End of training

README.md

@@ -0,0 +1,79 @@
+---
+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: 0.7053
+- Answer: {'precision': 0.7111597374179431, 'recall': 0.8034610630407911, 'f1': 0.7544979686593152, 'number': 809}
+- Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
+- Question: {'precision': 0.7862254025044723, 'recall': 0.8253521126760563, 'f1': 0.8053137883646359, 'number': 1065}
+- Overall Precision: 0.7313
+- Overall Recall: 0.7893
+- Overall F1: 0.7592
+- Overall Accuracy: 0.8115
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8129        | 1.0   | 10   | 1.6175          | {'precision': 0.024783147459727387, 'recall': 0.024721878862793572, 'f1': 0.02475247524752475, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24563318777292575, 'recall': 0.2112676056338028, 'f1': 0.2271580010095911, 'number': 1065} | 0.1422            | 0.1229         | 0.1319     | 0.3619           |
+| 1.4587        | 2.0   | 20   | 1.2242          | {'precision': 0.14423076923076922, 'recall': 0.12978986402966625, 'f1': 0.13662979830839295, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.45927075252133437, 'recall': 0.5558685446009389, 'f1': 0.5029736618521666, 'number': 1065} | 0.3456            | 0.3497         | 0.3476     | 0.5892           |
+| 1.0781        | 3.0   | 30   | 0.9399          | {'precision': 0.4616252821670429, 'recall': 0.5055624227441285, 'f1': 0.4825958702064897, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5979381443298969, 'recall': 0.6535211267605634, 'f1': 0.6244952893674293, 'number': 1065}  | 0.5300            | 0.5544         | 0.5419     | 0.6934           |
+| 0.8159        | 4.0   | 40   | 0.7947          | {'precision': 0.5964912280701754, 'recall': 0.7144622991347342, 'f1': 0.6501687289088864, 'number': 809}      | {'precision': 0.125, 'recall': 0.07563025210084033, 'f1': 0.09424083769633507, 'number': 119}               | {'precision': 0.6864175022789426, 'recall': 0.7070422535211267, 'f1': 0.696577243293247, 'number': 1065}   | 0.6268            | 0.6724         | 0.6488     | 0.7517           |
+| 0.6615        | 5.0   | 50   | 0.7330          | {'precision': 0.6485042735042735, 'recall': 0.7503090234857849, 'f1': 0.695702005730659, 'number': 809}       | {'precision': 0.28205128205128205, 'recall': 0.18487394957983194, 'f1': 0.2233502538071066, 'number': 119}  | {'precision': 0.7291857273559011, 'recall': 0.748356807511737, 'f1': 0.7386468952734011, 'number': 1065}   | 0.6768            | 0.7155         | 0.6956     | 0.7756           |
+| 0.5414        | 6.0   | 60   | 0.6814          | {'precision': 0.6427850655903128, 'recall': 0.7873918417799752, 'f1': 0.7077777777777777, 'number': 809}      | {'precision': 0.2857142857142857, 'recall': 0.16806722689075632, 'f1': 0.21164021164021166, 'number': 119}  | {'precision': 0.726039016115352, 'recall': 0.8037558685446009, 'f1': 0.7629233511586453, 'number': 1065}   | 0.6754            | 0.7592         | 0.7149     | 0.7878           |
+| 0.4787        | 7.0   | 70   | 0.6756          | {'precision': 0.6776947705442903, 'recall': 0.7849196538936959, 'f1': 0.7273768613974799, 'number': 809}      | {'precision': 0.3402061855670103, 'recall': 0.2773109243697479, 'f1': 0.3055555555555556, 'number': 119}    | {'precision': 0.7390557939914163, 'recall': 0.8084507042253521, 'f1': 0.7721973094170403, 'number': 1065}  | 0.6953            | 0.7672         | 0.7295     | 0.8014           |
+| 0.4379        | 8.0   | 80   | 0.6724          | {'precision': 0.6952695269526953, 'recall': 0.7812113720642769, 'f1': 0.7357392316647265, 'number': 809}      | {'precision': 0.3448275862068966, 'recall': 0.25210084033613445, 'f1': 0.2912621359223301, 'number': 119}   | {'precision': 0.7552264808362369, 'recall': 0.8140845070422535, 'f1': 0.7835517397198374, 'number': 1065}  | 0.7132            | 0.7672         | 0.7392     | 0.8063           |
+| 0.3864        | 9.0   | 90   | 0.6771          | {'precision': 0.6915584415584416, 'recall': 0.7898640296662547, 'f1': 0.7374495095210617, 'number': 809}      | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119}   | {'precision': 0.7570573139435415, 'recall': 0.8309859154929577, 'f1': 0.792300805729633, 'number': 1065}   | 0.7075            | 0.7827         | 0.7432     | 0.7962           |
+| 0.3486        | 10.0  | 100  | 0.6774          | {'precision': 0.6862955032119914, 'recall': 0.792336217552534, 'f1': 0.7355134825014343, 'number': 809}       | {'precision': 0.3557692307692308, 'recall': 0.31092436974789917, 'f1': 0.33183856502242154, 'number': 119}  | {'precision': 0.7725284339457568, 'recall': 0.8291079812206573, 'f1': 0.7998188405797102, 'number': 1065}  | 0.7157            | 0.7832         | 0.7480     | 0.8027           |
+| 0.3138        | 11.0  | 110  | 0.6960          | {'precision': 0.6893203883495146, 'recall': 0.7898640296662547, 'f1': 0.7361751152073734, 'number': 809}      | {'precision': 0.3619047619047619, 'recall': 0.31932773109243695, 'f1': 0.33928571428571425, 'number': 119}  | {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}  | 0.7240            | 0.7792         | 0.7506     | 0.8066           |
+| 0.303         | 12.0  | 120  | 0.6989          | {'precision': 0.6927194860813705, 'recall': 0.799752781211372, 'f1': 0.7423981640849111, 'number': 809}       | {'precision': 0.3783783783783784, 'recall': 0.35294117647058826, 'f1': 0.3652173913043478, 'number': 119}   | {'precision': 0.7902350813743219, 'recall': 0.8206572769953052, 'f1': 0.8051589129433441, 'number': 1065}  | 0.7266            | 0.7842         | 0.7543     | 0.8054           |
+| 0.2823        | 13.0  | 130  | 0.7023          | {'precision': 0.7027322404371584, 'recall': 0.7948084054388134, 'f1': 0.7459396751740139, 'number': 809}      | {'precision': 0.36134453781512604, 'recall': 0.36134453781512604, 'f1': 0.36134453781512604, 'number': 119} | {'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}  | 0.7251            | 0.7847         | 0.7537     | 0.8080           |
+| 0.2707        | 14.0  | 140  | 0.7048          | {'precision': 0.7040261153427638, 'recall': 0.799752781211372, 'f1': 0.7488425925925926, 'number': 809}       | {'precision': 0.35833333333333334, 'recall': 0.36134453781512604, 'f1': 0.35983263598326365, 'number': 119} | {'precision': 0.7774822695035462, 'recall': 0.8234741784037559, 'f1': 0.7998176014591885, 'number': 1065}  | 0.7231            | 0.7863         | 0.7534     | 0.8094           |
+| 0.2678        | 15.0  | 150  | 0.7053          | {'precision': 0.7111597374179431, 'recall': 0.8034610630407911, 'f1': 0.7544979686593152, 'number': 809}      | {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}    | {'precision': 0.7862254025044723, 'recall': 0.8253521126760563, 'f1': 0.8053137883646359, 'number': 1065}  | 0.7313            | 0.7893         | 0.7592     | 0.8115           |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3

logs/events.out.tfevents.1685570128.909f6e96fe53.3967.0

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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,
+  "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]"
+}