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
+  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: 0.7293
+- Answer: {'precision': 0.7119205298013245, 'recall': 0.7972805933250927, 'f1': 0.7521865889212828, 'number': 809}
+- Header: {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}
+- Question: {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}
+- Overall Precision: 0.7237
+- Overall Recall: 0.7873
+- Overall F1: 0.7541
+- Overall Accuracy: 0.7994
+
+## 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 OptimizerNames.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 | Answer                                                                                                       | Header                                                                                                         | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8121        | 1.0   | 10   | 1.5778          | {'precision': 0.02838221381267739, 'recall': 0.037082818294190356, 'f1': 0.03215434083601286, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.19402985074626866, 'recall': 0.20751173708920187, 'f1': 0.20054446460980033, 'number': 1065} | 0.1143            | 0.1259         | 0.1198     | 0.4198           |
+| 1.4301        | 2.0   | 20   | 1.2246          | {'precision': 0.15695067264573992, 'recall': 0.12978986402966625, 'f1': 0.14208389715832204, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.46811702925731435, 'recall': 0.5859154929577465, 'f1': 0.5204336947456214, 'number': 1065}   | 0.3641            | 0.3658         | 0.3650     | 0.5873           |
+| 1.0718        | 3.0   | 30   | 0.9296          | {'precision': 0.48633879781420764, 'recall': 0.5500618046971569, 'f1': 0.5162412993039442, 'number': 809}    | {'precision': 0.029411764705882353, 'recall': 0.008403361344537815, 'f1': 0.013071895424836602, 'number': 119} | {'precision': 0.5914927768860353, 'recall': 0.692018779342723, 'f1': 0.6378191259195154, 'number': 1065}     | 0.5390            | 0.5936         | 0.5649     | 0.7243           |
+| 0.814         | 4.0   | 40   | 0.7678          | {'precision': 0.5895765472312704, 'recall': 0.6711990111248455, 'f1': 0.6277456647398845, 'number': 809}     | {'precision': 0.1724137931034483, 'recall': 0.12605042016806722, 'f1': 0.14563106796116504, 'number': 119}     | {'precision': 0.64, 'recall': 0.7661971830985915, 'f1': 0.6974358974358974, 'number': 1065}                  | 0.6018            | 0.6894         | 0.6427     | 0.7677           |
+| 0.6527        | 5.0   | 50   | 0.7178          | {'precision': 0.6438653637350705, 'recall': 0.7330037082818294, 'f1': 0.6855491329479768, 'number': 809}     | {'precision': 0.3, 'recall': 0.17647058823529413, 'f1': 0.22222222222222224, 'number': 119}                    | {'precision': 0.675, 'recall': 0.8112676056338028, 'f1': 0.7368869936034115, 'number': 1065}                 | 0.6508            | 0.7416         | 0.6932     | 0.7851           |
+| 0.5605        | 6.0   | 60   | 0.6839          | {'precision': 0.6655982905982906, 'recall': 0.7700865265760197, 'f1': 0.7140401146131805, 'number': 809}     | {'precision': 0.29411764705882354, 'recall': 0.21008403361344538, 'f1': 0.2450980392156863, 'number': 119}     | {'precision': 0.7222222222222222, 'recall': 0.8178403755868544, 'f1': 0.7670629678555702, 'number': 1065}    | 0.6821            | 0.7622         | 0.7199     | 0.7950           |
+| 0.4793        | 7.0   | 70   | 0.6672          | {'precision': 0.6862955032119914, 'recall': 0.792336217552534, 'f1': 0.7355134825014343, 'number': 809}      | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119}        | {'precision': 0.7552083333333334, 'recall': 0.8169014084507042, 'f1': 0.7848443843031124, 'number': 1065}    | 0.7023            | 0.7717         | 0.7354     | 0.8014           |
+| 0.4262        | 8.0   | 80   | 0.6747          | {'precision': 0.6762886597938145, 'recall': 0.8108776266996292, 'f1': 0.7374929735806633, 'number': 809}     | {'precision': 0.24509803921568626, 'recall': 0.21008403361344538, 'f1': 0.22624434389140272, 'number': 119}    | {'precision': 0.7618228718830611, 'recall': 0.831924882629108, 'f1': 0.7953321364452423, 'number': 1065}     | 0.7011            | 0.7863         | 0.7412     | 0.8000           |
+| 0.3773        | 9.0   | 90   | 0.6885          | {'precision': 0.6932314410480349, 'recall': 0.7849196538936959, 'f1': 0.736231884057971, 'number': 809}      | {'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119}    | {'precision': 0.7628865979381443, 'recall': 0.8338028169014085, 'f1': 0.7967698519515478, 'number': 1065}    | 0.7101            | 0.7817         | 0.7442     | 0.8015           |
+| 0.3709        | 10.0  | 100  | 0.6915          | {'precision': 0.6982758620689655, 'recall': 0.8009888751545118, 'f1': 0.7461139896373058, 'number': 809}     | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119}      | {'precision': 0.7681660899653979, 'recall': 0.8338028169014085, 'f1': 0.7996398018910401, 'number': 1065}    | 0.7170            | 0.7868         | 0.7502     | 0.8041           |
+| 0.3123        | 11.0  | 110  | 0.7102          | {'precision': 0.7045951859956237, 'recall': 0.796044499381953, 'f1': 0.7475333720255369, 'number': 809}      | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119}                       | {'precision': 0.7717013888888888, 'recall': 0.8347417840375587, 'f1': 0.8019846639603068, 'number': 1065}    | 0.7177            | 0.7873         | 0.7509     | 0.8003           |
+| 0.2944        | 12.0  | 120  | 0.7214          | {'precision': 0.7073707370737073, 'recall': 0.7948084054388134, 'f1': 0.7485448195576251, 'number': 809}     | {'precision': 0.34285714285714286, 'recall': 0.3025210084033613, 'f1': 0.32142857142857145, 'number': 119}     | {'precision': 0.774390243902439, 'recall': 0.8347417840375587, 'f1': 0.8034342521464076, 'number': 1065}     | 0.7253            | 0.7868         | 0.7548     | 0.8031           |
+| 0.286         | 13.0  | 130  | 0.7283          | {'precision': 0.7105263157894737, 'recall': 0.8009888751545118, 'f1': 0.7530505520046484, 'number': 809}     | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}      | {'precision': 0.7801418439716312, 'recall': 0.8262910798122066, 'f1': 0.8025535795713634, 'number': 1065}    | 0.7274            | 0.7858         | 0.7554     | 0.7994           |
+| 0.2609        | 14.0  | 140  | 0.7260          | {'precision': 0.7144444444444444, 'recall': 0.7948084054388134, 'f1': 0.7524868344060853, 'number': 809}     | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119}      | {'precision': 0.7796312554872695, 'recall': 0.8338028169014085, 'f1': 0.8058076225045372, 'number': 1065}    | 0.7279            | 0.7868         | 0.7562     | 0.8000           |
+| 0.264         | 15.0  | 150  | 0.7293          | {'precision': 0.7119205298013245, 'recall': 0.7972805933250927, 'f1': 0.7521865889212828, 'number': 809}     | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}     | {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}    | 0.7237            | 0.7873         | 0.7541     | 0.7994           |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

logs/events.out.tfevents.1741155844.f146e1f583ac.417.0

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preprocessor_config.json

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+{
+  "apply_ocr": true,
+  "do_resize": true,
+  "image_processor_type": "LayoutLMv2ImageProcessor",
+  "ocr_lang": null,
+  "processor_class": "LayoutLMv2Processor",
+  "resample": 2,
+  "size": {
+    "height": 224,
+    "width": 224
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+  "tesseract_config": ""
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "100": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "only_label_first_subword": true,
+  "pad_token": "[PAD]",
+  "pad_token_box": [
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}