End of training

README.md

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+---
+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: 0.6178
+- Answer: {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809}
+- Header: {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119}
+- Question: {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065}
+- Overall Precision: 0.6893
+- Overall Recall: 0.7692
+- Overall F1: 0.7271
+- Overall Accuracy: 0.8014
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.5284        | 1.0   | 38   | 1.0167          | {'precision': 0.3938144329896907, 'recall': 0.4721878862793572, 'f1': 0.4294547498594716, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5845528455284553, 'recall': 0.6751173708920187, 'f1': 0.6265795206971678, 'number': 1065} | 0.4959            | 0.5524         | 0.5227     | 0.6689           |
+| 0.8661        | 2.0   | 76   | 0.7179          | {'precision': 0.630346232179226, 'recall': 0.765142150803461, 'f1': 0.6912339475153545, 'number': 809}   | {'precision': 0.2087912087912088, 'recall': 0.15966386554621848, 'f1': 0.18095238095238092, 'number': 119} | {'precision': 0.7058823529411765, 'recall': 0.7436619718309859, 'f1': 0.7242798353909465, 'number': 1065} | 0.6515            | 0.7175         | 0.6829     | 0.7596           |
+| 0.6265        | 3.0   | 114  | 0.6470          | {'precision': 0.6458546571136131, 'recall': 0.7799752781211372, 'f1': 0.7066069428891377, 'number': 809} | {'precision': 0.2972972972972973, 'recall': 0.2773109243697479, 'f1': 0.28695652173913044, 'number': 119}  | {'precision': 0.7359649122807017, 'recall': 0.787793427230047, 'f1': 0.7609977324263038, 'number': 1065}  | 0.6746            | 0.7541         | 0.7122     | 0.7879           |
+| 0.5076        | 4.0   | 152  | 0.6207          | {'precision': 0.6680851063829787, 'recall': 0.7762669962917181, 'f1': 0.7181246426529445, 'number': 809} | {'precision': 0.28, 'recall': 0.29411764705882354, 'f1': 0.28688524590163933, 'number': 119}               | {'precision': 0.7368421052631579, 'recall': 0.828169014084507, 'f1': 0.7798408488063661, 'number': 1065}  | 0.6830            | 0.7752         | 0.7262     | 0.8003           |
+| 0.4471        | 5.0   | 190  | 0.6178          | {'precision': 0.6652719665271967, 'recall': 0.7861557478368356, 'f1': 0.7206798866855525, 'number': 809} | {'precision': 0.29133858267716534, 'recall': 0.31092436974789917, 'f1': 0.3008130081300813, 'number': 119} | {'precision': 0.7537248028045574, 'recall': 0.8075117370892019, 'f1': 0.7796917497733454, 'number': 1065} | 0.6893            | 0.7692         | 0.7271     | 0.8014           |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2

logs/events.out.tfevents.1678372194.d32b24cc545c.19924.0

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preprocessor_config.json

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+{
+  "apply_ocr": true,
+  "do_resize": true,
+  "feature_extractor_type": "LayoutLMv2FeatureExtractor",
+  "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": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "additional_special_tokens": null,
+  "apply_ocr": false,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+    0
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "microsoft/layoutlmv2-base-uncased",
+  "never_split": null,
+  "only_label_first_subword": true,
+  "pad_token": "[PAD]",
+  "pad_token_box": [
+    0,
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
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+    1000,
+    1000
+  ],
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}