End of training

README.md

@@ -0,0 +1,81 @@
+---
+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: 0.6953
+- Answer: {'precision': 0.7040261153427638, 'recall': 0.799752781211372, 'f1': 0.7488425925925926, 'number': 809}
+- Header: {'precision': 0.24516129032258063, 'recall': 0.31932773109243695, 'f1': 0.2773722627737226, 'number': 119}
+- Question: {'precision': 0.7894273127753304, 'recall': 0.8413145539906103, 'f1': 0.8145454545454545, 'number': 1065}
+- Overall Precision: 0.7157
+- Overall Recall: 0.7933
+- Overall F1: 0.7525
+- Overall Accuracy: 0.8063
+
+## 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: 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 | Answer                                                                                                        | Header                                                                                                        | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8542        | 1.0   | 10   | 1.6365          | {'precision': 0.01658374792703151, 'recall': 0.012360939431396786, 'f1': 0.014164305949008497, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.21144674085850557, 'recall': 0.12488262910798122, 'f1': 0.15702479338842976, 'number': 1065} | 0.1161            | 0.0718         | 0.0887     | 0.3298           |
+| 1.4883        | 2.0   | 20   | 1.3085          | {'precision': 0.17417417417417416, 'recall': 0.21508034610630408, 'f1': 0.19247787610619468, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.37554585152838427, 'recall': 0.48450704225352115, 'f1': 0.4231242312423124, 'number': 1065}  | 0.2908            | 0.3462         | 0.3161     | 0.5523           |
+| 1.1521        | 3.0   | 30   | 0.9728          | {'precision': 0.452642073778664, 'recall': 0.5611866501854141, 'f1': 0.5011037527593818, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.5948678071539658, 'recall': 0.7183098591549296, 'f1': 0.6507868991918333, 'number': 1065}    | 0.53              | 0.6116         | 0.5679     | 0.6953           |
+| 0.8808        | 4.0   | 40   | 0.7922          | {'precision': 0.582089552238806, 'recall': 0.723114956736712, 'f1': 0.6449834619625138, 'number': 809}        | {'precision': 0.06666666666666667, 'recall': 0.025210084033613446, 'f1': 0.036585365853658534, 'number': 119} | {'precision': 0.6649916247906198, 'recall': 0.7455399061032864, 'f1': 0.7029659141212926, 'number': 1065}    | 0.6159            | 0.6934         | 0.6523     | 0.7508           |
+| 0.6941        | 5.0   | 50   | 0.7105          | {'precision': 0.6461704422869471, 'recall': 0.7404202719406675, 'f1': 0.6900921658986174, 'number': 809}      | {'precision': 0.1875, 'recall': 0.15126050420168066, 'f1': 0.16744186046511625, 'number': 119}                | {'precision': 0.651778955336866, 'recall': 0.8084507042253521, 'f1': 0.721709974853311, 'number': 1065}      | 0.6305            | 0.7416         | 0.6816     | 0.7809           |
+| 0.5844        | 6.0   | 60   | 0.6780          | {'precision': 0.6598360655737705, 'recall': 0.796044499381953, 'f1': 0.7215686274509804, 'number': 809}       | {'precision': 0.2079207920792079, 'recall': 0.17647058823529413, 'f1': 0.19090909090909092, 'number': 119}    | {'precision': 0.7283633247643531, 'recall': 0.7981220657276995, 'f1': 0.7616487455197132, 'number': 1065}    | 0.6751            | 0.7602         | 0.7151     | 0.7926           |
+| 0.5021        | 7.0   | 70   | 0.6522          | {'precision': 0.6852248394004282, 'recall': 0.7911001236093943, 'f1': 0.7343660355708548, 'number': 809}      | {'precision': 0.20149253731343283, 'recall': 0.226890756302521, 'f1': 0.21343873517786563, 'number': 119}     | {'precision': 0.7521514629948365, 'recall': 0.8206572769953052, 'f1': 0.7849124382577458, 'number': 1065}    | 0.6910            | 0.7732         | 0.7298     | 0.8036           |
+| 0.443         | 8.0   | 80   | 0.6501          | {'precision': 0.6827731092436975, 'recall': 0.8034610630407911, 'f1': 0.7382169222032936, 'number': 809}      | {'precision': 0.2542372881355932, 'recall': 0.25210084033613445, 'f1': 0.25316455696202533, 'number': 119}    | {'precision': 0.7689003436426117, 'recall': 0.8403755868544601, 'f1': 0.8030506953790938, 'number': 1065}    | 0.7050            | 0.7903         | 0.7452     | 0.8060           |
+| 0.3917        | 9.0   | 90   | 0.6715          | {'precision': 0.6913319238900634, 'recall': 0.8084054388133498, 'f1': 0.7452991452991453, 'number': 809}      | {'precision': 0.25547445255474455, 'recall': 0.29411764705882354, 'f1': 0.2734375, 'number': 119}             | {'precision': 0.7811387900355872, 'recall': 0.8244131455399061, 'f1': 0.8021927820922796, 'number': 1065}    | 0.7100            | 0.7863         | 0.7462     | 0.8032           |
+| 0.3849        | 10.0  | 100  | 0.6725          | {'precision': 0.6908315565031983, 'recall': 0.8009888751545118, 'f1': 0.7418431597023468, 'number': 809}      | {'precision': 0.24444444444444444, 'recall': 0.2773109243697479, 'f1': 0.25984251968503935, 'number': 119}    | {'precision': 0.7829937998228521, 'recall': 0.8300469483568075, 'f1': 0.805834092980857, 'number': 1065}     | 0.7107            | 0.7852         | 0.7461     | 0.8064           |
+| 0.3232        | 11.0  | 110  | 0.6747          | {'precision': 0.6918976545842217, 'recall': 0.8022249690976514, 'f1': 0.7429879793932456, 'number': 809}      | {'precision': 0.25161290322580643, 'recall': 0.3277310924369748, 'f1': 0.2846715328467153, 'number': 119}     | {'precision': 0.7609797297297297, 'recall': 0.8460093896713615, 'f1': 0.8012449977767896, 'number': 1065}    | 0.6978            | 0.7973         | 0.7443     | 0.8001           |
+| 0.3028        | 12.0  | 120  | 0.6871          | {'precision': 0.700218818380744, 'recall': 0.7911001236093943, 'f1': 0.7428903076030179, 'number': 809}       | {'precision': 0.25, 'recall': 0.31092436974789917, 'f1': 0.27715355805243447, 'number': 119}                  | {'precision': 0.7985611510791367, 'recall': 0.8338028169014085, 'f1': 0.8158015617822691, 'number': 1065}    | 0.7199            | 0.7852         | 0.7511     | 0.8042           |
+| 0.284         | 13.0  | 130  | 0.6905          | {'precision': 0.697524219590958, 'recall': 0.8009888751545118, 'f1': 0.7456846950517838, 'number': 809}       | {'precision': 0.2602739726027397, 'recall': 0.31932773109243695, 'f1': 0.28679245283018867, 'number': 119}    | {'precision': 0.7929203539823009, 'recall': 0.8413145539906103, 'f1': 0.8164009111617311, 'number': 1065}    | 0.7175            | 0.7938         | 0.7537     | 0.8057           |
+| 0.2666        | 14.0  | 140  | 0.6958          | {'precision': 0.6949516648764769, 'recall': 0.799752781211372, 'f1': 0.7436781609195402, 'number': 809}       | {'precision': 0.2585034013605442, 'recall': 0.31932773109243695, 'f1': 0.2857142857142857, 'number': 119}     | {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}    | 0.7146            | 0.7903         | 0.7505     | 0.8040           |
+| 0.2705        | 15.0  | 150  | 0.6953          | {'precision': 0.7040261153427638, 'recall': 0.799752781211372, 'f1': 0.7488425925925926, 'number': 809}       | {'precision': 0.24516129032258063, 'recall': 0.31932773109243695, 'f1': 0.2773722627737226, 'number': 119}    | {'precision': 0.7894273127753304, 'recall': 0.8413145539906103, 'f1': 0.8145454545454545, 'number': 1065}    | 0.7157            | 0.7933         | 0.7525     | 0.8063           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

logs/events.out.tfevents.1719273795.ae49b29c4439.7028.1

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preprocessor_config.json

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+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "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,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "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": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "additional_special_tokens": [],
+  "apply_ocr": false,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "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",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
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+    1000,
+    1000
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}