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: 0.6741
+- Answer: {'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809}
+- Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
+- Question: {'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065}
+- Overall Precision: 0.7178
+- Overall Recall: 0.7913
+- Overall F1: 0.7527
+- Overall Accuracy: 0.8085
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8231        | 1.0   | 10   | 1.5809          | {'precision': 0.02072538860103627, 'recall': 0.024721878862793572, 'f1': 0.022547914317925594, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.20584795321637428, 'recall': 0.1652582159624413, 'f1': 0.18333333333333335, 'number': 1065} | 0.1077            | 0.0983         | 0.1028     | 0.4047           |
+| 1.4405        | 2.0   | 20   | 1.2298          | {'precision': 0.12202380952380952, 'recall': 0.10135970333745364, 'f1': 0.11073598919648886, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4417808219178082, 'recall': 0.6056338028169014, 'f1': 0.5108910891089109, 'number': 1065}   | 0.3407            | 0.3648         | 0.3523     | 0.5730           |
+| 1.111         | 3.0   | 30   | 0.9547          | {'precision': 0.47729672650475186, 'recall': 0.5587144622991347, 'f1': 0.5148063781321185, 'number': 809}     | {'precision': 0.06451612903225806, 'recall': 0.01680672268907563, 'f1': 0.026666666666666665, 'number': 119} | {'precision': 0.6218274111675127, 'recall': 0.6901408450704225, 'f1': 0.6542056074766356, 'number': 1065}   | 0.5505            | 0.5966         | 0.5726     | 0.7074           |
+| 0.8317        | 4.0   | 40   | 0.7729          | {'precision': 0.5933649289099526, 'recall': 0.7737948084054388, 'f1': 0.6716738197424893, 'number': 809}      | {'precision': 0.24528301886792453, 'recall': 0.1092436974789916, 'f1': 0.1511627906976744, 'number': 119}    | {'precision': 0.6753574432296047, 'recall': 0.7539906103286385, 'f1': 0.7125110913930789, 'number': 1065}   | 0.6278            | 0.7235         | 0.6723     | 0.7637           |
+| 0.656         | 5.0   | 50   | 0.7105          | {'precision': 0.640973630831643, 'recall': 0.7812113720642769, 'f1': 0.7041782729805014, 'number': 809}       | {'precision': 0.2898550724637681, 'recall': 0.16806722689075632, 'f1': 0.2127659574468085, 'number': 119}    | {'precision': 0.7317518248175182, 'recall': 0.7530516431924883, 'f1': 0.7422489588153632, 'number': 1065}   | 0.6760            | 0.7296         | 0.7017     | 0.7811           |
+| 0.5543        | 6.0   | 60   | 0.6688          | {'precision': 0.6625514403292181, 'recall': 0.796044499381953, 'f1': 0.7231892195395846, 'number': 809}       | {'precision': 0.26732673267326734, 'recall': 0.226890756302521, 'f1': 0.24545454545454548, 'number': 119}    | {'precision': 0.7412587412587412, 'recall': 0.7962441314553991, 'f1': 0.7677682209144409, 'number': 1065}   | 0.6852            | 0.7622         | 0.7216     | 0.8004           |
+| 0.4829        | 7.0   | 70   | 0.6491          | {'precision': 0.6635610766045549, 'recall': 0.792336217552534, 'f1': 0.7222535211267606, 'number': 809}       | {'precision': 0.25471698113207547, 'recall': 0.226890756302521, 'f1': 0.24, 'number': 119}                   | {'precision': 0.7401372212692967, 'recall': 0.8103286384976526, 'f1': 0.7736441057821605, 'number': 1065}   | 0.6841            | 0.7682         | 0.7237     | 0.8056           |
+| 0.4371        | 8.0   | 80   | 0.6419          | {'precision': 0.6742502585315409, 'recall': 0.8059332509270705, 'f1': 0.7342342342342343, 'number': 809}      | {'precision': 0.25210084033613445, 'recall': 0.25210084033613445, 'f1': 0.25210084033613445, 'number': 119}  | {'precision': 0.7491228070175439, 'recall': 0.8018779342723005, 'f1': 0.7746031746031745, 'number': 1065}   | 0.6900            | 0.7707         | 0.7281     | 0.8062           |
+| 0.3855        | 9.0   | 90   | 0.6560          | {'precision': 0.6869747899159664, 'recall': 0.8084054388133498, 'f1': 0.7427597955706984, 'number': 809}      | {'precision': 0.2711864406779661, 'recall': 0.2689075630252101, 'f1': 0.270042194092827, 'number': 119}      | {'precision': 0.7871559633027523, 'recall': 0.8056338028169014, 'f1': 0.7962877030162413, 'number': 1065}   | 0.7148            | 0.7747         | 0.7436     | 0.8047           |
+| 0.3511        | 10.0  | 100  | 0.6675          | {'precision': 0.6853932584269663, 'recall': 0.8294190358467244, 'f1': 0.750559284116331, 'number': 809}       | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}    | {'precision': 0.7851239669421488, 'recall': 0.8028169014084507, 'f1': 0.7938718662952646, 'number': 1065}   | 0.7141            | 0.7832         | 0.7471     | 0.8062           |
+| 0.3236        | 11.0  | 110  | 0.6729          | {'precision': 0.7195121951219512, 'recall': 0.8022249690976514, 'f1': 0.7586206896551723, 'number': 809}      | {'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}   | {'precision': 0.774798927613941, 'recall': 0.8140845070422535, 'f1': 0.7939560439560438, 'number': 1065}    | 0.7227            | 0.7807         | 0.7506     | 0.8045           |
+| 0.307         | 12.0  | 120  | 0.6755          | {'precision': 0.6757575757575758, 'recall': 0.826946847960445, 'f1': 0.7437465258476932, 'number': 809}       | {'precision': 0.29365079365079366, 'recall': 0.31092436974789917, 'f1': 0.30204081632653057, 'number': 119}  | {'precision': 0.7683363148479427, 'recall': 0.8065727699530516, 'f1': 0.7869903802107192, 'number': 1065}   | 0.7005            | 0.7852         | 0.7405     | 0.8052           |
+| 0.2905        | 13.0  | 130  | 0.6712          | {'precision': 0.6970021413276232, 'recall': 0.8046971569839307, 'f1': 0.7469879518072289, 'number': 809}      | {'precision': 0.3007518796992481, 'recall': 0.33613445378151263, 'f1': 0.31746031746031744, 'number': 119}   | {'precision': 0.7817028985507246, 'recall': 0.8103286384976526, 'f1': 0.7957584140156754, 'number': 1065}   | 0.7158            | 0.7797         | 0.7464     | 0.8067           |
+| 0.2734        | 14.0  | 140  | 0.6758          | {'precision': 0.6912681912681913, 'recall': 0.8220024721878862, 'f1': 0.7509881422924901, 'number': 809}      | {'precision': 0.3089430894308943, 'recall': 0.31932773109243695, 'f1': 0.3140495867768595, 'number': 119}    | {'precision': 0.7850045167118338, 'recall': 0.815962441314554, 'f1': 0.8001841620626151, 'number': 1065}    | 0.7172            | 0.7888         | 0.7513     | 0.8097           |
+| 0.2672        | 15.0  | 150  | 0.6741          | {'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809}      | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}   | {'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065}   | 0.7178            | 0.7913         | 0.7527     | 0.8085           |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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": ""
+}

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]"
+}