End of training

README.md

@@ -0,0 +1,80 @@
+---
+library_name: transformers
+license: mit
+base_model: microsoft/layoutlm-base-uncased
+tags:
+- generated_from_trainer
+model-index:
+- name: layoutlm-FUNSDxSynthetic-1fold
+  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-FUNSDxSynthetic-1fold
+
+This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7358
+- Eader: {'precision': 0.4666666666666667, 'recall': 0.3373493975903614, 'f1': 0.3916083916083916, 'number': 83}
+- Nswer: {'precision': 0.5065502183406113, 'recall': 0.5658536585365853, 'f1': 0.5345622119815668, 'number': 205}
+- Uestion: {'precision': 0.43373493975903615, 'recall': 0.4675324675324675, 'f1': 0.45, 'number': 231}
+- Overall Precision: 0.4684
+- Overall Recall: 0.4855
+- Overall F1: 0.4768
+- Overall Accuracy: 0.7947
+
+## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                       | Nswer                                                                                                       | Uestion                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.2347        | 1.0   | 12   | 1.0175          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83}                                                  | {'precision': 0.12563667232597622, 'recall': 0.36097560975609755, 'f1': 0.18639798488664988, 'number': 205} | {'precision': 0.11375212224108659, 'recall': 0.29004329004329005, 'f1': 0.16341463414634144, 'number': 231} | 0.1197            | 0.2717         | 0.1662     | 0.6488           |
+| 0.9147        | 2.0   | 24   | 0.7456          | {'precision': 0.07142857142857142, 'recall': 0.012048192771084338, 'f1': 0.02061855670103093, 'number': 83} | {'precision': 0.2911764705882353, 'recall': 0.48292682926829267, 'f1': 0.36330275229357795, 'number': 205}  | {'precision': 0.24507042253521127, 'recall': 0.37662337662337664, 'f1': 0.29692832764505117, 'number': 231} | 0.2638            | 0.3603         | 0.3046     | 0.7487           |
+| 0.675         | 3.0   | 36   | 0.6233          | {'precision': 0.19148936170212766, 'recall': 0.10843373493975904, 'f1': 0.13846153846153844, 'number': 83}  | {'precision': 0.36551724137931035, 'recall': 0.5170731707317073, 'f1': 0.42828282828282827, 'number': 205}  | {'precision': 0.32094594594594594, 'recall': 0.41125541125541126, 'f1': 0.36053130929791266, 'number': 231} | 0.3318            | 0.4046         | 0.3646     | 0.7793           |
+| 0.4928        | 4.0   | 48   | 0.6064          | {'precision': 0.3584905660377358, 'recall': 0.2289156626506024, 'f1': 0.2794117647058823, 'number': 83}     | {'precision': 0.42592592592592593, 'recall': 0.5609756097560976, 'f1': 0.4842105263157895, 'number': 205}   | {'precision': 0.34172661870503596, 'recall': 0.41125541125541126, 'f1': 0.37328094302554027, 'number': 231} | 0.3810            | 0.4412         | 0.4089     | 0.7733           |
+| 0.4142        | 5.0   | 60   | 0.5817          | {'precision': 0.4489795918367347, 'recall': 0.26506024096385544, 'f1': 0.33333333333333337, 'number': 83}   | {'precision': 0.4573643410852713, 'recall': 0.5756097560975609, 'f1': 0.5097192224622029, 'number': 205}    | {'precision': 0.3754646840148699, 'recall': 0.43722943722943725, 'f1': 0.404, 'number': 231}                | 0.4184            | 0.4644         | 0.4402     | 0.7878           |
+| 0.3198        | 6.0   | 72   | 0.5888          | {'precision': 0.39285714285714285, 'recall': 0.26506024096385544, 'f1': 0.3165467625899281, 'number': 83}   | {'precision': 0.4674329501915709, 'recall': 0.5951219512195122, 'f1': 0.5236051502145922, 'number': 205}    | {'precision': 0.381294964028777, 'recall': 0.4588744588744589, 'f1': 0.4165029469548133, 'number': 231}     | 0.4202            | 0.4817         | 0.4488     | 0.7998           |
+| 0.2752        | 7.0   | 84   | 0.6608          | {'precision': 0.3968253968253968, 'recall': 0.30120481927710846, 'f1': 0.34246575342465757, 'number': 83}   | {'precision': 0.47639484978540775, 'recall': 0.5414634146341464, 'f1': 0.5068493150684932, 'number': 205}   | {'precision': 0.40458015267175573, 'recall': 0.4588744588744589, 'f1': 0.4300202839756592, 'number': 231}   | 0.4337            | 0.4663         | 0.4494     | 0.7728           |
+| 0.2275        | 8.0   | 96   | 0.6552          | {'precision': 0.3924050632911392, 'recall': 0.37349397590361444, 'f1': 0.38271604938271603, 'number': 83}   | {'precision': 0.47478991596638653, 'recall': 0.551219512195122, 'f1': 0.510158013544018, 'number': 205}     | {'precision': 0.37593984962406013, 'recall': 0.4329004329004329, 'f1': 0.40241448692152915, 'number': 231}  | 0.4185            | 0.4701         | 0.4428     | 0.7848           |
+| 0.1947        | 9.0   | 108  | 0.6603          | {'precision': 0.4166666666666667, 'recall': 0.30120481927710846, 'f1': 0.34965034965034963, 'number': 83}   | {'precision': 0.4769874476987448, 'recall': 0.5560975609756098, 'f1': 0.5135135135135136, 'number': 205}    | {'precision': 0.3917910447761194, 'recall': 0.45454545454545453, 'f1': 0.42084168336673344, 'number': 231}  | 0.4303            | 0.4701         | 0.4494     | 0.7867           |
+| 0.169         | 10.0  | 120  | 0.6796          | {'precision': 0.4264705882352941, 'recall': 0.3493975903614458, 'f1': 0.3841059602649007, 'number': 83}     | {'precision': 0.4915254237288136, 'recall': 0.5658536585365853, 'f1': 0.5260770975056689, 'number': 205}    | {'precision': 0.39015151515151514, 'recall': 0.4458874458874459, 'f1': 0.4161616161616162, 'number': 231}   | 0.4366            | 0.4778         | 0.4563     | 0.7891           |
+| 0.1462        | 11.0  | 132  | 0.6880          | {'precision': 0.41379310344827586, 'recall': 0.2891566265060241, 'f1': 0.3404255319148936, 'number': 83}    | {'precision': 0.5109170305676856, 'recall': 0.5707317073170731, 'f1': 0.5391705069124425, 'number': 205}    | {'precision': 0.41832669322709165, 'recall': 0.45454545454545453, 'f1': 0.43568464730290457, 'number': 231} | 0.4572            | 0.4740         | 0.4655     | 0.7949           |
+| 0.1295        | 12.0  | 144  | 0.7007          | {'precision': 0.43283582089552236, 'recall': 0.3493975903614458, 'f1': 0.38666666666666666, 'number': 83}   | {'precision': 0.4936708860759494, 'recall': 0.5707317073170731, 'f1': 0.5294117647058822, 'number': 205}    | {'precision': 0.42023346303501946, 'recall': 0.4675324675324675, 'f1': 0.4426229508196721, 'number': 231}   | 0.4528            | 0.4894         | 0.4704     | 0.7951           |
+| 0.1208        | 13.0  | 156  | 0.7216          | {'precision': 0.4727272727272727, 'recall': 0.3132530120481928, 'f1': 0.3768115942028985, 'number': 83}     | {'precision': 0.4957983193277311, 'recall': 0.5756097560975609, 'f1': 0.5327313769751693, 'number': 205}    | {'precision': 0.42248062015503873, 'recall': 0.47186147186147187, 'f1': 0.44580777096114516, 'number': 231} | 0.4592            | 0.4875         | 0.4729     | 0.7974           |
+| 0.1146        | 14.0  | 168  | 0.7440          | {'precision': 0.45901639344262296, 'recall': 0.3373493975903614, 'f1': 0.38888888888888884, 'number': 83}   | {'precision': 0.5066666666666667, 'recall': 0.5560975609756098, 'f1': 0.5302325581395348, 'number': 205}    | {'precision': 0.4291497975708502, 'recall': 0.4588744588744589, 'f1': 0.4435146443514644, 'number': 231}    | 0.4653            | 0.4778         | 0.4715     | 0.7910           |
+| 0.1083        | 15.0  | 180  | 0.7358          | {'precision': 0.4666666666666667, 'recall': 0.3373493975903614, 'f1': 0.3916083916083916, 'number': 83}     | {'precision': 0.5065502183406113, 'recall': 0.5658536585365853, 'f1': 0.5345622119815668, 'number': 205}    | {'precision': 0.43373493975903615, 'recall': 0.4675324675324675, 'f1': 0.45, 'number': 231}                 | 0.4684            | 0.4855         | 0.4768     | 0.7947           |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

logs/events.out.tfevents.1741266190.DESKTOP-HA84SVN.94162.5

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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": {
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+    "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,
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+  "cls_token": "[CLS]",
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "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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+  "processor_class": "LayoutLMv2Processor",
+  "sep_token": "[SEP]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "LayoutLMv2Tokenizer",
+  "unk_token": "[UNK]"
+}