End of training

README.md

@@ -0,0 +1,82 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-FUNSD-only-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-FUNSD-only-5fold
+
+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: 0.0018
+- Eader: {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67}
+- Nswer: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}
+- Uestion: {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}
+- Overall Precision: 0.9955
+- Overall Recall: 0.9978
+- Overall F1: 0.9967
+- Overall Accuracy: 0.9998
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0276        | 1.0   | 8    | 0.0029          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 0.9943181818181818, 'recall': 0.9943181818181818, 'f1': 0.9943181818181818, 'number': 176} | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9933            | 0.9955         | 0.9944     | 0.9995           |
+| 0.0298        | 2.0   | 16   | 0.0053          | {'precision': 0.9558823529411765, 'recall': 0.9701492537313433, 'f1': 0.962962962962963, 'number': 67}  | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9806763285024155, 'recall': 0.9902439024390244, 'f1': 0.9854368932038836, 'number': 205} | 0.9845            | 0.9911         | 0.9878     | 0.9993           |
+| 0.0256        | 3.0   | 24   | 0.0027          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0177        | 4.0   | 32   | 0.0024          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0129        | 5.0   | 40   | 0.0022          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.012         | 6.0   | 48   | 0.0021          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0092        | 7.0   | 56   | 0.0019          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0079        | 8.0   | 64   | 0.0018          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0075        | 9.0   | 72   | 0.0018          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0078        | 10.0  | 80   | 0.0019          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0073        | 11.0  | 88   | 0.0020          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0063        | 12.0  | 96   | 0.0021          | {'precision': 0.9705882352941176, 'recall': 0.9850746268656716, 'f1': 0.9777777777777777, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9855072463768116, 'recall': 0.9951219512195122, 'f1': 0.9902912621359223, 'number': 205} | 0.9889            | 0.9955         | 0.9922     | 0.9995           |
+| 0.0054        | 13.0  | 104  | 0.0018          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0046        | 14.0  | 112  | 0.0018          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+| 0.0052        | 15.0  | 120  | 0.0018          | {'precision': 0.9850746268656716, 'recall': 0.9850746268656716, 'f1': 0.9850746268656716, 'number': 67} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 176}                                              | {'precision': 0.9951456310679612, 'recall': 1.0, 'f1': 0.9975669099756691, 'number': 205}                | 0.9955            | 0.9978         | 0.9967     | 0.9998           |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

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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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+}