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: 1.1695
+- Answer: {'precision': 0.35993485342019543, 'recall': 0.546353522867738, 'f1': 0.4339715267550319, 'number': 809}
+- Header: {'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119}
+- Question: {'precision': 0.5217762596071733, 'recall': 0.5737089201877934, 'f1': 0.5465116279069767, 'number': 1065}
+- Overall Precision: 0.4358
+- Overall Recall: 0.5434
+- Overall F1: 0.4837
+- Overall Accuracy: 0.5907
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7983        | 1.0   | 10   | 1.5799          | {'precision': 0.02035830618892508, 'recall': 0.030902348578491966, 'f1': 0.024545900834560624, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.19827586206896552, 'recall': 0.1295774647887324, 'f1': 0.15672913117546847, 'number': 1065} | 0.0847            | 0.0818         | 0.0832     | 0.3629           |
+| 1.5573        | 2.0   | 20   | 1.4500          | {'precision': 0.11375661375661375, 'recall': 0.2126081582200247, 'f1': 0.14821197759586383, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.21101928374655649, 'recall': 0.3596244131455399, 'f1': 0.2659722222222222, 'number': 1065}  | 0.1668            | 0.2785         | 0.2086     | 0.4045           |
+| 1.3717        | 3.0   | 30   | 1.2959          | {'precision': 0.16349108789182545, 'recall': 0.3288009888751545, 'f1': 0.21839080459770116, 'number': 809}    | {'precision': 0.061224489795918366, 'recall': 0.025210084033613446, 'f1': 0.03571428571428571, 'number': 119} | {'precision': 0.2739408009286129, 'recall': 0.4431924882629108, 'f1': 0.3385939741750359, 'number': 1065}   | 0.2180            | 0.3718         | 0.2749     | 0.4466           |
+| 1.2568        | 4.0   | 40   | 1.2032          | {'precision': 0.20166898470097358, 'recall': 0.3584672435105068, 'f1': 0.258121940364931, 'number': 809}      | {'precision': 0.23809523809523808, 'recall': 0.16806722689075632, 'f1': 0.19704433497536947, 'number': 119}   | {'precision': 0.3354214123006834, 'recall': 0.5530516431924882, 'f1': 0.4175824175824176, 'number': 1065}   | 0.2743            | 0.4511         | 0.3411     | 0.4942           |
+| 1.1591        | 5.0   | 50   | 1.1897          | {'precision': 0.23584277148567623, 'recall': 0.43757725587144625, 'f1': 0.30649350649350654, 'number': 809}   | {'precision': 0.27848101265822783, 'recall': 0.18487394957983194, 'f1': 0.2222222222222222, 'number': 119}    | {'precision': 0.36172765446910615, 'recall': 0.5661971830985916, 'f1': 0.44143484626647145, 'number': 1065} | 0.3015            | 0.4912         | 0.3737     | 0.5069           |
+| 1.075         | 6.0   | 60   | 1.1092          | {'precision': 0.2832044975404076, 'recall': 0.49814585908529047, 'f1': 0.36111111111111116, 'number': 809}    | {'precision': 0.34375, 'recall': 0.18487394957983194, 'f1': 0.24043715846994534, 'number': 119}               | {'precision': 0.4609571788413098, 'recall': 0.5154929577464789, 'f1': 0.48670212765957444, 'number': 1065}  | 0.3637            | 0.4887         | 0.4170     | 0.5650           |
+| 1.0011        | 7.0   | 70   | 1.0638          | {'precision': 0.30843373493975906, 'recall': 0.4746600741656366, 'f1': 0.3739045764362221, 'number': 809}     | {'precision': 0.3333333333333333, 'recall': 0.19327731092436976, 'f1': 0.24468085106382975, 'number': 119}    | {'precision': 0.47068021892103207, 'recall': 0.5652582159624413, 'f1': 0.5136518771331057, 'number': 1065}  | 0.3891            | 0.5063         | 0.4400     | 0.5852           |
+| 0.9357        | 8.0   | 80   | 1.1937          | {'precision': 0.3132867132867133, 'recall': 0.553770086526576, 'f1': 0.4001786511835641, 'number': 809}       | {'precision': 0.2987012987012987, 'recall': 0.19327731092436976, 'f1': 0.23469387755102045, 'number': 119}    | {'precision': 0.48117839607201307, 'recall': 0.5521126760563381, 'f1': 0.5142107564494972, 'number': 1065}  | 0.3881            | 0.5314         | 0.4485     | 0.5559           |
+| 0.8751        | 9.0   | 90   | 1.0553          | {'precision': 0.3413173652694611, 'recall': 0.4932014833127318, 'f1': 0.40343781597573314, 'number': 809}     | {'precision': 0.29577464788732394, 'recall': 0.17647058823529413, 'f1': 0.2210526315789474, 'number': 119}    | {'precision': 0.47987851176917234, 'recall': 0.5934272300469483, 'f1': 0.5306465155331653, 'number': 1065}  | 0.4114            | 0.5278         | 0.4624     | 0.5986           |
+| 0.8585        | 10.0  | 100  | 1.1844          | {'precision': 0.33745454545454545, 'recall': 0.5735475896168108, 'f1': 0.4249084249084249, 'number': 809}     | {'precision': 0.37333333333333335, 'recall': 0.23529411764705882, 'f1': 0.28865979381443296, 'number': 119}   | {'precision': 0.5013333333333333, 'recall': 0.5295774647887324, 'f1': 0.5150684931506849, 'number': 1065}   | 0.4101            | 0.5299         | 0.4623     | 0.5706           |
+| 0.7894        | 11.0  | 110  | 1.1752          | {'precision': 0.3395311236863379, 'recall': 0.519159456118665, 'f1': 0.41055718475073316, 'number': 809}      | {'precision': 0.4, 'recall': 0.23529411764705882, 'f1': 0.29629629629629634, 'number': 119}                   | {'precision': 0.5020885547201337, 'recall': 0.564319248826291, 'f1': 0.5313881520778072, 'number': 1065}    | 0.4189            | 0.5263         | 0.4665     | 0.5781           |
+| 0.7809        | 12.0  | 120  | 1.2025          | {'precision': 0.34820031298904536, 'recall': 0.5500618046971569, 'f1': 0.4264494489698131, 'number': 809}     | {'precision': 0.37037037037037035, 'recall': 0.25210084033613445, 'f1': 0.3, 'number': 119}                   | {'precision': 0.5093062605752962, 'recall': 0.5652582159624413, 'f1': 0.5358255451713396, 'number': 1065}   | 0.4238            | 0.5404         | 0.4751     | 0.5800           |
+| 0.7452        | 13.0  | 130  | 1.1974          | {'precision': 0.3550436854646545, 'recall': 0.5525339925834364, 'f1': 0.4323017408123791, 'number': 809}      | {'precision': 0.358974358974359, 'recall': 0.23529411764705882, 'f1': 0.28426395939086296, 'number': 119}     | {'precision': 0.5247787610619469, 'recall': 0.5568075117370892, 'f1': 0.5403189066059226, 'number': 1065}   | 0.4329            | 0.5359         | 0.4789     | 0.5830           |
+| 0.7108        | 14.0  | 140  | 1.1267          | {'precision': 0.373015873015873, 'recall': 0.522867737948084, 'f1': 0.43540916109109623, 'number': 809}       | {'precision': 0.3448275862068966, 'recall': 0.25210084033613445, 'f1': 0.2912621359223301, 'number': 119}     | {'precision': 0.5193548387096775, 'recall': 0.6046948356807512, 'f1': 0.5587852494577007, 'number': 1065}   | 0.4458            | 0.5504         | 0.4926     | 0.6044           |
+| 0.702         | 15.0  | 150  | 1.1695          | {'precision': 0.35993485342019543, 'recall': 0.546353522867738, 'f1': 0.4339715267550319, 'number': 809}      | {'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119}     | {'precision': 0.5217762596071733, 'recall': 0.5737089201877934, 'f1': 0.5465116279069767, 'number': 1065}   | 0.4358            | 0.5434         | 0.4837     | 0.5907           |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

logs/events.out.tfevents.1709773612.360e1c85cbe7.2486.0

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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
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+  "tesseract_config": ""
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "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
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+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "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
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "additional_special_tokens": [],
+  "apply_ocr": false,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "cls_token_box": [
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+  "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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+  "pad_token_label": -100,
+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
+  "sep_token_box": [
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}