End of training

README.md

@@ -16,14 +16,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 1.3725
-- Answer: {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809}
+- Loss: 1.3215
+- Answer: {'precision': 0.10096818810511757, 'recall': 0.09023485784919653, 'f1': 0.09530026109660573, 'number': 809}
 - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
-- Question: {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065}
-- Overall Precision: 0.2804
-- Overall Recall: 0.3126
-- Overall F1: 0.2956
-- Overall Accuracy: 0.5437
+- Question: {'precision': 0.3980815347721823, 'recall': 0.4676056338028169, 'f1': 0.43005181347150256, 'number': 1065}
+- Overall Precision: 0.2891
+- Overall Recall: 0.2865
+- Overall F1: 0.2878
+- Overall Accuracy: 0.5339
 
 ## Model description
 
@@ -52,28 +52,28 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                          | Header                                                      | Question                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8773        | 1.0   | 10   | 1.8489          | {'precision': 0.00547645125958379, 'recall': 0.006180469715698393, 'f1': 0.005807200929152149, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04874446085672083, 'recall': 0.030985915492957747, 'f1': 0.03788748564867968, 'number': 1065} | 0.0227            | 0.0191         | 0.0207     | 0.2819           |
-| 1.807         | 2.0   | 20   | 1.7831          | {'precision': 0.005925925925925926, 'recall': 0.004944375772558714, 'f1': 0.005390835579514824, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.06716417910447761, 'recall': 0.03380281690140845, 'f1': 0.04497189256714553, 'number': 1065}  | 0.0327            | 0.0201         | 0.0249     | 0.2996           |
-| 1.7516        | 3.0   | 30   | 1.7272          | {'precision': 0.0071633237822349575, 'recall': 0.006180469715698393, 'f1': 0.006635700066357001, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.10175438596491228, 'recall': 0.054460093896713614, 'f1': 0.0709480122324159, 'number': 1065}  | 0.0496            | 0.0316         | 0.0386     | 0.3189           |
-| 1.7057        | 4.0   | 40   | 1.6785          | {'precision': 0.012626262626262626, 'recall': 0.012360939431396786, 'f1': 0.012492192379762648, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.16886930983847284, 'recall': 0.107981220657277, 'f1': 0.13172966781214204, 'number': 1065}    | 0.0849            | 0.0627         | 0.0721     | 0.3426           |
-| 1.6571        | 5.0   | 50   | 1.6336          | {'precision': 0.016286644951140065, 'recall': 0.018541409147095178, 'f1': 0.017341040462427744, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2211764705882353, 'recall': 0.17652582159624414, 'f1': 0.19634464751958225, 'number': 1065}   | 0.1146            | 0.1019         | 0.1079     | 0.3714           |
-| 1.6219        | 6.0   | 60   | 1.5894          | {'precision': 0.03238095238095238, 'recall': 0.042027194066749075, 'f1': 0.036578805809575045, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26129666011787817, 'recall': 0.24976525821596243, 'f1': 0.2554008641382621, 'number': 1065}   | 0.1451            | 0.1505         | 0.1477     | 0.4028           |
-| 1.5748        | 7.0   | 70   | 1.5484          | {'precision': 0.03796296296296296, 'recall': 0.05067985166872682, 'f1': 0.04340921122286924, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.28073394495412846, 'recall': 0.28732394366197184, 'f1': 0.28399071925754066, 'number': 1065}  | 0.1599            | 0.1741         | 0.1667     | 0.4319           |
-| 1.5387        | 8.0   | 80   | 1.5098          | {'precision': 0.044036697247706424, 'recall': 0.059332509270704575, 'f1': 0.05055292259083728, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.30583333333333335, 'recall': 0.34460093896713617, 'f1': 0.3240618101545254, 'number': 1065}   | 0.1812            | 0.2082         | 0.1938     | 0.4623           |
-| 1.5004        | 9.0   | 90   | 1.4753          | {'precision': 0.05149812734082397, 'recall': 0.06798516687268233, 'f1': 0.05860415556739478, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3378812199036918, 'recall': 0.39530516431924884, 'f1': 0.36434443963652097, 'number': 1065}   | 0.2057            | 0.2388         | 0.2210     | 0.4887           |
-| 1.4659        | 10.0  | 100  | 1.4462          | {'precision': 0.058823529411764705, 'recall': 0.0754017305315204, 'f1': 0.06608884073672806, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3586530931871574, 'recall': 0.4300469483568075, 'f1': 0.39111870196413323, 'number': 1065}    | 0.2243            | 0.2604         | 0.2410     | 0.5046           |
-| 1.4314        | 11.0  | 110  | 1.4207          | {'precision': 0.06769230769230769, 'recall': 0.0815822002472188, 'f1': 0.07399103139013452, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38271604938271603, 'recall': 0.46572769953051646, 'f1': 0.42016094875052945, 'number': 1065}  | 0.2475            | 0.2820         | 0.2636     | 0.5184           |
-| 1.4242        | 12.0  | 120  | 1.4003          | {'precision': 0.07203389830508475, 'recall': 0.08405438813349815, 'f1': 0.0775812892184826, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40076628352490423, 'recall': 0.49107981220657276, 'f1': 0.4413502109704641, 'number': 1065}   | 0.2628            | 0.2965         | 0.2786     | 0.5273           |
-| 1.3939        | 13.0  | 130  | 1.3855          | {'precision': 0.07792207792207792, 'recall': 0.08899876390605686, 'f1': 0.0830929024812464, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40953822861468586, 'recall': 0.507981220657277, 'f1': 0.45347862531433364, 'number': 1065}    | 0.2731            | 0.3076         | 0.2893     | 0.5367           |
-| 1.3837        | 14.0  | 140  | 1.3764          | {'precision': 0.08021978021978023, 'recall': 0.09023485784919653, 'f1': 0.08493310063990692, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41635124905374715, 'recall': 0.5164319248826291, 'f1': 0.4610226320201173, 'number': 1065}    | 0.2792            | 0.3126         | 0.2950     | 0.5410           |
-| 1.3603        | 15.0  | 150  | 1.3725          | {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065}    | 0.2804            | 0.3126         | 0.2956     | 0.5437           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                       | Question                                                                                                      | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.9471        | 1.0   | 10   | 1.8844          | {'precision': 0.022006141248720572, 'recall': 0.05315203955500618, 'f1': 0.031125588128845458, 'number': 809} | {'precision': 0.00702576112412178, 'recall': 0.05042016806722689, 'f1': 0.012332990750256937, 'number': 119} | {'precision': 0.054583995760466346, 'recall': 0.09671361502347418, 'f1': 0.06978319783197831, 'number': 1065} | 0.0324            | 0.0763         | 0.0455     | 0.2491           |
+| 1.8584        | 2.0   | 20   | 1.8099          | {'precision': 0.018408941485864562, 'recall': 0.034610630407911, 'f1': 0.024034334763948496, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.08241758241758242, 'recall': 0.11267605633802817, 'f1': 0.09520031733439112, 'number': 1065}  | 0.0469            | 0.0743         | 0.0575     | 0.3139           |
+| 1.7841        | 3.0   | 30   | 1.7444          | {'precision': 0.02190395956192081, 'recall': 0.032138442521631644, 'f1': 0.026052104208416832, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.10752688172043011, 'recall': 0.12206572769953052, 'f1': 0.11433597185576078, 'number': 1065}  | 0.0645            | 0.0783         | 0.0707     | 0.3426           |
+| 1.7255        | 4.0   | 40   | 1.6851          | {'precision': 0.026865671641791045, 'recall': 0.03337453646477132, 'f1': 0.029768467475192944, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.15547024952015356, 'recall': 0.15211267605633802, 'f1': 0.1537731371618415, 'number': 1065}   | 0.0922            | 0.0948         | 0.0935     | 0.3647           |
+| 1.6607        | 5.0   | 50   | 1.6287          | {'precision': 0.036458333333333336, 'recall': 0.04326328800988875, 'f1': 0.03957037874505371, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.2018348623853211, 'recall': 0.20657276995305165, 'f1': 0.20417633410672859, 'number': 1065}   | 0.1244            | 0.1279         | 0.1261     | 0.3943           |
+| 1.6127        | 6.0   | 60   | 1.5738          | {'precision': 0.045, 'recall': 0.05562422744128554, 'f1': 0.04975124378109452, 'number': 809}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.24034334763948498, 'recall': 0.26291079812206575, 'f1': 0.25112107623318386, 'number': 1065}  | 0.1501            | 0.1631         | 0.1563     | 0.4234           |
+| 1.5582        | 7.0   | 70   | 1.5242          | {'precision': 0.05465587044534413, 'recall': 0.06674907292954264, 'f1': 0.060100166944908176, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.26282051282051283, 'recall': 0.307981220657277, 'f1': 0.2836143536532642, 'number': 1065}     | 0.1708            | 0.1917         | 0.1807     | 0.4483           |
+| 1.5135        | 8.0   | 80   | 1.4789          | {'precision': 0.05976520811099253, 'recall': 0.069221260815822, 'f1': 0.06414662084765177, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.29073482428115016, 'recall': 0.34178403755868547, 'f1': 0.31419939577039274, 'number': 1065}  | 0.1919            | 0.2107         | 0.2009     | 0.4679           |
+| 1.4676        | 9.0   | 90   | 1.4380          | {'precision': 0.06818181818181818, 'recall': 0.07416563658838071, 'f1': 0.07104795737122557, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3149480415667466, 'recall': 0.3699530516431925, 'f1': 0.34024179620034545, 'number': 1065}    | 0.2130            | 0.2278         | 0.2202     | 0.4851           |
+| 1.4233        | 10.0  | 100  | 1.4035          | {'precision': 0.07664670658682635, 'recall': 0.07911001236093942, 'f1': 0.0778588807785888, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3413848631239936, 'recall': 0.39812206572769954, 'f1': 0.3675769397485913, 'number': 1065}    | 0.2350            | 0.2449         | 0.2398     | 0.4988           |
+| 1.3864        | 11.0  | 110  | 1.3744          | {'precision': 0.0810126582278481, 'recall': 0.07911001236093942, 'f1': 0.08005003126954345, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3583535108958838, 'recall': 0.4169014084507042, 'f1': 0.38541666666666663, 'number': 1065}    | 0.2504            | 0.2549         | 0.2526     | 0.5113           |
+| 1.3746        | 12.0  | 120  | 1.3519          | {'precision': 0.0870712401055409, 'recall': 0.0815822002472188, 'f1': 0.08423739629865987, 'number': 809}     | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3806818181818182, 'recall': 0.4403755868544601, 'f1': 0.40835872877666524, 'number': 1065}    | 0.2688            | 0.2684         | 0.2686     | 0.5175           |
+| 1.3417        | 13.0  | 130  | 1.3352          | {'precision': 0.09568733153638814, 'recall': 0.08776266996291718, 'f1': 0.09155383623468731, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.39403706688154716, 'recall': 0.4591549295774648, 'f1': 0.4241110147441457, 'number': 1065}    | 0.2824            | 0.2810         | 0.2817     | 0.5272           |
+| 1.3318        | 14.0  | 140  | 1.3254          | {'precision': 0.09686221009549795, 'recall': 0.08776266996291718, 'f1': 0.09208819714656291, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3942307692307692, 'recall': 0.4619718309859155, 'f1': 0.4254215304798963, 'number': 1065}     | 0.2841            | 0.2825         | 0.2833     | 0.5314           |
+| 1.3086        | 15.0  | 150  | 1.3215          | {'precision': 0.10096818810511757, 'recall': 0.09023485784919653, 'f1': 0.09530026109660573, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3980815347721823, 'recall': 0.4676056338028169, 'f1': 0.43005181347150256, 'number': 1065}    | 0.2891            | 0.2865         | 0.2878     | 0.5339           |
 
 
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.0
+- Datasets 2.14.4
 - Tokenizers 0.13.3

logs/events.out.tfevents.1691736207.4d17f9ce90b7.2427.0

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