End of training

README.md

@@ -0,0 +1,78 @@
+---
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-custom_no_text
+  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-custom_no_text
+
+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: 0.1523
+-  Noise: {'precision': 0.8811544991511036, 'recall': 0.8994800693240901, 'f1': 0.8902229845626072, 'number': 577}
+-  Signal: {'precision': 0.8675721561969439, 'recall': 0.8856152512998267, 'f1': 0.8765008576329331, 'number': 577}
+- Overall Precision: 0.8744
+- Overall Recall: 0.8925
+- Overall F1: 0.8834
+- Overall Accuracy: 0.9664
+
+## 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: 8
+- 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 |  Noise                                                                                                   |  Signal                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3886        | 1.0   | 18   | 0.2452          | {'precision': 0.6213235294117647, 'recall': 0.58578856152513, 'f1': 0.6030330062444246, 'number': 577}   | {'precision': 0.6323529411764706, 'recall': 0.5961871750433275, 'f1': 0.6137377341659233, 'number': 577} | 0.6268            | 0.5910         | 0.6084     | 0.8992           |
+| 0.1673        | 2.0   | 36   | 0.1441          | {'precision': 0.7667269439421338, 'recall': 0.7348353552859619, 'f1': 0.7504424778761062, 'number': 577} | {'precision': 0.7450271247739603, 'recall': 0.7140381282495667, 'f1': 0.7292035398230089, 'number': 577} | 0.7559            | 0.7244         | 0.7398     | 0.9356           |
+| 0.0959        | 3.0   | 54   | 0.1168          | {'precision': 0.8131487889273357, 'recall': 0.8145580589254766, 'f1': 0.8138528138528138, 'number': 577} | {'precision': 0.7941176470588235, 'recall': 0.7954939341421143, 'f1': 0.7948051948051947, 'number': 577} | 0.8036            | 0.8050         | 0.8043     | 0.9510           |
+| 0.0622        | 4.0   | 72   | 0.1166          | {'precision': 0.8402061855670103, 'recall': 0.8474870017331022, 'f1': 0.8438308886971526, 'number': 577} | {'precision': 0.8333333333333334, 'recall': 0.8405545927209706, 'f1': 0.8369283865401207, 'number': 577} | 0.8368            | 0.8440         | 0.8404     | 0.9591           |
+| 0.0424        | 5.0   | 90   | 0.1325          | {'precision': 0.8476027397260274, 'recall': 0.8578856152512998, 'f1': 0.8527131782945737, 'number': 577} | {'precision': 0.839041095890411, 'recall': 0.8492201039861352, 'f1': 0.8440999138673558, 'number': 577}  | 0.8433            | 0.8536         | 0.8484     | 0.9586           |
+| 0.031         | 6.0   | 108  | 0.1167          | {'precision': 0.8720136518771331, 'recall': 0.8856152512998267, 'f1': 0.878761822871883, 'number': 577}  | {'precision': 0.8583617747440273, 'recall': 0.8717504332755632, 'f1': 0.8650042992261393, 'number': 577} | 0.8652            | 0.8787         | 0.8719     | 0.9628           |
+| 0.0213        | 7.0   | 126  | 0.1339          | {'precision': 0.8610634648370498, 'recall': 0.8700173310225303, 'f1': 0.8655172413793105, 'number': 577} | {'precision': 0.855917667238422, 'recall': 0.8648180242634316, 'f1': 0.860344827586207, 'number': 577}   | 0.8585            | 0.8674         | 0.8629     | 0.9608           |
+| 0.0159        | 8.0   | 144  | 0.1335          | {'precision': 0.8692699490662139, 'recall': 0.8873483535528596, 'f1': 0.8782161234991425, 'number': 577} | {'precision': 0.8590831918505942, 'recall': 0.8769497400346621, 'f1': 0.8679245283018868, 'number': 577} | 0.8642            | 0.8821         | 0.8731     | 0.9630           |
+| 0.0117        | 9.0   | 162  | 0.1489          | {'precision': 0.8686006825938567, 'recall': 0.8821490467937608, 'f1': 0.8753224419604471, 'number': 577} | {'precision': 0.8600682593856656, 'recall': 0.8734835355285961, 'f1': 0.8667239896818572, 'number': 577} | 0.8643            | 0.8778         | 0.8710     | 0.9622           |
+| 0.011         | 10.0  | 180  | 0.1593          | {'precision': 0.8623063683304647, 'recall': 0.8682842287694974, 'f1': 0.8652849740932642, 'number': 577} | {'precision': 0.8519793459552496, 'recall': 0.8578856152512998, 'f1': 0.854922279792746, 'number': 577}  | 0.8571            | 0.8631         | 0.8601     | 0.9600           |
+| 0.0094        | 11.0  | 198  | 0.1336          | {'precision': 0.8896434634974533, 'recall': 0.9081455805892548, 'f1': 0.8987993138936535, 'number': 577} | {'precision': 0.8760611205432938, 'recall': 0.8942807625649913, 'f1': 0.8850771869639794, 'number': 577} | 0.8829            | 0.9012         | 0.8919     | 0.9686           |
+| 0.0066        | 12.0  | 216  | 0.1357          | {'precision': 0.8928571428571429, 'recall': 0.9098786828422877, 'f1': 0.9012875536480687, 'number': 577} | {'precision': 0.8792517006802721, 'recall': 0.8960138648180243, 'f1': 0.8875536480686695, 'number': 577} | 0.8861            | 0.9029         | 0.8944     | 0.9692           |
+| 0.0072        | 13.0  | 234  | 0.1528          | {'precision': 0.8830508474576271, 'recall': 0.902946273830156, 'f1': 0.8928877463581834, 'number': 577}  | {'precision': 0.8711864406779661, 'recall': 0.8908145580589255, 'f1': 0.8808911739502999, 'number': 577} | 0.8771            | 0.8969         | 0.8869     | 0.9670           |
+| 0.0061        | 14.0  | 252  | 0.1552          | {'precision': 0.8779661016949153, 'recall': 0.8977469670710572, 'f1': 0.8877463581833762, 'number': 577} | {'precision': 0.8661016949152542, 'recall': 0.8856152512998267, 'f1': 0.8757497857754927, 'number': 577} | 0.8720            | 0.8917         | 0.8817     | 0.9664           |
+| 0.0054        | 15.0  | 270  | 0.1523          | {'precision': 0.8811544991511036, 'recall': 0.8994800693240901, 'f1': 0.8902229845626072, 'number': 577} | {'precision': 0.8675721561969439, 'recall': 0.8856152512998267, 'f1': 0.8765008576329331, 'number': 577} | 0.8744            | 0.8925         | 0.8834     | 0.9664           |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0

logs/events.out.tfevents.1704797153.854ba1d670db.1167.0

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preprocessor_config.json

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+  "do_resize": true,
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+  "image_processor_type": "LayoutLMv2ImageProcessor",
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+  "processor_class": "LayoutLMv2Processor",
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special_tokens_map.json

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+{
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+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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tokenizer_config.json

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+  "processor_class": "LayoutLMv2Processor",
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+  "tokenizer_class": "LayoutLMv2Tokenizer",
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+}