End of training

README.md

@@ -0,0 +1,81 @@
+---
+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: 1.1243
+- Answer: {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809}
+- Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}
+- Question: {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065}
+- Overall Precision: 0.4616
+- Overall Recall: 0.5459
+- Overall F1: 0.5002
+- Overall Accuracy: 0.6215
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7728        | 1.0   | 10   | 1.5441          | {'precision': 0.04580152671755725, 'recall': 0.059332509270704575, 'f1': 0.05169628432956382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.20335429769392033, 'recall': 0.18215962441314554, 'f1': 0.19217434373452202, 'number': 1065} | 0.1209            | 0.1214         | 0.1212     | 0.3719           |
+| 1.4551        | 2.0   | 20   | 1.3517          | {'precision': 0.20478234212139793, 'recall': 0.41285537700865266, 'f1': 0.27377049180327867, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.26090225563909775, 'recall': 0.32582159624413143, 'f1': 0.28977035490605424, 'number': 1065} | 0.2297            | 0.3417         | 0.2747     | 0.4263           |
+| 1.295         | 3.0   | 30   | 1.2465          | {'precision': 0.26224426534407935, 'recall': 0.522867737948084, 'f1': 0.34929810074318746, 'number': 809}    | {'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119} | {'precision': 0.3458528951486698, 'recall': 0.41502347417840374, 'f1': 0.37729406743491256, 'number': 1065}  | 0.2964            | 0.4350         | 0.3526     | 0.4803           |
+| 1.1635        | 4.0   | 40   | 1.1449          | {'precision': 0.28778467908902694, 'recall': 0.515451174289246, 'f1': 0.3693534100974314, 'number': 809}     | {'precision': 0.2638888888888889, 'recall': 0.15966386554621848, 'f1': 0.19895287958115182, 'number': 119}    | {'precision': 0.412396694214876, 'recall': 0.46854460093896716, 'f1': 0.4386813186813187, 'number': 1065}    | 0.3424            | 0.4691         | 0.3959     | 0.5521           |
+| 1.0456        | 5.0   | 50   | 1.0703          | {'precision': 0.3060240963855422, 'recall': 0.47095179233621753, 'f1': 0.37098344693281404, 'number': 809}   | {'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119}     | {'precision': 0.40298507462686567, 'recall': 0.5830985915492958, 'f1': 0.476592478894858, 'number': 1065}    | 0.3593            | 0.5153         | 0.4234     | 0.5797           |
+| 0.9601        | 6.0   | 60   | 1.2304          | {'precision': 0.30907920154539603, 'recall': 0.5933250927070457, 'f1': 0.40643522438611346, 'number': 809}   | {'precision': 0.3333333333333333, 'recall': 0.16806722689075632, 'f1': 0.223463687150838, 'number': 119}      | {'precision': 0.4642857142857143, 'recall': 0.4394366197183099, 'f1': 0.4515195369030391, 'number': 1065}    | 0.3693            | 0.4857         | 0.4196     | 0.5479           |
+| 0.9153        | 7.0   | 70   | 1.1091          | {'precision': 0.35518157661647476, 'recall': 0.4956736711990111, 'f1': 0.41382868937048506, 'number': 809}   | {'precision': 0.3125, 'recall': 0.21008403361344538, 'f1': 0.25125628140703515, 'number': 119}                | {'precision': 0.5262645914396887, 'recall': 0.507981220657277, 'f1': 0.5169612995699953, 'number': 1065}     | 0.4323            | 0.4852         | 0.4572     | 0.6011           |
+| 0.8346        | 8.0   | 80   | 1.0632          | {'precision': 0.35597826086956524, 'recall': 0.4857849196538937, 'f1': 0.4108729743857816, 'number': 809}    | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}     | {'precision': 0.46401799100449775, 'recall': 0.5812206572769953, 'f1': 0.516048353480617, 'number': 1065}    | 0.4102            | 0.5213         | 0.4591     | 0.6103           |
+| 0.7789        | 9.0   | 90   | 1.0955          | {'precision': 0.3817062445030783, 'recall': 0.5364647713226205, 'f1': 0.44604316546762585, 'number': 809}    | {'precision': 0.26, 'recall': 0.2184873949579832, 'f1': 0.23744292237442924, 'number': 119}                   | {'precision': 0.5137693631669535, 'recall': 0.5605633802816902, 'f1': 0.5361472833408173, 'number': 1065}    | 0.4406            | 0.5304         | 0.4813     | 0.6082           |
+| 0.7751        | 10.0  | 100  | 1.1232          | {'precision': 0.38474434199497065, 'recall': 0.5673671199011124, 'f1': 0.45854145854145856, 'number': 809}   | {'precision': 0.3010752688172043, 'recall': 0.23529411764705882, 'f1': 0.2641509433962264, 'number': 119}     | {'precision': 0.5040358744394619, 'recall': 0.5276995305164319, 'f1': 0.5155963302752293, 'number': 1065}    | 0.4369            | 0.5263         | 0.4775     | 0.6032           |
+| 0.6875        | 11.0  | 110  | 1.1092          | {'precision': 0.39342723004694835, 'recall': 0.5179233621755254, 'f1': 0.44717182497331914, 'number': 809}   | {'precision': 0.34146341463414637, 'recall': 0.23529411764705882, 'f1': 0.27860696517412936, 'number': 119}   | {'precision': 0.5076305220883535, 'recall': 0.5934272300469483, 'f1': 0.5471861471861472, 'number': 1065}    | 0.4511            | 0.5414         | 0.4921     | 0.6233           |
+| 0.6808        | 12.0  | 120  | 1.1286          | {'precision': 0.40641158221303, 'recall': 0.4857849196538937, 'f1': 0.44256756756756754, 'number': 809}      | {'precision': 0.24561403508771928, 'recall': 0.23529411764705882, 'f1': 0.24034334763948498, 'number': 119}   | {'precision': 0.49772036474164133, 'recall': 0.6150234741784038, 'f1': 0.5501889962200757, 'number': 1065}   | 0.4489            | 0.5399         | 0.4902     | 0.6159           |
+| 0.656         | 13.0  | 130  | 1.1237          | {'precision': 0.39822134387351776, 'recall': 0.49814585908529047, 'f1': 0.442613948380011, 'number': 809}    | {'precision': 0.2967032967032967, 'recall': 0.226890756302521, 'f1': 0.2571428571428572, 'number': 119}       | {'precision': 0.5141732283464567, 'recall': 0.6131455399061033, 'f1': 0.5593147751605996, 'number': 1065}    | 0.4564            | 0.5434         | 0.4961     | 0.6179           |
+| 0.6359        | 14.0  | 140  | 1.1296          | {'precision': 0.3996399639963996, 'recall': 0.5488257107540173, 'f1': 0.46249999999999997, 'number': 809}    | {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}     | {'precision': 0.5376712328767124, 'recall': 0.5896713615023474, 'f1': 0.5624720107478729, 'number': 1065}    | 0.4655            | 0.5514         | 0.5048     | 0.6173           |
+| 0.6117        | 15.0  | 150  | 1.1243          | {'precision': 0.40076335877862596, 'recall': 0.519159456118665, 'f1': 0.4523424878836834, 'number': 809}     | {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}     | {'precision': 0.5280065897858319, 'recall': 0.6018779342723005, 'f1': 0.5625274243089073, 'number': 1065}    | 0.4616            | 0.5459         | 0.5002     | 0.6215           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1709869740.66dbcb48ba8b.1198.0

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preprocessor_config.json

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+{
+  "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

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+{
+  "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
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+  "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

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+{
+  "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
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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
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+      "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": [
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+    0,
+    0,
+    0
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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
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+    1000,
+    1000,
+    1000
+  ],
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}