End of training

README.md

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+---
+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: 0.7043
+- Answer: {'precision': 0.7119021134593994, 'recall': 0.7911001236093943, 'f1': 0.7494145199063232, 'number': 809}
+- Header: {'precision': 0.37209302325581395, 'recall': 0.40336134453781514, 'f1': 0.3870967741935484, 'number': 119}
+- Question: {'precision': 0.7965796579657966, 'recall': 0.8309859154929577, 'f1': 0.8134191176470588, 'number': 1065}
+- Overall Precision: 0.7354
+- Overall Recall: 0.7893
+- Overall F1: 0.7614
+- Overall Accuracy: 0.8036
+
+## 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: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                        | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.5459        | 1.0   | 38   | 1.0013          | {'precision': 0.4444444444444444, 'recall': 0.5735475896168108, 'f1': 0.500809498111171, 'number': 809}  | {'precision': 0.05263157894736842, 'recall': 0.008403361344537815, 'f1': 0.014492753623188406, 'number': 119} | {'precision': 0.5643024162120032, 'recall': 0.67981220657277, 'f1': 0.616695059625213, 'number': 1065}    | 0.5068            | 0.5966         | 0.5481     | 0.6648           |
+| 0.8508        | 2.0   | 76   | 0.7119          | {'precision': 0.6081081081081081, 'recall': 0.7787391841779975, 'f1': 0.6829268292682927, 'number': 809} | {'precision': 0.16455696202531644, 'recall': 0.1092436974789916, 'f1': 0.1313131313131313, 'number': 119}     | {'precision': 0.6774703557312253, 'recall': 0.8046948356807512, 'f1': 0.7356223175965665, 'number': 1065} | 0.6303            | 0.7526         | 0.6860     | 0.7691           |
+| 0.6131        | 3.0   | 114  | 0.6432          | {'precision': 0.6631689401888772, 'recall': 0.7812113720642769, 'f1': 0.717366628830874, 'number': 809}  | {'precision': 0.248, 'recall': 0.2605042016806723, 'f1': 0.2540983606557377, 'number': 119}                   | {'precision': 0.7474048442906575, 'recall': 0.8112676056338028, 'f1': 0.7780279153534444, 'number': 1065} | 0.6835            | 0.7662         | 0.7225     | 0.7837           |
+| 0.4734        | 4.0   | 152  | 0.6196          | {'precision': 0.6882845188284519, 'recall': 0.8133498145859085, 'f1': 0.7456090651558074, 'number': 809} | {'precision': 0.2569444444444444, 'recall': 0.31092436974789917, 'f1': 0.28136882129277563, 'number': 119}    | {'precision': 0.763716814159292, 'recall': 0.8103286384976526, 'f1': 0.7863325740318907, 'number': 1065}  | 0.6987            | 0.7817         | 0.7379     | 0.8005           |
+| 0.3721        | 5.0   | 190  | 0.6197          | {'precision': 0.6894343649946638, 'recall': 0.7985166872682324, 'f1': 0.7399770904925544, 'number': 809} | {'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}    | {'precision': 0.7683566433566433, 'recall': 0.8253521126760563, 'f1': 0.7958352195563604, 'number': 1065} | 0.7081            | 0.7852         | 0.7447     | 0.8005           |
+| 0.2989        | 6.0   | 228  | 0.6483          | {'precision': 0.6992316136114161, 'recall': 0.7873918417799752, 'f1': 0.7406976744186047, 'number': 809} | {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}              | {'precision': 0.7854578096947935, 'recall': 0.8215962441314554, 'f1': 0.8031206975676914, 'number': 1065} | 0.7220            | 0.7832         | 0.7514     | 0.7987           |
+| 0.2437        | 7.0   | 266  | 0.6707          | {'precision': 0.7067415730337079, 'recall': 0.7775030902348579, 'f1': 0.7404355503237198, 'number': 809} | {'precision': 0.34057971014492755, 'recall': 0.3949579831932773, 'f1': 0.36575875486381326, 'number': 119}    | {'precision': 0.7804232804232805, 'recall': 0.8309859154929577, 'f1': 0.8049113233287858, 'number': 1065} | 0.7220            | 0.7832         | 0.7514     | 0.7993           |
+| 0.2008        | 8.0   | 304  | 0.6904          | {'precision': 0.7038251366120218, 'recall': 0.796044499381953, 'f1': 0.7470997679814385, 'number': 809}  | {'precision': 0.3356643356643357, 'recall': 0.40336134453781514, 'f1': 0.366412213740458, 'number': 119}      | {'precision': 0.7885304659498208, 'recall': 0.8262910798122066, 'f1': 0.8069692801467218, 'number': 1065} | 0.7231            | 0.7888         | 0.7545     | 0.7990           |
+| 0.1802        | 9.0   | 342  | 0.7072          | {'precision': 0.7161862527716186, 'recall': 0.7985166872682324, 'f1': 0.7551139684395091, 'number': 809} | {'precision': 0.34459459459459457, 'recall': 0.42857142857142855, 'f1': 0.38202247191011235, 'number': 119}   | {'precision': 0.7896174863387978, 'recall': 0.8140845070422535, 'f1': 0.8016643550624134, 'number': 1065} | 0.7281            | 0.7847         | 0.7554     | 0.7989           |
+| 0.1681        | 10.0  | 380  | 0.7043          | {'precision': 0.7119021134593994, 'recall': 0.7911001236093943, 'f1': 0.7494145199063232, 'number': 809} | {'precision': 0.37209302325581395, 'recall': 0.40336134453781514, 'f1': 0.3870967741935484, 'number': 119}    | {'precision': 0.7965796579657966, 'recall': 0.8309859154929577, 'f1': 0.8134191176470588, 'number': 1065} | 0.7354            | 0.7893         | 0.7614     | 0.8036           |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

preprocessor_config.json

@@ -0,0 +1,25 @@
+{
+  "_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
+  },
+  "tesseract_config": ""
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "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

@@ -0,0 +1,80 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "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
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "apply_ocr": false,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "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": [
+    0,
+    0,
+    0,
+    0
+  ],
+  "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]"
+}