End of training

README.md

@@ -0,0 +1,76 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+model-index:
+- name: layoutlm-Synthetic-only
+  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-Synthetic-only
+
+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: 1.0120
+- Eader: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57}
+- Nswer: {'precision': 0.07565011820330969, 'recall': 0.22695035460992907, 'f1': 0.11347517730496454, 'number': 141}
+- Uestion: {'precision': 0.09090909090909091, 'recall': 0.2360248447204969, 'f1': 0.13126079447322972, 'number': 161}
+- Overall Precision: 0.0827
+- Overall Recall: 0.1950
+- Overall F1: 0.1162
+- Overall Accuracy: 0.6022
+
+## 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: 9
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                      | Nswer                                                                                                           | Uestion                                                                                                        | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.4643        | 1.0   | 4    | 1.4050          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.003937007874015748, 'recall': 0.0070921985815602835, 'f1': 0.005063291139240506, 'number': 141} | {'precision': 0.013215859030837005, 'recall': 0.055900621118012424, 'f1': 0.02137767220902613, 'number': 161}  | 0.0078            | 0.0279         | 0.0122     | 0.2729           |
+| 1.109         | 2.0   | 8    | 1.2847          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 141}                                                     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 161}                                                    | 0.0               | 0.0            | 0.0        | 0.3613           |
+| 0.8554        | 3.0   | 12   | 1.1887          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.016172506738544475, 'recall': 0.0425531914893617, 'f1': 0.023437500000000003, 'number': 141}    | {'precision': 0.010554089709762533, 'recall': 0.024844720496894408, 'f1': 0.014814814814814814, 'number': 161} | 0.0133            | 0.0279         | 0.0180     | 0.4262           |
+| 0.6863        | 4.0   | 16   | 1.1168          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.029411764705882353, 'recall': 0.11347517730496454, 'f1': 0.04671532846715328, 'number': 141}    | {'precision': 0.034358047016274866, 'recall': 0.11801242236024845, 'f1': 0.05322128851540617, 'number': 161}   | 0.0319            | 0.0975         | 0.0481     | 0.5231           |
+| 0.5693        | 5.0   | 20   | 1.0760          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.05, 'recall': 0.1773049645390071, 'f1': 0.078003120124805, 'number': 141}                       | {'precision': 0.057803468208092484, 'recall': 0.18633540372670807, 'f1': 0.08823529411764705, 'number': 161}   | 0.0539            | 0.1532         | 0.0798     | 0.5545           |
+| 0.4404        | 6.0   | 24   | 1.0488          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.06329113924050633, 'recall': 0.2127659574468085, 'f1': 0.09756097560975611, 'number': 141}      | {'precision': 0.06534653465346535, 'recall': 0.20496894409937888, 'f1': 0.0990990990990991, 'number': 161}     | 0.0642            | 0.1755         | 0.0940     | 0.5750           |
+| 0.3635        | 7.0   | 28   | 1.0277          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07534246575342465, 'recall': 0.23404255319148937, 'f1': 0.11398963730569948, 'number': 141}     | {'precision': 0.08053691275167785, 'recall': 0.2236024844720497, 'f1': 0.11842105263157895, 'number': 161}     | 0.0776            | 0.1922         | 0.1106     | 0.5998           |
+| 0.3201        | 8.0   | 32   | 1.0163          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07801418439716312, 'recall': 0.23404255319148937, 'f1': 0.11702127659574468, 'number': 141}     | {'precision': 0.08747044917257683, 'recall': 0.22981366459627328, 'f1': 0.1267123287671233, 'number': 161}     | 0.0823            | 0.1950         | 0.1157     | 0.6037           |
+| 0.2915        | 9.0   | 36   | 1.0120          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 57} | {'precision': 0.07565011820330969, 'recall': 0.22695035460992907, 'f1': 0.11347517730496454, 'number': 141}     | {'precision': 0.09090909090909091, 'recall': 0.2360248447204969, 'f1': 0.13126079447322972, 'number': 161}     | 0.0827            | 0.1950         | 0.1162     | 0.6022           |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

logs/events.out.tfevents.1741611990.DESKTOP-HA84SVN.1143408.0

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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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+  "extra_special_tokens": {},
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+}