 0.0289        | 3.0   | 33   | 0.0146          | {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9890909090909091, 'recall': 0.9963369963369964, 'f1': 0.9927007299270072, 'number': 273} | 0.9900            | 0.9917         | 0.9908     | 0.9965           |
+| 0.0243        | 4.0   | 44   | 0.0157          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273}  | 0.9834            | 0.9867         | 0.9850     | 0.9958           |
+| 0.0215        | 5.0   | 55   | 0.0129          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921875, 'recall': 0.9921875, 'f1': 0.9921875, 'number': 256}                   | {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}  | 0.9850            | 0.9883         | 0.9867     | 0.9975           |
+| 0.0176        | 6.0   | 66   | 0.0151          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921875, 'recall': 0.9921875, 'f1': 0.9921875, 'number': 256}                   | {'precision': 0.9782608695652174, 'recall': 0.989010989010989, 'f1': 0.9836065573770493, 'number': 273}  | 0.9834            | 0.9883         | 0.9859     | 0.9963           |
+| 0.0151        | 7.0   | 77   | 0.0149          | {'precision': 0.9583333333333334, 'recall': 0.971830985915493, 'f1': 0.965034965034965, 'number': 71}   | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9817518248175182, 'recall': 0.9853479853479854, 'f1': 0.9835466179159049, 'number': 273} | 0.9834            | 0.985          | 0.9842     | 0.9968           |
+| 0.0136        | 8.0   | 88   | 0.0142          | {'precision': 0.9583333333333334, 'recall': 0.971830985915493, 'f1': 0.965034965034965, 'number': 71}   | {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256}                | {'precision': 0.9781818181818182, 'recall': 0.9853479853479854, 'f1': 0.9817518248175183, 'number': 273} | 0.9801            | 0.985          | 0.9825     | 0.9965           |
+| 0.0136        | 9.0   | 99   | 0.0148          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}  | 0.9850            | 0.9867         | 0.9858     | 0.9963           |
+| 0.0103        | 10.0  | 110  | 0.0138          | {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9854545454545455, 'recall': 0.9926739926739927, 'f1': 0.989051094890511, 'number': 273}  | 0.9884            | 0.99           | 0.9892     | 0.9965           |
+| 0.0091        | 11.0  | 121  | 0.0136          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9854014598540146, 'recall': 0.989010989010989, 'f1': 0.9872029250457038, 'number': 273}  | 0.9867            | 0.9867         | 0.9867     | 0.9968           |
+| 0.0081        | 12.0  | 132  | 0.0131          | {'precision': 0.9859154929577465, 'recall': 0.9859154929577465, 'f1': 0.9859154929577465, 'number': 71} | {'precision': 0.98828125, 'recall': 0.98828125, 'f1': 0.98828125, 'number': 256}                | {'precision': 0.9890510948905109, 'recall': 0.9926739926739927, 'f1': 0.9908592321755026, 'number': 273} | 0.9884            | 0.99           | 0.9892     | 0.9973           |
+| 0.0098        | 13.0  | 143  | 0.0136          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9854014598540146, 'recall': 0.989010989010989, 'f1': 0.9872029250457038, 'number': 273}  | 0.9867            | 0.9867         | 0.9867     | 0.9968           |
+| 0.0066        | 14.0  | 154  | 0.0139          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}  | 0.9850            | 0.9867         | 0.9858     | 0.9965           |
+| 0.007         | 15.0  | 165  | 0.0137          | {'precision': 0.971830985915493, 'recall': 0.971830985915493, 'f1': 0.971830985915493, 'number': 71}    | {'precision': 0.9921568627450981, 'recall': 0.98828125, 'f1': 0.990215264187867, 'number': 256} | {'precision': 0.9818181818181818, 'recall': 0.989010989010989, 'f1': 0.9854014598540145, 'number': 273}  | 0.9850            | 0.9867         | 0.9858     | 0.9965           |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

logs/events.out.tfevents.1741259668.DESKTOP-HA84SVN.94162.4

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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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+}