End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-sroie_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-sroie_only
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0629
+- Ate: {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50}
+- Ddress: {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50}
+- Ompany: {'precision': 0.7777777777777778, 'recall': 0.84, 'f1': 0.8076923076923077, 'number': 50}
+- Otal: {'precision': 0.4166666666666667, 'recall': 0.3, 'f1': 0.3488372093023256, 'number': 50}
+- Overall Precision: 0.7692
+- Overall Recall: 0.75
+- Overall F1: 0.7595
+- Overall Accuracy: 0.9820
+
+## 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: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Ate                                                                                       | Ddress                                                                                   | Ompany                                                                                    | Otal                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.5019        | 1.0   | 36   | 0.1120          | {'precision': 0.671875, 'recall': 0.86, 'f1': 0.7543859649122807, 'number': 50}           | {'precision': 0.7454545454545455, 'recall': 0.82, 'f1': 0.780952380952381, 'number': 50} | {'precision': 0.5344827586206896, 'recall': 0.62, 'f1': 0.574074074074074, 'number': 50}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 50}                                | 0.6497            | 0.575          | 0.6101     | 0.9739           |
+| 0.0635        | 2.0   | 72   | 0.0728          | {'precision': 0.8448275862068966, 'recall': 0.98, 'f1': 0.9074074074074074, 'number': 50} | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50}              | {'precision': 0.7924528301886793, 'recall': 0.84, 'f1': 0.8155339805825242, 'number': 50} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 50}                                | 0.8272            | 0.67           | 0.7403     | 0.9818           |
+| 0.0429        | 3.0   | 108  | 0.0650          | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50}  | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50}              | {'precision': 0.7924528301886793, 'recall': 0.84, 'f1': 0.8155339805825242, 'number': 50} | {'precision': 0.4117647058823529, 'recall': 0.28, 'f1': 0.3333333333333333, 'number': 50} | 0.7760            | 0.745          | 0.7602     | 0.9818           |
+| 0.0341        | 4.0   | 144  | 0.0629          | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50}  | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50}              | {'precision': 0.7777777777777778, 'recall': 0.84, 'f1': 0.8076923076923077, 'number': 50} | {'precision': 0.4166666666666667, 'recall': 0.3, 'f1': 0.3488372093023256, 'number': 50}  | 0.7692            | 0.75           | 0.7595     | 0.9820           |
+
+
+### Framework versions
+
+- Transformers 4.50.0
+- Pytorch 2.1.0+cu118
+- Datasets 3.4.1
+- Tokenizers 0.21.1

logs/events.out.tfevents.1742906856.14d8c44976ca.6703.0

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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
+  },
+  "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
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+  "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
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "additional_special_tokens": [],
+  "apply_ocr": false,
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+    0,
+    0,
+    0
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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": [
+    0,
+    0,
+    0,
+    0
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
+    1000,
+    1000,
+    1000,
+    1000
+  ],
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}