End of training

README.md

@@ -0,0 +1,79 @@
+---
+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: 0.6771
+- Answer: {'precision': 0.7053571428571429, 'recall': 0.7812113720642769, 'f1': 0.7413489736070381, 'number': 809}
+- Header: {'precision': 0.29770992366412213, 'recall': 0.3277310924369748, 'f1': 0.312, 'number': 119}
+- Question: {'precision': 0.763716814159292, 'recall': 0.8103286384976526, 'f1': 0.7863325740318907, 'number': 1065}
+- Overall Precision: 0.7112
+- Overall Recall: 0.7697
+- Overall F1: 0.7393
+- 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: 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7668        | 1.0   | 10   | 1.5655          | {'precision': 0.01564945226917058, 'recall': 0.012360939431396786, 'f1': 0.013812154696132598, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.31925675675675674, 'recall': 0.17746478873239438, 'f1': 0.22812311406155703, 'number': 1065} | 0.1617            | 0.0998         | 0.1234     | 0.3599           |
+| 1.4335        | 2.0   | 20   | 1.2504          | {'precision': 0.22972972972972974, 'recall': 0.2521631644004944, 'f1': 0.24042427813789036, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.391304347826087, 'recall': 0.5492957746478874, 'f1': 0.45703125, 'number': 1065}             | 0.3311            | 0.3959         | 0.3606     | 0.5926           |
+| 1.1215        | 3.0   | 30   | 0.9771          | {'precision': 0.4570273003033367, 'recall': 0.5587144622991347, 'f1': 0.5027808676307008, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5460829493087558, 'recall': 0.6676056338028169, 'f1': 0.6007604562737643, 'number': 1065}    | 0.5022            | 0.5835         | 0.5398     | 0.6802           |
+| 0.8632        | 4.0   | 40   | 0.7960          | {'precision': 0.5714285714285714, 'recall': 0.7317676143386898, 'f1': 0.6417344173441735, 'number': 809}      | {'precision': 0.0379746835443038, 'recall': 0.025210084033613446, 'f1': 0.030303030303030304, 'number': 119} | {'precision': 0.633201581027668, 'recall': 0.752112676056338, 'f1': 0.6875536480686696, 'number': 1065}      | 0.5866            | 0.7005         | 0.6385     | 0.7572           |
+| 0.6913        | 5.0   | 50   | 0.7202          | {'precision': 0.6335797254487856, 'recall': 0.7416563658838071, 'f1': 0.683371298405467, 'number': 809}       | {'precision': 0.10752688172043011, 'recall': 0.08403361344537816, 'f1': 0.09433962264150944, 'number': 119}  | {'precision': 0.6907216494845361, 'recall': 0.7549295774647887, 'f1': 0.721399730820996, 'number': 1065}     | 0.6416            | 0.7095         | 0.6738     | 0.7808           |
+| 0.5758        | 6.0   | 60   | 0.6812          | {'precision': 0.6677248677248677, 'recall': 0.7799752781211372, 'f1': 0.7194982896237172, 'number': 809}      | {'precision': 0.17857142857142858, 'recall': 0.16806722689075632, 'f1': 0.17316017316017318, 'number': 119}  | {'precision': 0.6919315403422983, 'recall': 0.7971830985915493, 'f1': 0.7408376963350787, 'number': 1065}    | 0.6567            | 0.7526         | 0.7014     | 0.7947           |
+| 0.4993        | 7.0   | 70   | 0.6593          | {'precision': 0.6597938144329897, 'recall': 0.7911001236093943, 'f1': 0.7195053400786959, 'number': 809}      | {'precision': 0.21875, 'recall': 0.17647058823529413, 'f1': 0.19534883720930232, 'number': 119}              | {'precision': 0.7158703071672355, 'recall': 0.787793427230047, 'f1': 0.7501117568171659, 'number': 1065}     | 0.6702            | 0.7526         | 0.7091     | 0.7951           |
+| 0.4397        | 8.0   | 80   | 0.6633          | {'precision': 0.6749192680301399, 'recall': 0.7750309023485785, 'f1': 0.721518987341772, 'number': 809}       | {'precision': 0.23943661971830985, 'recall': 0.2857142857142857, 'f1': 0.26053639846743293, 'number': 119}   | {'precision': 0.7274290627687017, 'recall': 0.7943661971830986, 'f1': 0.7594254937163376, 'number': 1065}    | 0.6746            | 0.7561         | 0.7130     | 0.7987           |
+| 0.396         | 9.0   | 90   | 0.6605          | {'precision': 0.6875699888017918, 'recall': 0.7589616810877626, 'f1': 0.7215041128084607, 'number': 809}      | {'precision': 0.25203252032520324, 'recall': 0.2605042016806723, 'f1': 0.25619834710743805, 'number': 119}   | {'precision': 0.7344013490725126, 'recall': 0.8178403755868544, 'f1': 0.7738782763216348, 'number': 1065}    | 0.6885            | 0.7607         | 0.7228     | 0.8026           |
+| 0.3578        | 10.0  | 100  | 0.6624          | {'precision': 0.6842105263157895, 'recall': 0.7873918417799752, 'f1': 0.7321839080459769, 'number': 809}      | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119}     | {'precision': 0.7476231633535004, 'recall': 0.812206572769953, 'f1': 0.7785778577857785, 'number': 1065}     | 0.6955            | 0.7702         | 0.7310     | 0.8022           |
+| 0.3249        | 11.0  | 110  | 0.6678          | {'precision': 0.6865671641791045, 'recall': 0.796044499381953, 'f1': 0.7372638809387521, 'number': 809}       | {'precision': 0.273972602739726, 'recall': 0.33613445378151263, 'f1': 0.3018867924528302, 'number': 119}     | {'precision': 0.7584973166368515, 'recall': 0.7962441314553991, 'f1': 0.7769125057260651, 'number': 1065}    | 0.6957            | 0.7687         | 0.7304     | 0.8010           |
+| 0.3021        | 12.0  | 120  | 0.6681          | {'precision': 0.7120535714285714, 'recall': 0.788627935723115, 'f1': 0.7483870967741936, 'number': 809}       | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119}    | {'precision': 0.7547826086956522, 'recall': 0.8150234741784037, 'f1': 0.7837471783295711, 'number': 1065}    | 0.7096            | 0.7747         | 0.7407     | 0.8045           |
+| 0.2895        | 13.0  | 130  | 0.6755          | {'precision': 0.7122060470324748, 'recall': 0.7861557478368356, 'f1': 0.7473560517038778, 'number': 809}      | {'precision': 0.2887323943661972, 'recall': 0.3445378151260504, 'f1': 0.31417624521072796, 'number': 119}    | {'precision': 0.7595048629531388, 'recall': 0.8065727699530516, 'f1': 0.7823315118397085, 'number': 1065}    | 0.7091            | 0.7707         | 0.7386     | 0.8026           |
+| 0.2734        | 14.0  | 140  | 0.6768          | {'precision': 0.7093541202672605, 'recall': 0.7873918417799752, 'f1': 0.7463386057410661, 'number': 809}      | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}   | {'precision': 0.7570175438596491, 'recall': 0.8103286384976526, 'f1': 0.7827664399092971, 'number': 1065}    | 0.7067            | 0.7727         | 0.7383     | 0.8026           |
+| 0.2804        | 15.0  | 150  | 0.6771          | {'precision': 0.7053571428571429, 'recall': 0.7812113720642769, 'f1': 0.7413489736070381, 'number': 809}      | {'precision': 0.29770992366412213, 'recall': 0.3277310924369748, 'f1': 0.312, 'number': 119}                 | {'precision': 0.763716814159292, 'recall': 0.8103286384976526, 'f1': 0.7863325740318907, 'number': 1065}     | 0.7112            | 0.7697         | 0.7393     | 0.8036           |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.0+cpu
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

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

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": ""
+}

pytorch_model.bin

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 size 450601534

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": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+    "102": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
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+    "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]"
+}