End of training

README.md

@@ -0,0 +1,84 @@
+---
+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: 0.8174
+- Answer: {'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809}
+- Header: {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}
+- Question: {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065}
+- Overall Precision: 0.7351
+- Overall Recall: 0.7978
+- Overall F1: 0.7652
+- Overall Accuracy: 0.8019
+
+## 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: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                     | Header                                                                                                       | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.3435        | 1.0   | 10   | 1.1455          | {'precision': 0.29554655870445345, 'recall': 0.27070457354758964, 'f1': 0.2825806451612903, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.43828125, 'recall': 0.5267605633802817, 'f1': 0.47846481876332625, 'number': 1065}        | 0.3858            | 0.3914         | 0.3885     | 0.6180           |
+| 0.9706        | 2.0   | 20   | 0.8933          | {'precision': 0.5545454545454546, 'recall': 0.6786155747836835, 'f1': 0.6103390772651472, 'number': 809}   | {'precision': 0.08695652173913043, 'recall': 0.03361344537815126, 'f1': 0.048484848484848485, 'number': 119} | {'precision': 0.6115916955017301, 'recall': 0.6638497652582159, 'f1': 0.6366501575866726, 'number': 1065} | 0.5748            | 0.6322         | 0.6022     | 0.7308           |
+| 0.7426        | 3.0   | 30   | 0.7478          | {'precision': 0.6294058408862034, 'recall': 0.7725587144622992, 'f1': 0.6936736958934517, 'number': 809}   | {'precision': 0.1891891891891892, 'recall': 0.11764705882352941, 'f1': 0.1450777202072539, 'number': 119}    | {'precision': 0.6858333333333333, 'recall': 0.7727699530516432, 'f1': 0.726710816777042, 'number': 1065}  | 0.6449            | 0.7336         | 0.6864     | 0.7770           |
+| 0.6123        | 4.0   | 40   | 0.6950          | {'precision': 0.6286266924564797, 'recall': 0.8034610630407911, 'f1': 0.705371676614216, 'number': 809}    | {'precision': 0.19387755102040816, 'recall': 0.15966386554621848, 'f1': 0.17511520737327188, 'number': 119}  | {'precision': 0.6943268416596104, 'recall': 0.7699530516431925, 'f1': 0.730186999109528, 'number': 1065}  | 0.6438            | 0.7471         | 0.6916     | 0.7886           |
+| 0.5267        | 5.0   | 50   | 0.6804          | {'precision': 0.6574172892209178, 'recall': 0.761433868974042, 'f1': 0.7056128293241695, 'number': 809}    | {'precision': 0.21818181818181817, 'recall': 0.20168067226890757, 'f1': 0.2096069868995633, 'number': 119}   | {'precision': 0.7246496290189612, 'recall': 0.8253521126760563, 'f1': 0.771729587357331, 'number': 1065}  | 0.6721            | 0.7622         | 0.7143     | 0.8013           |
+| 0.4587        | 6.0   | 60   | 0.6701          | {'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809}    | {'precision': 0.2108843537414966, 'recall': 0.2605042016806723, 'f1': 0.2330827067669173, 'number': 119}     | {'precision': 0.7309602649006622, 'recall': 0.8291079812206573, 'f1': 0.7769467663880335, 'number': 1065} | 0.6729            | 0.7812         | 0.7230     | 0.7977           |
+| 0.3981        | 7.0   | 70   | 0.6637          | {'precision': 0.7029063509149623, 'recall': 0.8071693448702101, 'f1': 0.7514384349827388, 'number': 809}   | {'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119}    | {'precision': 0.7621483375959079, 'recall': 0.8394366197183099, 'f1': 0.7989276139410187, 'number': 1065} | 0.7096            | 0.7933         | 0.7491     | 0.8062           |
+| 0.3608        | 8.0   | 80   | 0.6778          | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}   | {'precision': 0.25874125874125875, 'recall': 0.31092436974789917, 'f1': 0.2824427480916031, 'number': 119}   | {'precision': 0.7633851468048359, 'recall': 0.8300469483568075, 'f1': 0.7953216374269007, 'number': 1065} | 0.7081            | 0.7863         | 0.7451     | 0.8003           |
+| 0.311         | 9.0   | 90   | 0.6931          | {'precision': 0.6991247264770241, 'recall': 0.7898640296662547, 'f1': 0.7417295414973882, 'number': 809}   | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}   | {'precision': 0.7606244579358196, 'recall': 0.8234741784037559, 'f1': 0.7908025247971145, 'number': 1065} | 0.7060            | 0.7797         | 0.7411     | 0.8055           |
+| 0.276         | 10.0  | 100  | 0.7144          | {'precision': 0.7298787210584344, 'recall': 0.8182941903584673, 'f1': 0.7715617715617716, 'number': 809}   | {'precision': 0.3103448275862069, 'recall': 0.37815126050420167, 'f1': 0.34090909090909094, 'number': 119}   | {'precision': 0.7814159292035399, 'recall': 0.8291079812206573, 'f1': 0.8045558086560365, 'number': 1065} | 0.7287            | 0.7978         | 0.7617     | 0.8062           |
+| 0.2393        | 11.0  | 110  | 0.7342          | {'precision': 0.7155555555555555, 'recall': 0.796044499381953, 'f1': 0.7536571094207138, 'number': 809}    | {'precision': 0.296551724137931, 'recall': 0.36134453781512604, 'f1': 0.32575757575757575, 'number': 119}    | {'precision': 0.774869109947644, 'recall': 0.8338028169014085, 'f1': 0.8032564450474899, 'number': 1065}  | 0.7188            | 0.7903         | 0.7529     | 0.8042           |
+| 0.2227        | 12.0  | 120  | 0.7539          | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809}   | {'precision': 0.33884297520661155, 'recall': 0.3445378151260504, 'f1': 0.3416666666666667, 'number': 119}    | {'precision': 0.7686440677966102, 'recall': 0.8516431924882629, 'f1': 0.8080178173719377, 'number': 1065} | 0.7191            | 0.7978         | 0.7564     | 0.8006           |
+| 0.2119        | 13.0  | 130  | 0.7774          | {'precision': 0.7263736263736263, 'recall': 0.8170580964153276, 'f1': 0.7690517742873763, 'number': 809}   | {'precision': 0.28125, 'recall': 0.37815126050420167, 'f1': 0.3225806451612903, 'number': 119}               | {'precision': 0.7714033539276258, 'recall': 0.8206572769953052, 'f1': 0.7952684258416743, 'number': 1065} | 0.7172            | 0.7928         | 0.7531     | 0.7952           |
+| 0.1882        | 14.0  | 140  | 0.7688          | {'precision': 0.7270668176670442, 'recall': 0.7935723114956736, 'f1': 0.7588652482269503, 'number': 809}   | {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119}    | {'precision': 0.7883597883597884, 'recall': 0.8394366197183099, 'f1': 0.8130968622100955, 'number': 1065} | 0.7359            | 0.7928         | 0.7633     | 0.8024           |
+| 0.1767        | 15.0  | 150  | 0.7717          | {'precision': 0.7244785949506037, 'recall': 0.8158220024721878, 'f1': 0.7674418604651163, 'number': 809}   | {'precision': 0.3548387096774194, 'recall': 0.3697478991596639, 'f1': 0.36213991769547327, 'number': 119}    | {'precision': 0.789612676056338, 'recall': 0.8422535211267606, 'f1': 0.8150840527033166, 'number': 1065}  | 0.7374            | 0.8033         | 0.7690     | 0.8020           |
+| 0.1703        | 16.0  | 160  | 0.7943          | {'precision': 0.7231638418079096, 'recall': 0.7911001236093943, 'f1': 0.755608028335301, 'number': 809}    | {'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119}  | {'precision': 0.79185119574845, 'recall': 0.8394366197183099, 'f1': 0.8149498632634458, 'number': 1065}   | 0.7361            | 0.7948         | 0.7643     | 0.8017           |
+| 0.1643        | 17.0  | 170  | 0.8087          | {'precision': 0.7207207207207207, 'recall': 0.7911001236093943, 'f1': 0.7542722451384797, 'number': 809}   | {'precision': 0.33098591549295775, 'recall': 0.3949579831932773, 'f1': 0.3601532567049809, 'number': 119}    | {'precision': 0.7932263814616756, 'recall': 0.8356807511737089, 'f1': 0.8139003200731596, 'number': 1065} | 0.7328            | 0.7913         | 0.7609     | 0.7990           |
+| 0.1443        | 18.0  | 180  | 0.8170          | {'precision': 0.7230419977298524, 'recall': 0.7873918417799752, 'f1': 0.7538461538461538, 'number': 809}   | {'precision': 0.36231884057971014, 'recall': 0.42016806722689076, 'f1': 0.38910505836575876, 'number': 119}  | {'precision': 0.7898936170212766, 'recall': 0.8366197183098592, 'f1': 0.8125854993160054, 'number': 1065} | 0.7350            | 0.7918         | 0.7623     | 0.7994           |
+| 0.148         | 19.0  | 190  | 0.8169          | {'precision': 0.7245240761478163, 'recall': 0.799752781211372, 'f1': 0.7602820211515863, 'number': 809}    | {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}             | {'precision': 0.792149866190901, 'recall': 0.8338028169014085, 'f1': 0.8124428179322964, 'number': 1065}  | 0.7364            | 0.7948         | 0.7645     | 0.8015           |
+| 0.1441        | 20.0  | 200  | 0.8174          | {'precision': 0.7233333333333334, 'recall': 0.8046971569839307, 'f1': 0.7618490345231129, 'number': 809}   | {'precision': 0.35766423357664234, 'recall': 0.4117647058823529, 'f1': 0.3828125, 'number': 119}             | {'precision': 0.7904085257548845, 'recall': 0.8356807511737089, 'f1': 0.8124144226380648, 'number': 1065} | 0.7351            | 0.7978         | 0.7652     | 0.8019           |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "[CLS]": 101,
+  "[MASK]": 103,
+  "[PAD]": 0,
+  "[SEP]": 102,
+  "[UNK]": 100
+}

preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "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": ""
+}

pytorch_model.bin

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 size 450603969

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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+{
+  "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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+    "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
+    },
+    "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]"
+}