End of training

README.md

@@ -4,8 +4,6 @@ license: mit
 base_model: microsoft/layoutlm-base-uncased
 tags:
 - generated_from_trainer
-datasets:
-- funsd
 model-index:
 - name: layoutlm-funsd
   results: []
@@ -16,16 +14,16 @@ 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.
+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: 0.7010
-- Eader: {'precision': 0.20833333333333334, 'recall': 0.10416666666666667, 'f1': 0.1388888888888889, 'number': 96}
-- Nswer: {'precision': 0.3401360544217687, 'recall': 0.5514705882352942, 'f1': 0.420757363253857, 'number': 272}
-- Uestion: {'precision': 0.3463414634146341, 'recall': 0.46557377049180326, 'f1': 0.39720279720279716, 'number': 305}
-- Overall Precision: 0.3359
-- Overall Recall: 0.4487
-- Overall F1: 0.3842
-- Overall Accuracy: 0.7593
+- Loss: 0.1967
+- Eader: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
+- Nswer: {'precision': 0.8354430379746836, 'recall': 0.88, 'f1': 0.8571428571428572, 'number': 75}
+- Uestion: {'precision': 0.8571428571428571, 'recall': 0.868421052631579, 'f1': 0.8627450980392157, 'number': 76}
+- Overall Precision: 0.8554
+- Overall Recall: 0.8820
+- Overall F1: 0.8685
+- Overall Accuracy: 0.9435
 
 ## Model description
 
@@ -48,35 +46,35 @@ The following hyperparameters were used during training:
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.3693        | 1.0   | 4    | 1.2778          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.010467980295566502, 'recall': 0.0625, 'f1': 0.017932489451476793, 'number': 272}             | {'precision': 0.010113780025284451, 'recall': 0.05245901639344262, 'f1': 0.01695813460519343, 'number': 305} | 0.0102            | 0.0490         | 0.0168     | 0.4230           |
-| 1.2081        | 2.0   | 8    | 1.1974          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.047841306884480746, 'recall': 0.15073529411764705, 'f1': 0.07263064658990256, 'number': 272} | {'precision': 0.05380116959064327, 'recall': 0.15081967213114755, 'f1': 0.0793103448275862, 'number': 305}   | 0.0508            | 0.1293         | 0.0730     | 0.4805           |
-| 1.0716        | 3.0   | 12   | 1.0999          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.08643457382953182, 'recall': 0.2647058823529412, 'f1': 0.13031674208144797, 'number': 272}   | {'precision': 0.10704960835509138, 'recall': 0.26885245901639343, 'f1': 0.1531279178338002, 'number': 305}   | 0.0963            | 0.2288         | 0.1356     | 0.5225           |
-| 0.855         | 4.0   | 16   | 0.9899          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.11137440758293839, 'recall': 0.34558823529411764, 'f1': 0.16845878136200718, 'number': 272}  | {'precision': 0.13172043010752688, 'recall': 0.32131147540983607, 'f1': 0.18684461391801713, 'number': 305}  | 0.1209            | 0.2853         | 0.1698     | 0.5886           |
-| 0.753         | 5.0   | 20   | 0.9095          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.1562043795620438, 'recall': 0.39338235294117646, 'f1': 0.22361546499477536, 'number': 272}   | {'precision': 0.15538461538461537, 'recall': 0.33114754098360655, 'f1': 0.21151832460732983, 'number': 305}  | 0.1558            | 0.3091         | 0.2072     | 0.6306           |
-| 0.6212        | 6.0   | 24   | 0.8551          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.19864176570458403, 'recall': 0.43014705882352944, 'f1': 0.27177700348432055, 'number': 272}  | {'precision': 0.19141323792486584, 'recall': 0.35081967213114756, 'f1': 0.2476851851851852, 'number': 305}   | 0.1951            | 0.3328         | 0.2460     | 0.6520           |
-| 0.637         | 7.0   | 28   | 0.7987          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.2270363951473137, 'recall': 0.48161764705882354, 'f1': 0.30859835100117783, 'number': 272}   | {'precision': 0.22201492537313433, 'recall': 0.3901639344262295, 'f1': 0.28299643281807374, 'number': 305}   | 0.2244            | 0.3715         | 0.2798     | 0.6884           |
-| 0.5212        | 8.0   | 32   | 0.7621          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96}                                                   | {'precision': 0.260707635009311, 'recall': 0.5147058823529411, 'f1': 0.34610630407911, 'number': 272}        | {'precision': 0.23529411764705882, 'recall': 0.380327868852459, 'f1': 0.2907268170426065, 'number': 305}     | 0.2471            | 0.3804         | 0.2996     | 0.7189           |
-| 0.4969        | 9.0   | 36   | 0.7494          | {'precision': 0.06666666666666667, 'recall': 0.010416666666666666, 'f1': 0.018018018018018014, 'number': 96} | {'precision': 0.283203125, 'recall': 0.5330882352941176, 'f1': 0.36989795918367346, 'number': 272}           | {'precision': 0.2857142857142857, 'recall': 0.4131147540983607, 'f1': 0.3378016085790885, 'number': 305}     | 0.2810            | 0.4042         | 0.3315     | 0.7331           |
-| 0.4764        | 10.0  | 40   | 0.7321          | {'precision': 0.21428571428571427, 'recall': 0.0625, 'f1': 0.09677419354838708, 'number': 96}                | {'precision': 0.302713987473904, 'recall': 0.5330882352941176, 'f1': 0.38615179760319573, 'number': 272}     | {'precision': 0.3036529680365297, 'recall': 0.4360655737704918, 'f1': 0.3580080753701211, 'number': 305}     | 0.3005            | 0.4220         | 0.3511     | 0.7443           |
-| 0.3805        | 11.0  | 44   | 0.7123          | {'precision': 0.22857142857142856, 'recall': 0.08333333333333333, 'f1': 0.12213740458015265, 'number': 96}   | {'precision': 0.3008474576271186, 'recall': 0.5220588235294118, 'f1': 0.3817204301075269, 'number': 272}     | {'precision': 0.3120728929384966, 'recall': 0.4491803278688525, 'f1': 0.3682795698924731, 'number': 305}     | 0.3034            | 0.4264         | 0.3545     | 0.7504           |
-| 0.3651        | 12.0  | 48   | 0.7054          | {'precision': 0.2, 'recall': 0.08333333333333333, 'f1': 0.11764705882352941, 'number': 96}                   | {'precision': 0.31947483588621445, 'recall': 0.5367647058823529, 'f1': 0.40054869684499317, 'number': 272}   | {'precision': 0.3194444444444444, 'recall': 0.4524590163934426, 'f1': 0.37449118046132973, 'number': 305}    | 0.3143            | 0.4339         | 0.3645     | 0.7532           |
-| 0.3562        | 13.0  | 52   | 0.7085          | {'precision': 0.22727272727272727, 'recall': 0.10416666666666667, 'f1': 0.14285714285714288, 'number': 96}   | {'precision': 0.3400900900900901, 'recall': 0.5551470588235294, 'f1': 0.42178770949720673, 'number': 272}    | {'precision': 0.33816425120772947, 'recall': 0.45901639344262296, 'f1': 0.3894297635605007, 'number': 305}   | 0.3337            | 0.4473         | 0.3822     | 0.7555           |
-| 0.3191        | 14.0  | 56   | 0.7046          | {'precision': 0.1836734693877551, 'recall': 0.09375, 'f1': 0.12413793103448278, 'number': 96}                | {'precision': 0.3393665158371041, 'recall': 0.5514705882352942, 'f1': 0.42016806722689076, 'number': 272}    | {'precision': 0.34057971014492755, 'recall': 0.46229508196721314, 'f1': 0.39221140472879, 'number': 305}     | 0.3315            | 0.4458         | 0.3802     | 0.7579           |
-| 0.3347        | 15.0  | 60   | 0.7010          | {'precision': 0.20833333333333334, 'recall': 0.10416666666666667, 'f1': 0.1388888888888889, 'number': 96}    | {'precision': 0.3401360544217687, 'recall': 0.5514705882352942, 'f1': 0.420757363253857, 'number': 272}      | {'precision': 0.3463414634146341, 'recall': 0.46557377049180326, 'f1': 0.39720279720279716, 'number': 305}   | 0.3359            | 0.4487         | 0.3842     | 0.7593           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                     | Nswer                                                                                                   | Uestion                                                                                                 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.1495        | 1.0   | 4    | 1.0698          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                | {'precision': 0.2, 'recall': 0.013333333333333334, 'f1': 0.025, 'number': 75}                           | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 76}                                              | 0.0909            | 0.0062         | 0.0116     | 0.4960           |
+| 0.9448        | 2.0   | 8    | 0.9517          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                | {'precision': 0.2, 'recall': 0.013333333333333334, 'f1': 0.025, 'number': 75}                           | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 76}                                              | 0.0909            | 0.0062         | 0.0116     | 0.5              |
+| 0.8403        | 3.0   | 12   | 0.7857          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}                                | {'precision': 0.4927536231884058, 'recall': 0.4533333333333333, 'f1': 0.4722222222222222, 'number': 75} | {'precision': 0.3684210526315789, 'recall': 0.3684210526315789, 'f1': 0.3684210526315789, 'number': 76} | 0.4189            | 0.3851         | 0.4013     | 0.6633           |
+| 0.6355        | 4.0   | 16   | 0.6129          | {'precision': 0.2857142857142857, 'recall': 0.2, 'f1': 0.23529411764705882, 'number': 10} | {'precision': 0.5353535353535354, 'recall': 0.7066666666666667, 'f1': 0.6091954022988506, 'number': 75} | {'precision': 0.5865384615384616, 'recall': 0.8026315789473685, 'f1': 0.6777777777777778, 'number': 76} | 0.5524            | 0.7205         | 0.6253     | 0.7802           |
+| 0.4708        | 5.0   | 20   | 0.4714          | {'precision': 0.5555555555555556, 'recall': 0.5, 'f1': 0.5263157894736842, 'number': 10}  | {'precision': 0.6853932584269663, 'recall': 0.8133333333333334, 'f1': 0.7439024390243902, 'number': 75} | {'precision': 0.6956521739130435, 'recall': 0.8421052631578947, 'f1': 0.761904761904762, 'number': 76}  | 0.6842            | 0.8075         | 0.7407     | 0.8387           |
+| 0.3531        | 6.0   | 24   | 0.3538          | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 10}                 | {'precision': 0.7471264367816092, 'recall': 0.8666666666666667, 'f1': 0.8024691358024691, 'number': 75} | {'precision': 0.7764705882352941, 'recall': 0.868421052631579, 'f1': 0.8198757763975155, 'number': 76}  | 0.7637            | 0.8634         | 0.8105     | 0.8831           |
+| 0.2414        | 7.0   | 28   | 0.2841          | {'precision': 0.9, 'recall': 0.9, 'f1': 0.9, 'number': 10}                                | {'precision': 0.7804878048780488, 'recall': 0.8533333333333334, 'f1': 0.8152866242038218, 'number': 75} | {'precision': 0.7804878048780488, 'recall': 0.8421052631578947, 'f1': 0.810126582278481, 'number': 76}  | 0.7874            | 0.8509         | 0.8179     | 0.9133           |
+| 0.2138        | 8.0   | 32   | 0.2437          | {'precision': 0.9, 'recall': 0.9, 'f1': 0.9, 'number': 10}                                | {'precision': 0.7875, 'recall': 0.84, 'f1': 0.8129032258064516, 'number': 75}                           | {'precision': 0.7682926829268293, 'recall': 0.8289473684210527, 'f1': 0.7974683544303798, 'number': 76} | 0.7849            | 0.8385         | 0.8108     | 0.9294           |
+| 0.2558        | 9.0   | 36   | 0.2162          | {'precision': 0.9, 'recall': 0.9, 'f1': 0.9, 'number': 10}                                | {'precision': 0.8, 'recall': 0.8533333333333334, 'f1': 0.8258064516129033, 'number': 75}                | {'precision': 0.8441558441558441, 'recall': 0.8552631578947368, 'f1': 0.8496732026143792, 'number': 76} | 0.8263            | 0.8571         | 0.8415     | 0.9375           |
+| 0.1247        | 10.0  | 40   | 0.2025          | {'precision': 0.9, 'recall': 0.9, 'f1': 0.9, 'number': 10}                                | {'precision': 0.8, 'recall': 0.8533333333333334, 'f1': 0.8258064516129033, 'number': 75}                | {'precision': 0.8441558441558441, 'recall': 0.8552631578947368, 'f1': 0.8496732026143792, 'number': 76} | 0.8263            | 0.8571         | 0.8415     | 0.9375           |
+| 0.1053        | 11.0  | 44   | 0.2047          | {'precision': 0.9, 'recall': 0.9, 'f1': 0.9, 'number': 10}                                | {'precision': 0.810126582278481, 'recall': 0.8533333333333334, 'f1': 0.8311688311688312, 'number': 75}  | {'precision': 0.8421052631578947, 'recall': 0.8421052631578947, 'f1': 0.8421052631578947, 'number': 76} | 0.8303            | 0.8509         | 0.8405     | 0.9375           |
+| 0.1101        | 12.0  | 48   | 0.2053          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                                | {'precision': 0.810126582278481, 'recall': 0.8533333333333334, 'f1': 0.8311688311688312, 'number': 75}  | {'precision': 0.8421052631578947, 'recall': 0.8421052631578947, 'f1': 0.8421052631578947, 'number': 76} | 0.8364            | 0.8571         | 0.8466     | 0.9395           |
+| 0.0894        | 13.0  | 52   | 0.2016          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                                | {'precision': 0.810126582278481, 'recall': 0.8533333333333334, 'f1': 0.8311688311688312, 'number': 75}  | {'precision': 0.8421052631578947, 'recall': 0.8421052631578947, 'f1': 0.8421052631578947, 'number': 76} | 0.8364            | 0.8571         | 0.8466     | 0.9395           |
+| 0.1657        | 14.0  | 56   | 0.1980          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                                | {'precision': 0.8354430379746836, 'recall': 0.88, 'f1': 0.8571428571428572, 'number': 75}               | {'precision': 0.8571428571428571, 'recall': 0.868421052631579, 'f1': 0.8627450980392157, 'number': 76}  | 0.8554            | 0.8820         | 0.8685     | 0.9435           |
+| 0.0884        | 15.0  | 60   | 0.1967          | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}                                | {'precision': 0.8354430379746836, 'recall': 0.88, 'f1': 0.8571428571428572, 'number': 75}               | {'precision': 0.8571428571428571, 'recall': 0.868421052631579, 'f1': 0.8627450980392157, 'number': 76}  | 0.8554            | 0.8820         | 0.8685     | 0.9435           |
 
 
 ### Framework versions
 
-- Transformers 4.48.3
-- Pytorch 2.5.1+cu124
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
 - Datasets 3.3.2
 - Tokenizers 0.21.0

logs/events.out.tfevents.1740646307.DESKTOP-HA84SVN.2492567.1

@@ -1,3 +1,3 @@
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-oid sha256:292953d8dfa61cc9a512198036f80b22500467433233a2f6d8b78119625eaddd
-size 15036
+oid sha256:a8cc63fe7328521f6db7903ed0dd58165ad2cfc5f9170f63cdd3411d4a04fe83
+size 16086

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bcff362ba3cdcd1581f6eeab30f6381743938b4cdfe2534065d1f832dd0513e7
+oid sha256:167280f93ce3a2d5893161136fea858fd5713a3930cde58a2dfdeb34d6650475
 size 450548984

tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 512
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "[PAD]"
+  },
   "added_tokens": [
     {
       "id": 0,

tokenizer_config.json

@@ -43,7 +43,7 @@
   },
   "additional_special_tokens": [],
   "apply_ocr": false,
-  "clean_up_tokenization_spaces": true,
+  "clean_up_tokenization_spaces": false,
   "cls_token": "[CLS]",
   "cls_token_box": [
     0,
@@ -55,11 +55,9 @@
   "do_lower_case": true,
   "extra_special_tokens": {},
   "mask_token": "[MASK]",
-  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
   "only_label_first_subword": true,
-  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
   "pad_token_box": [
     0,
@@ -68,8 +66,6 @@
     0
   ],
   "pad_token_label": -100,
-  "pad_token_type_id": 0,
-  "padding_side": "right",
   "processor_class": "LayoutLMv2Processor",
   "sep_token": "[SEP]",
   "sep_token_box": [
@@ -78,11 +74,8 @@
     1000,
     1000
   ],
-  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "LayoutLMv2Tokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }