End of training

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: mit
-base_model: microsoft/layoutlm-base-uncased
+base_model: pabloma09/layoutlm-funsd
 tags:
 - generated_from_trainer
 model-index:
@@ -14,16 +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 None dataset.
+This model is a fine-tuned version of [pabloma09/layoutlm-funsd](https://huggingface.co/pabloma09/layoutlm-funsd) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8302
-- Eader: {'precision': 0.4444444444444444, 'recall': 0.25, 'f1': 0.32, 'number': 32}
-- Nswer: {'precision': 0.45569620253164556, 'recall': 0.5142857142857142, 'f1': 0.4832214765100671, 'number': 70}
-- Uestion: {'precision': 0.4868421052631579, 'recall': 0.47435897435897434, 'f1': 0.48051948051948057, 'number': 78}
-- Overall Precision: 0.4682
+- Loss: 0.9937
+- Eader: {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}
+- Nswer: {'precision': 0.5135135135135135, 'recall': 0.5428571428571428, 'f1': 0.5277777777777778, 'number': 70}
+- Uestion: {'precision': 0.4931506849315068, 'recall': 0.46153846153846156, 'f1': 0.4768211920529801, 'number': 78}
+- Overall Precision: 0.4850
 - Overall Recall: 0.45
-- Overall F1: 0.4589
-- Overall Accuracy: 0.7758
+- Overall F1: 0.4669
+- Overall Accuracy: 0.8029
 
 ## Model description
 
@@ -53,23 +53,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                          | Nswer                                                                                                       | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.2754        | 1.0   | 13   | 1.0702          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                     | {'precision': 0.046413502109704644, 'recall': 0.15714285714285714, 'f1': 0.07166123778501629, 'number': 70} | {'precision': 0.05063291139240506, 'recall': 0.15384615384615385, 'f1': 0.0761904761904762, 'number': 78}  | 0.0485            | 0.1278         | 0.0703     | 0.5865           |
-| 0.8597        | 2.0   | 26   | 0.7226          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                     | {'precision': 0.30097087378640774, 'recall': 0.44285714285714284, 'f1': 0.3583815028901734, 'number': 70}   | {'precision': 0.2647058823529412, 'recall': 0.34615384615384615, 'f1': 0.29999999999999993, 'number': 78}  | 0.2723            | 0.3222         | 0.2952     | 0.7685           |
-| 0.624         | 3.0   | 39   | 0.6206          | {'precision': 0.1875, 'recall': 0.09375, 'f1': 0.125, 'number': 32}                            | {'precision': 0.36470588235294116, 'recall': 0.44285714285714284, 'f1': 0.3999999999999999, 'number': 70}   | {'precision': 0.4533333333333333, 'recall': 0.4358974358974359, 'f1': 0.44444444444444436, 'number': 78}   | 0.3864            | 0.3778         | 0.3820     | 0.8065           |
-| 0.4821        | 4.0   | 52   | 0.6513          | {'precision': 0.3125, 'recall': 0.15625, 'f1': 0.20833333333333334, 'number': 32}              | {'precision': 0.3707865168539326, 'recall': 0.4714285714285714, 'f1': 0.41509433962264153, 'number': 70}    | {'precision': 0.4050632911392405, 'recall': 0.41025641025641024, 'f1': 0.4076433121019108, 'number': 78}   | 0.3804            | 0.3889         | 0.3846     | 0.7637           |
-| 0.371         | 5.0   | 65   | 0.7466          | {'precision': 0.36363636363636365, 'recall': 0.25, 'f1': 0.2962962962962963, 'number': 32}     | {'precision': 0.4069767441860465, 'recall': 0.5, 'f1': 0.4487179487179487, 'number': 70}                    | {'precision': 0.40476190476190477, 'recall': 0.4358974358974359, 'f1': 0.4197530864197531, 'number': 78}   | 0.4010            | 0.4278         | 0.4140     | 0.7131           |
-| 0.3068        | 6.0   | 78   | 0.6943          | {'precision': 0.4444444444444444, 'recall': 0.25, 'f1': 0.32, 'number': 32}                    | {'precision': 0.42857142857142855, 'recall': 0.5142857142857142, 'f1': 0.4675324675324675, 'number': 70}    | {'precision': 0.44871794871794873, 'recall': 0.44871794871794873, 'f1': 0.44871794871794873, 'number': 78} | 0.4389            | 0.4389         | 0.4389     | 0.7498           |
-| 0.2523        | 7.0   | 91   | 0.7129          | {'precision': 0.4117647058823529, 'recall': 0.21875, 'f1': 0.2857142857142857, 'number': 32}   | {'precision': 0.3950617283950617, 'recall': 0.45714285714285713, 'f1': 0.423841059602649, 'number': 70}     | {'precision': 0.4788732394366197, 'recall': 0.4358974358974359, 'f1': 0.45637583892617445, 'number': 78}   | 0.4320            | 0.4056         | 0.4183     | 0.7728           |
-| 0.2073        | 8.0   | 104  | 0.8039          | {'precision': 0.4, 'recall': 0.25, 'f1': 0.3076923076923077, 'number': 32}                     | {'precision': 0.546875, 'recall': 0.5, 'f1': 0.5223880597014925, 'number': 70}                              | {'precision': 0.4864864864864865, 'recall': 0.46153846153846156, 'f1': 0.47368421052631576, 'number': 78}  | 0.5               | 0.4389         | 0.4675     | 0.7426           |
-| 0.1673        | 9.0   | 117  | 0.7874          | {'precision': 0.3157894736842105, 'recall': 0.1875, 'f1': 0.23529411764705882, 'number': 32}   | {'precision': 0.4583333333333333, 'recall': 0.4714285714285714, 'f1': 0.46478873239436613, 'number': 70}    | {'precision': 0.4722222222222222, 'recall': 0.4358974358974359, 'f1': 0.45333333333333337, 'number': 78}   | 0.4479            | 0.4056         | 0.4257     | 0.7559           |
-| 0.1556        | 10.0  | 130  | 0.7440          | {'precision': 0.42105263157894735, 'recall': 0.25, 'f1': 0.3137254901960784, 'number': 32}     | {'precision': 0.4931506849315068, 'recall': 0.5142857142857142, 'f1': 0.5034965034965034, 'number': 70}     | {'precision': 0.4794520547945205, 'recall': 0.44871794871794873, 'f1': 0.4635761589403974, 'number': 78}   | 0.4788            | 0.4389         | 0.4580     | 0.7860           |
-| 0.1304        | 11.0  | 143  | 0.8451          | {'precision': 0.42105263157894735, 'recall': 0.25, 'f1': 0.3137254901960784, 'number': 32}     | {'precision': 0.45, 'recall': 0.5142857142857142, 'f1': 0.48, 'number': 70}                                 | {'precision': 0.45, 'recall': 0.46153846153846156, 'f1': 0.45569620253164556, 'number': 78}                | 0.4469            | 0.4444         | 0.4457     | 0.7655           |
-| 0.1231        | 12.0  | 156  | 0.9197          | {'precision': 0.375, 'recall': 0.28125, 'f1': 0.32142857142857145, 'number': 32}               | {'precision': 0.49295774647887325, 'recall': 0.5, 'f1': 0.49645390070921985, 'number': 70}                  | {'precision': 0.44871794871794873, 'recall': 0.44871794871794873, 'f1': 0.44871794871794873, 'number': 78} | 0.4566            | 0.4389         | 0.4476     | 0.7432           |
-| 0.1062        | 13.0  | 169  | 0.8122          | {'precision': 0.47368421052631576, 'recall': 0.28125, 'f1': 0.35294117647058826, 'number': 32} | {'precision': 0.47368421052631576, 'recall': 0.5142857142857142, 'f1': 0.4931506849315068, 'number': 70}    | {'precision': 0.5135135135135135, 'recall': 0.48717948717948717, 'f1': 0.5, 'number': 78}                  | 0.4911            | 0.4611         | 0.4756     | 0.7836           |
-| 0.1072        | 14.0  | 182  | 0.8369          | {'precision': 0.391304347826087, 'recall': 0.28125, 'f1': 0.3272727272727273, 'number': 32}    | {'precision': 0.43037974683544306, 'recall': 0.4857142857142857, 'f1': 0.4563758389261745, 'number': 70}    | {'precision': 0.46153846153846156, 'recall': 0.46153846153846156, 'f1': 0.46153846153846156, 'number': 78} | 0.4389            | 0.4389         | 0.4389     | 0.7746           |
-| 0.1028        | 15.0  | 195  | 0.8302          | {'precision': 0.4444444444444444, 'recall': 0.25, 'f1': 0.32, 'number': 32}                    | {'precision': 0.45569620253164556, 'recall': 0.5142857142857142, 'f1': 0.4832214765100671, 'number': 70}    | {'precision': 0.4868421052631579, 'recall': 0.47435897435897434, 'f1': 0.48051948051948057, 'number': 78}  | 0.4682            | 0.45           | 0.4589     | 0.7758           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                         | Nswer                                                                                                     | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1124        | 1.0   | 13   | 0.8542          | {'precision': 0.32, 'recall': 0.25, 'f1': 0.2807017543859649, 'number': 32}                   | {'precision': 0.4647887323943662, 'recall': 0.4714285714285714, 'f1': 0.46808510638297873, 'number': 70}  | {'precision': 0.4861111111111111, 'recall': 0.44871794871794873, 'f1': 0.4666666666666667, 'number': 78}   | 0.4524            | 0.4222         | 0.4368     | 0.7848           |
+| 0.1002        | 2.0   | 26   | 0.8579          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.4146341463414634, 'recall': 0.4857142857142857, 'f1': 0.4473684210526316, 'number': 70}   | {'precision': 0.4125, 'recall': 0.4230769230769231, 'f1': 0.4177215189873418, 'number': 78}                | 0.4022            | 0.4111         | 0.4066     | 0.7559           |
+| 0.0905        | 3.0   | 39   | 0.7874          | {'precision': 0.34782608695652173, 'recall': 0.25, 'f1': 0.2909090909090909, 'number': 32}    | {'precision': 0.45, 'recall': 0.5142857142857142, 'f1': 0.48, 'number': 70}                               | {'precision': 0.5, 'recall': 0.48717948717948717, 'f1': 0.49350649350649345, 'number': 78}                 | 0.4581            | 0.4556         | 0.4568     | 0.7987           |
+| 0.0743        | 4.0   | 52   | 0.9167          | {'precision': 0.4, 'recall': 0.1875, 'f1': 0.25531914893617025, 'number': 32}                 | {'precision': 0.48, 'recall': 0.5142857142857142, 'f1': 0.496551724137931, 'number': 70}                  | {'precision': 0.5333333333333333, 'recall': 0.5128205128205128, 'f1': 0.5228758169934641, 'number': 78}    | 0.4970            | 0.4556         | 0.4754     | 0.7926           |
+| 0.0534        | 5.0   | 65   | 0.9266          | {'precision': 0.45, 'recall': 0.28125, 'f1': 0.34615384615384615, 'number': 32}               | {'precision': 0.43373493975903615, 'recall': 0.5142857142857142, 'f1': 0.47058823529411764, 'number': 70} | {'precision': 0.4805194805194805, 'recall': 0.47435897435897434, 'f1': 0.47741935483870973, 'number': 78}  | 0.4556            | 0.4556         | 0.4556     | 0.7800           |
+| 0.0465        | 6.0   | 78   | 1.0600          | {'precision': 0.3, 'recall': 0.1875, 'f1': 0.23076923076923075, 'number': 32}                 | {'precision': 0.4864864864864865, 'recall': 0.5142857142857142, 'f1': 0.5, 'number': 70}                  | {'precision': 0.4666666666666667, 'recall': 0.44871794871794873, 'f1': 0.45751633986928103, 'number': 78}  | 0.4556            | 0.4278         | 0.4413     | 0.7197           |
+| 0.0388        | 7.0   | 91   | 0.9172          | {'precision': 0.4444444444444444, 'recall': 0.25, 'f1': 0.32, 'number': 32}                   | {'precision': 0.5, 'recall': 0.5571428571428572, 'f1': 0.5270270270270271, 'number': 70}                  | {'precision': 0.4625, 'recall': 0.47435897435897434, 'f1': 0.46835443037974683, 'number': 78}              | 0.4773            | 0.4667         | 0.4719     | 0.7740           |
+| 0.0333        | 8.0   | 104  | 0.9758          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.4157303370786517, 'recall': 0.5285714285714286, 'f1': 0.46540880503144655, 'number': 70}  | {'precision': 0.4186046511627907, 'recall': 0.46153846153846156, 'f1': 0.4390243902439025, 'number': 78}   | 0.4124            | 0.4444         | 0.4278     | 0.7770           |
+| 0.0269        | 9.0   | 117  | 0.9879          | {'precision': 0.2916666666666667, 'recall': 0.21875, 'f1': 0.25, 'number': 32}                | {'precision': 0.4, 'recall': 0.5142857142857142, 'f1': 0.45, 'number': 70}                                | {'precision': 0.3888888888888889, 'recall': 0.44871794871794873, 'f1': 0.41666666666666663, 'number': 78}  | 0.3824            | 0.4333         | 0.4062     | 0.7794           |
+| 0.0255        | 10.0  | 130  | 0.9909          | {'precision': 0.3, 'recall': 0.1875, 'f1': 0.23076923076923075, 'number': 32}                 | {'precision': 0.43902439024390244, 'recall': 0.5142857142857142, 'f1': 0.4736842105263158, 'number': 70}  | {'precision': 0.4358974358974359, 'recall': 0.4358974358974359, 'f1': 0.4358974358974359, 'number': 78}    | 0.4222            | 0.4222         | 0.4222     | 0.7914           |
+| 0.0217        | 11.0  | 143  | 0.9914          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5066666666666667, 'recall': 0.5428571428571428, 'f1': 0.5241379310344827, 'number': 70}   | {'precision': 0.45121951219512196, 'recall': 0.47435897435897434, 'f1': 0.46249999999999997, 'number': 78} | 0.4633            | 0.4556         | 0.4594     | 0.7951           |
+| 0.024         | 12.0  | 156  | 0.9999          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.5064935064935064, 'recall': 0.5571428571428572, 'f1': 0.5306122448979592, 'number': 70}   | {'precision': 0.475, 'recall': 0.48717948717948717, 'f1': 0.4810126582278481, 'number': 78}                | 0.4773            | 0.4667         | 0.4719     | 0.7896           |
+| 0.0197        | 13.0  | 169  | 0.9820          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.49333333333333335, 'recall': 0.5285714285714286, 'f1': 0.5103448275862069, 'number': 70}  | {'precision': 0.5205479452054794, 'recall': 0.48717948717948717, 'f1': 0.5033112582781456, 'number': 78}   | 0.4852            | 0.4556         | 0.4699     | 0.8053           |
+| 0.022         | 14.0  | 182  | 0.9912          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5, 'recall': 0.5428571428571428, 'f1': 0.5205479452054795, 'number': 70}                  | {'precision': 0.4864864864864865, 'recall': 0.46153846153846156, 'f1': 0.47368421052631576, 'number': 78}  | 0.4765            | 0.45           | 0.4629     | 0.8023           |
+| 0.0215        | 15.0  | 195  | 0.9937          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5135135135135135, 'recall': 0.5428571428571428, 'f1': 0.5277777777777778, 'number': 70}   | {'precision': 0.4931506849315068, 'recall': 0.46153846153846156, 'f1': 0.4768211920529801, 'number': 78}   | 0.4850            | 0.45           | 0.4669     | 0.8029           |
 
 
 ### Framework versions

logs/events.out.tfevents.1741096961.DESKTOP-HA84SVN.3577307.1

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a90cab19ffd73fd2a530aca3be29dff53c19694bf253f949480be65e727bd8d
-size 15134
+oid sha256:e0db03db479a12996a6d6e5ce6bd0d80d4072b29879acca018b07ca91bd2f55f
+size 16203

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5d16e80a23aad2db3a90e34ba14cb56bf75566a422fbb69c69a89f0e9250eabe
+oid sha256:c7ef94d4ffdda985b490413d5c9360b9f5c2aaa1a3150b167cd21c54d7f5860e
 size 450548984

tokenizer.json

@@ -1,21 +1,7 @@
 {
   "version": "1.0",
-  "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]"
-  },
+  "truncation": null,
+  "padding": null,
   "added_tokens": [
     {
       "id": 0,

tokenizer_config.json

@@ -55,9 +55,11 @@
   "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,
@@ -66,6 +68,8 @@
     0
   ],
   "pad_token_label": -100,
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "processor_class": "LayoutLMv2Processor",
   "sep_token": "[SEP]",
   "sep_token_box": [
@@ -74,8 +78,11 @@
     1000,
     1000
   ],
+  "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "LayoutLMv2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }