End of training

README.md

@@ -1,81 +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: 0.8707
-- Answer: {'precision': 0.731359649122807, 'recall': 0.8244746600741656, 'f1': 0.7751307379430563, 'number': 809}
-- Header: {'precision': 0.47101449275362317, 'recall': 0.5462184873949579, 'f1': 0.5058365758754864, 'number': 119}
-- Question: {'precision': 0.8043087971274686, 'recall': 0.8413145539906103, 'f1': 0.8223955943093162, 'number': 1065}
-- Overall Precision: 0.7523
-- Overall Recall: 0.8169
-- Overall F1: 0.7833
-- Overall Accuracy: 0.8120
-
-## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.6648        | 1.0   | 19   | 1.2802          | {'precision': 0.24287028518859247, 'recall': 0.3263288009888752, 'f1': 0.2784810126582279, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3659942363112392, 'recall': 0.596244131455399, 'f1': 0.45357142857142857, 'number': 1065} | 0.3186            | 0.4511         | 0.3734     | 0.5834           |
-| 1.0035        | 2.0   | 38   | 0.7824          | {'precision': 0.5849056603773585, 'recall': 0.7280593325092707, 'f1': 0.6486784140969163, 'number': 809}  | {'precision': 0.031746031746031744, 'recall': 0.01680672268907563, 'f1': 0.02197802197802198, 'number': 119} | {'precision': 0.6030042918454935, 'recall': 0.7915492957746478, 'f1': 0.684531059683313, 'number': 1065}  | 0.5810            | 0.7195         | 0.6429     | 0.7645           |
-| 0.6425        | 3.0   | 57   | 0.6680          | {'precision': 0.6607515657620042, 'recall': 0.7824474660074165, 'f1': 0.7164685908319186, 'number': 809}  | {'precision': 0.12037037037037036, 'recall': 0.1092436974789916, 'f1': 0.1145374449339207, 'number': 119}    | {'precision': 0.7028753993610224, 'recall': 0.8262910798122066, 'f1': 0.7596029348295209, 'number': 1065} | 0.6583            | 0.7657         | 0.7080     | 0.7896           |
-| 0.4629        | 4.0   | 76   | 0.6420          | {'precision': 0.6653102746693794, 'recall': 0.8084054388133498, 'f1': 0.7299107142857143, 'number': 809}  | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}   | {'precision': 0.7648578811369509, 'recall': 0.8338028169014085, 'f1': 0.7978436657681941, 'number': 1065} | 0.6986            | 0.7898         | 0.7414     | 0.8070           |
-| 0.3456        | 5.0   | 95   | 0.6765          | {'precision': 0.6901709401709402, 'recall': 0.7985166872682324, 'f1': 0.7404011461318052, 'number': 809}  | {'precision': 0.3007518796992481, 'recall': 0.33613445378151263, 'f1': 0.31746031746031744, 'number': 119}   | {'precision': 0.7731685789938217, 'recall': 0.8225352112676056, 'f1': 0.7970882620564149, 'number': 1065} | 0.7094            | 0.7837         | 0.7447     | 0.8024           |
-| 0.2667        | 6.0   | 114  | 0.6940          | {'precision': 0.6908315565031983, 'recall': 0.8009888751545118, 'f1': 0.7418431597023468, 'number': 809}  | {'precision': 0.35294117647058826, 'recall': 0.35294117647058826, 'f1': 0.35294117647058826, 'number': 119}  | {'precision': 0.7787610619469026, 'recall': 0.8262910798122066, 'f1': 0.8018223234624146, 'number': 1065} | 0.7179            | 0.7878         | 0.7512     | 0.8054           |
-| 0.2131        | 7.0   | 133  | 0.7425          | {'precision': 0.696652719665272, 'recall': 0.823238566131026, 'f1': 0.7546742209631728, 'number': 809}    | {'precision': 0.40310077519379844, 'recall': 0.4369747899159664, 'f1': 0.4193548387096774, 'number': 119}    | {'precision': 0.8073394495412844, 'recall': 0.8262910798122066, 'f1': 0.8167053364269143, 'number': 1065} | 0.7347            | 0.8018         | 0.7668     | 0.8062           |
-| 0.1712        | 8.0   | 152  | 0.7707          | {'precision': 0.7065101387406617, 'recall': 0.8182941903584673, 'f1': 0.7583046964490264, 'number': 809}  | {'precision': 0.39215686274509803, 'recall': 0.5042016806722689, 'f1': 0.4411764705882353, 'number': 119}    | {'precision': 0.8030713640469738, 'recall': 0.8347417840375587, 'f1': 0.8186003683241252, 'number': 1065} | 0.7333            | 0.8083         | 0.7690     | 0.8080           |
-| 0.1426        | 9.0   | 171  | 0.7811          | {'precision': 0.717391304347826, 'recall': 0.8158220024721878, 'f1': 0.7634470792365529, 'number': 809}   | {'precision': 0.4274193548387097, 'recall': 0.44537815126050423, 'f1': 0.43621399176954734, 'number': 119}   | {'precision': 0.7968056787932565, 'recall': 0.8431924882629108, 'f1': 0.8193430656934306, 'number': 1065} | 0.7421            | 0.8083         | 0.7738     | 0.8127           |
-| 0.1229        | 10.0  | 190  | 0.8075          | {'precision': 0.7242524916943521, 'recall': 0.8084054388133498, 'f1': 0.764018691588785, 'number': 809}   | {'precision': 0.4722222222222222, 'recall': 0.5714285714285714, 'f1': 0.5171102661596959, 'number': 119}     | {'precision': 0.8030438675022381, 'recall': 0.8422535211267606, 'f1': 0.8221814848762603, 'number': 1065} | 0.7482            | 0.8123         | 0.7789     | 0.8139           |
-| 0.1023        | 11.0  | 209  | 0.8426          | {'precision': 0.7358490566037735, 'recall': 0.8195302843016069, 'f1': 0.775438596491228, 'number': 809}   | {'precision': 0.45774647887323944, 'recall': 0.5462184873949579, 'f1': 0.49808429118773945, 'number': 119}   | {'precision': 0.7978339350180506, 'recall': 0.8300469483568075, 'f1': 0.8136217211228716, 'number': 1065} | 0.7494            | 0.8088         | 0.7780     | 0.8106           |
-| 0.0935        | 12.0  | 228  | 0.8502          | {'precision': 0.7300546448087432, 'recall': 0.8257107540173053, 'f1': 0.7749419953596288, 'number': 809}  | {'precision': 0.4520547945205479, 'recall': 0.5546218487394958, 'f1': 0.4981132075471698, 'number': 119}     | {'precision': 0.7978339350180506, 'recall': 0.8300469483568075, 'f1': 0.8136217211228716, 'number': 1065} | 0.7460            | 0.8118         | 0.7775     | 0.8110           |
-| 0.0829        | 13.0  | 247  | 0.8513          | {'precision': 0.7335562987736901, 'recall': 0.8133498145859085, 'f1': 0.7713950762016414, 'number': 809}  | {'precision': 0.4589041095890411, 'recall': 0.5630252100840336, 'f1': 0.5056603773584906, 'number': 119}     | {'precision': 0.8018018018018018, 'recall': 0.8356807511737089, 'f1': 0.8183908045977012, 'number': 1065} | 0.7501            | 0.8103         | 0.7791     | 0.8124           |
-| 0.0801        | 14.0  | 266  | 0.8655          | {'precision': 0.729490022172949, 'recall': 0.8133498145859085, 'f1': 0.7691408533021624, 'number': 809}   | {'precision': 0.4507042253521127, 'recall': 0.5378151260504201, 'f1': 0.4904214559386973, 'number': 119}     | {'precision': 0.8057553956834532, 'recall': 0.8413145539906103, 'f1': 0.8231511254019293, 'number': 1065} | 0.7505            | 0.8118         | 0.7799     | 0.8110           |
-| 0.0751        | 15.0  | 285  | 0.8707          | {'precision': 0.731359649122807, 'recall': 0.8244746600741656, 'f1': 0.7751307379430563, 'number': 809}   | {'precision': 0.47101449275362317, 'recall': 0.5462184873949579, 'f1': 0.5058365758754864, 'number': 119}    | {'precision': 0.8043087971274686, 'recall': 0.8413145539906103, 'f1': 0.8223955943093162, 'number': 1065} | 0.7523            | 0.8169         | 0.7833     | 0.8120           |
-
-
-### Framework versions
-
-- Transformers 4.41.2
-- Pytorch 2.3.1+cu118
-- Datasets 2.19.2
-- Tokenizers 0.19.1
+---
+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: 0.7113
+- Answer: {'precision': 0.7158962795941376, 'recall': 0.7849196538936959, 'f1': 0.7488207547169812, 'number': 809}
+- Header: {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}
+- Question: {'precision': 0.7726480836236934, 'recall': 0.8328638497652582, 'f1': 0.8016267510167194, 'number': 1065}
+- Overall Precision: 0.7258
+- Overall Recall: 0.7837
+- Overall F1: 0.7537
+- Overall Accuracy: 0.8028
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7452        | 1.0   | 10   | 1.5534          | {'precision': 0.03913894324853229, 'recall': 0.049443757725587144, 'f1': 0.043691971600218454, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.26121794871794873, 'recall': 0.3061032863849765, 'f1': 0.2818849978383052, 'number': 1065} | 0.1612            | 0.1836         | 0.1717     | 0.4436           |
+| 1.3859        | 2.0   | 20   | 1.1959          | {'precision': 0.3140161725067385, 'recall': 0.2880098887515451, 'f1': 0.30045132172791744, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.5303030303030303, 'recall': 0.5915492957746479, 'f1': 0.559254327563249, 'number': 1065}   | 0.4467            | 0.4330         | 0.4397     | 0.6150           |
+| 1.037         | 3.0   | 30   | 0.8960          | {'precision': 0.5337078651685393, 'recall': 0.5871446229913473, 'f1': 0.559152442613302, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.616597510373444, 'recall': 0.6976525821596244, 'f1': 0.654625550660793, 'number': 1065}    | 0.5724            | 0.6111         | 0.5911     | 0.7266           |
+| 0.7743        | 4.0   | 40   | 0.7486          | {'precision': 0.6307363927427961, 'recall': 0.73053152039555, 'f1': 0.6769759450171822, 'number': 809}        | {'precision': 0.1044776119402985, 'recall': 0.058823529411764705, 'f1': 0.07526881720430108, 'number': 119} | {'precision': 0.6512013256006628, 'recall': 0.7380281690140845, 'f1': 0.6919014084507042, 'number': 1065}  | 0.6260            | 0.6944         | 0.6584     | 0.7694           |
+| 0.6151        | 5.0   | 50   | 0.7067          | {'precision': 0.6449511400651465, 'recall': 0.7342398022249691, 'f1': 0.6867052023121387, 'number': 809}      | {'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119}  | {'precision': 0.6762589928057554, 'recall': 0.7943661971830986, 'f1': 0.7305699481865285, 'number': 1065}  | 0.6466            | 0.7316         | 0.6864     | 0.7804           |
+| 0.5319        | 6.0   | 60   | 0.6947          | {'precision': 0.6685022026431718, 'recall': 0.7503090234857849, 'f1': 0.707047175305766, 'number': 809}       | {'precision': 0.24096385542168675, 'recall': 0.16806722689075632, 'f1': 0.19801980198019803, 'number': 119} | {'precision': 0.7186700767263428, 'recall': 0.7915492957746478, 'f1': 0.7533512064343162, 'number': 1065}  | 0.6793            | 0.7376         | 0.7072     | 0.7920           |
+| 0.4602        | 7.0   | 70   | 0.6794          | {'precision': 0.6762430939226519, 'recall': 0.7564894932014833, 'f1': 0.7141190198366394, 'number': 809}      | {'precision': 0.28225806451612906, 'recall': 0.29411764705882354, 'f1': 0.2880658436213992, 'number': 119}  | {'precision': 0.7359307359307359, 'recall': 0.7981220657276995, 'f1': 0.7657657657657657, 'number': 1065}  | 0.6854            | 0.7511         | 0.7168     | 0.7980           |
+| 0.4119        | 8.0   | 80   | 0.6743          | {'precision': 0.6659619450317125, 'recall': 0.7787391841779975, 'f1': 0.7179487179487181, 'number': 809}      | {'precision': 0.32, 'recall': 0.2689075630252101, 'f1': 0.2922374429223744, 'number': 119}                  | {'precision': 0.7383820998278829, 'recall': 0.8056338028169014, 'f1': 0.7705433318365513, 'number': 1065}  | 0.6884            | 0.7627         | 0.7236     | 0.7996           |
+| 0.3663        | 9.0   | 90   | 0.6797          | {'precision': 0.6924803591470258, 'recall': 0.7626699629171817, 'f1': 0.7258823529411764, 'number': 809}      | {'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119}  | {'precision': 0.7450812660393499, 'recall': 0.8178403755868544, 'f1': 0.7797672336615935, 'number': 1065}  | 0.6999            | 0.7642         | 0.7306     | 0.7968           |
+| 0.3556        | 10.0  | 100  | 0.6809          | {'precision': 0.699666295884316, 'recall': 0.7775030902348579, 'f1': 0.7365339578454333, 'number': 809}       | {'precision': 0.34615384615384615, 'recall': 0.3025210084033613, 'f1': 0.32286995515695066, 'number': 119}  | {'precision': 0.7615720524017467, 'recall': 0.8187793427230047, 'f1': 0.7891402714932126, 'number': 1065}  | 0.7155            | 0.7712         | 0.7423     | 0.8013           |
+| 0.3051        | 11.0  | 110  | 0.6935          | {'precision': 0.6933187294633077, 'recall': 0.7824474660074165, 'f1': 0.7351916376306621, 'number': 809}      | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119}   | {'precision': 0.764102564102564, 'recall': 0.8394366197183099, 'f1': 0.7999999999999999, 'number': 1065}   | 0.7116            | 0.7847         | 0.7464     | 0.8018           |
+| 0.2905        | 12.0  | 120  | 0.7059          | {'precision': 0.7200929152148664, 'recall': 0.7663782447466008, 'f1': 0.7425149700598803, 'number': 809}      | {'precision': 0.35185185185185186, 'recall': 0.31932773109243695, 'f1': 0.33480176211453744, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}  | 0.7279            | 0.7717         | 0.7491     | 0.7986           |
+| 0.2804        | 13.0  | 130  | 0.7065          | {'precision': 0.709211986681465, 'recall': 0.7898640296662547, 'f1': 0.7473684210526316, 'number': 809}       | {'precision': 0.35135135135135137, 'recall': 0.3277310924369748, 'f1': 0.3391304347826087, 'number': 119}   | {'precision': 0.7648068669527897, 'recall': 0.8366197183098592, 'f1': 0.7991031390134529, 'number': 1065}  | 0.7207            | 0.7873         | 0.7525     | 0.8008           |
+| 0.261         | 14.0  | 140  | 0.7096          | {'precision': 0.713963963963964, 'recall': 0.7836835599505563, 'f1': 0.7472009428403065, 'number': 809}       | {'precision': 0.3448275862068966, 'recall': 0.33613445378151263, 'f1': 0.3404255319148936, 'number': 119}   | {'precision': 0.7722943722943723, 'recall': 0.8375586854460094, 'f1': 0.8036036036036036, 'number': 1065}  | 0.7253            | 0.7858         | 0.7543     | 0.8028           |
+| 0.2537        | 15.0  | 150  | 0.7113          | {'precision': 0.7158962795941376, 'recall': 0.7849196538936959, 'f1': 0.7488207547169812, 'number': 809}      | {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}   | {'precision': 0.7726480836236934, 'recall': 0.8328638497652582, 'f1': 0.8016267510167194, 'number': 1065}  | 0.7258            | 0.7837         | 0.7537     | 0.8028           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

logs/events.out.tfevents.1718614849.HCIDC-SV-DMZ-ORC-NODE02.723418.0

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model.safetensors

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preprocessor_config.json

@@ -1,25 +1,25 @@
-{
-  "_valid_processor_keys": [
-    "images",
-    "do_resize",
-    "size",
-    "resample",
-    "apply_ocr",
-    "ocr_lang",
-    "tesseract_config",
-    "return_tensors",
-    "data_format",
-    "input_data_format"
-  ],
-  "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": ""
-}
+{
+  "_valid_processor_keys": [
+    "images",
+    "do_resize",
+    "size",
+    "resample",
+    "apply_ocr",
+    "ocr_lang",
+    "tesseract_config",
+    "return_tensors",
+    "data_format",
+    "input_data_format"
+  ],
+  "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

@@ -1,37 +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
-  }
-}
+{
+  "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

@@ -1,80 +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]"
-}
+{
+  "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]"
+}