End of training

README.md

@@ -17,14 +17,14 @@ 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: 1.1153
-- Answer: {'precision': 0.3834652594547054, 'recall': 0.5389369592088998, 'f1': 0.44809866392600206, 'number': 809}
-- Header: {'precision': 0.29347826086956524, 'recall': 0.226890756302521, 'f1': 0.2559241706161137, 'number': 119}
-- Question: {'precision': 0.5310457516339869, 'recall': 0.6103286384976526, 'f1': 0.5679335954565312, 'number': 1065}
-- Overall Precision: 0.4537
-- Overall Recall: 0.5585
-- Overall F1: 0.5007
-- Overall Accuracy: 0.6247
+- Loss: 1.1050
+- Answer: {'precision': 0.37133808392715756, 'recall': 0.5797280593325093, 'f1': 0.45270270270270274, 'number': 809}
+- Header: {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}
+- Question: {'precision': 0.49682539682539684, 'recall': 0.5877934272300469, 'f1': 0.538494623655914, 'number': 1065}
+- Overall Precision: 0.4307
+- Overall Recall: 0.5630
+- Overall F1: 0.4880
+- Overall Accuracy: 0.6093
 
 ## Model description
 
@@ -54,23 +54,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7836        | 1.0   | 10   | 1.5583          | {'precision': 0.03470919324577861, 'recall': 0.04573547589616811, 'f1': 0.039466666666666664, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                    | {'precision': 0.1947049089906233, 'recall': 0.3314553990610329, 'f1': 0.24530924252953445, 'number': 1065} | 0.1355            | 0.1957         | 0.1601     | 0.3600           |
-| 1.4728        | 2.0   | 20   | 1.3281          | {'precision': 0.1966216216216216, 'recall': 0.35970333745364647, 'f1': 0.25425950196592395, 'number': 809}   | {'precision': 0.017543859649122806, 'recall': 0.008403361344537815, 'f1': 0.011363636363636362, 'number': 119} | {'precision': 0.2851758793969849, 'recall': 0.4262910798122066, 'f1': 0.34173880316146027, 'number': 1065} | 0.2384            | 0.3743         | 0.2913     | 0.4276           |
-| 1.2767        | 3.0   | 30   | 1.1812          | {'precision': 0.24393624393624394, 'recall': 0.43510506798516685, 'f1': 0.3126110124333925, 'number': 809}   | {'precision': 0.21052631578947367, 'recall': 0.16806722689075632, 'f1': 0.1869158878504673, 'number': 119}     | {'precision': 0.36664584634603375, 'recall': 0.5511737089201878, 'f1': 0.4403600900225056, 'number': 1065} | 0.3055            | 0.4812         | 0.3737     | 0.4924           |
-| 1.1394        | 4.0   | 40   | 1.1120          | {'precision': 0.28407350689127103, 'recall': 0.45859085290482077, 'f1': 0.35082742316784865, 'number': 809}  | {'precision': 0.24691358024691357, 'recall': 0.16806722689075632, 'f1': 0.2, 'number': 119}                    | {'precision': 0.47240802675585286, 'recall': 0.5305164319248826, 'f1': 0.4997788589119858, 'number': 1065} | 0.3701            | 0.4797         | 0.4178     | 0.5608           |
-| 1.0619        | 5.0   | 50   | 1.1166          | {'precision': 0.29784454604833444, 'recall': 0.5636588380716935, 'f1': 0.38974358974358975, 'number': 809}   | {'precision': 0.30303030303030304, 'recall': 0.16806722689075632, 'f1': 0.21621621621621626, 'number': 119}    | {'precision': 0.4780621572212066, 'recall': 0.49107981220657276, 'f1': 0.4844835572024085, 'number': 1065} | 0.3712            | 0.5013         | 0.4266     | 0.5636           |
-| 0.9741        | 6.0   | 60   | 1.0751          | {'precision': 0.33383010432190763, 'recall': 0.553770086526576, 'f1': 0.4165504416550442, 'number': 809}     | {'precision': 0.2676056338028169, 'recall': 0.15966386554621848, 'f1': 0.19999999999999998, 'number': 119}     | {'precision': 0.5021570319240725, 'recall': 0.5464788732394367, 'f1': 0.5233812949640289, 'number': 1065}  | 0.4079            | 0.5263         | 0.4596     | 0.5844           |
-| 0.907         | 7.0   | 70   | 1.0587          | {'precision': 0.3643835616438356, 'recall': 0.4932014833127318, 'f1': 0.41911764705882354, 'number': 809}    | {'precision': 0.2125, 'recall': 0.14285714285714285, 'f1': 0.1708542713567839, 'number': 119}                  | {'precision': 0.5027624309392266, 'recall': 0.5981220657276995, 'f1': 0.5463121783876502, 'number': 1065}  | 0.4312            | 0.5283         | 0.4749     | 0.6038           |
-| 0.8469        | 8.0   | 80   | 1.1514          | {'precision': 0.365814696485623, 'recall': 0.5661310259579728, 'f1': 0.4444444444444444, 'number': 809}      | {'precision': 0.25, 'recall': 0.16806722689075632, 'f1': 0.20100502512562815, 'number': 119}                   | {'precision': 0.5555555555555556, 'recall': 0.4788732394366197, 'f1': 0.5143721633888049, 'number': 1065}  | 0.4391            | 0.4957         | 0.4657     | 0.5893           |
-| 0.7877        | 9.0   | 90   | 1.0944          | {'precision': 0.37608318890814557, 'recall': 0.5364647713226205, 'f1': 0.44218033622007136, 'number': 809}   | {'precision': 0.2558139534883721, 'recall': 0.18487394957983194, 'f1': 0.21463414634146344, 'number': 119}     | {'precision': 0.531934306569343, 'recall': 0.5474178403755868, 'f1': 0.5395650161962054, 'number': 1065}   | 0.4448            | 0.5213         | 0.4800     | 0.6101           |
-| 0.7464        | 10.0  | 100  | 1.0861          | {'precision': 0.3794642857142857, 'recall': 0.5253399258343634, 'f1': 0.4406428201140487, 'number': 809}     | {'precision': 0.24444444444444444, 'recall': 0.18487394957983194, 'f1': 0.21052631578947367, 'number': 119}    | {'precision': 0.5038699690402477, 'recall': 0.6112676056338028, 'f1': 0.5523971149766653, 'number': 1065}  | 0.4388            | 0.5509         | 0.4885     | 0.6188           |
-| 0.7109        | 11.0  | 110  | 1.0985          | {'precision': 0.3770491803278688, 'recall': 0.5401730531520396, 'f1': 0.44410569105691056, 'number': 809}    | {'precision': 0.30120481927710846, 'recall': 0.21008403361344538, 'f1': 0.24752475247524758, 'number': 119}    | {'precision': 0.5317725752508361, 'recall': 0.5971830985915493, 'f1': 0.5625829279080052, 'number': 1065}  | 0.4504            | 0.5509         | 0.4956     | 0.6204           |
-| 0.6833        | 12.0  | 120  | 1.1252          | {'precision': 0.380327868852459, 'recall': 0.5735475896168108, 'f1': 0.45736816165598815, 'number': 809}     | {'precision': 0.2967032967032967, 'recall': 0.226890756302521, 'f1': 0.2571428571428572, 'number': 119}        | {'precision': 0.5510018214936248, 'recall': 0.568075117370892, 'f1': 0.559408229311142, 'number': 1065}    | 0.4550            | 0.5499         | 0.4980     | 0.6213           |
-| 0.6591        | 13.0  | 130  | 1.1009          | {'precision': 0.38546458141674333, 'recall': 0.5179233621755254, 'f1': 0.4419831223628692, 'number': 809}    | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119}     | {'precision': 0.5226400613967767, 'recall': 0.6394366197183099, 'f1': 0.575168918918919, 'number': 1065}   | 0.4520            | 0.5650         | 0.5022     | 0.6212           |
-| 0.639         | 14.0  | 140  | 1.1190          | {'precision': 0.3828125, 'recall': 0.484548825710754, 'f1': 0.42771412984178947, 'number': 809}              | {'precision': 0.2755102040816326, 'recall': 0.226890756302521, 'f1': 0.2488479262672811, 'number': 119}        | {'precision': 0.5295527156549521, 'recall': 0.6225352112676056, 'f1': 0.5722917565817868, 'number': 1065}  | 0.4558            | 0.5429         | 0.4955     | 0.6197           |
-| 0.6544        | 15.0  | 150  | 1.1153          | {'precision': 0.3834652594547054, 'recall': 0.5389369592088998, 'f1': 0.44809866392600206, 'number': 809}    | {'precision': 0.29347826086956524, 'recall': 0.226890756302521, 'f1': 0.2559241706161137, 'number': 119}       | {'precision': 0.5310457516339869, 'recall': 0.6103286384976526, 'f1': 0.5679335954565312, 'number': 1065}  | 0.4537            | 0.5585         | 0.5007     | 0.6247           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8038        | 1.0   | 10   | 1.5073          | {'precision': 0.06441476826394343, 'recall': 0.10135970333745364, 'f1': 0.07877041306436118, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24326241134751772, 'recall': 0.3220657276995305, 'f1': 0.2771717171717171, 'number': 1065}  | 0.1584            | 0.2132         | 0.1818     | 0.3843           |
+| 1.4521        | 2.0   | 20   | 1.3396          | {'precision': 0.20421753607103219, 'recall': 0.45488257107540175, 'f1': 0.28188433550363845, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2649350649350649, 'recall': 0.38309859154929576, 'f1': 0.31324376199616116, 'number': 1065} | 0.2321            | 0.3894         | 0.2909     | 0.4184           |
+| 1.278         | 3.0   | 30   | 1.2050          | {'precision': 0.2645794966236955, 'recall': 0.5327564894932015, 'f1': 0.3535684987694832, 'number': 809}    | {'precision': 0.12903225806451613, 'recall': 0.06722689075630252, 'f1': 0.08839779005524862, 'number': 119} | {'precision': 0.34989503149055284, 'recall': 0.4694835680751174, 'f1': 0.400962309542903, 'number': 1065}   | 0.3010            | 0.4711         | 0.3673     | 0.4760           |
+| 1.1503        | 4.0   | 40   | 1.1044          | {'precision': 0.28089080459770116, 'recall': 0.48331273176761436, 'f1': 0.3552930486142663, 'number': 809}  | {'precision': 0.2391304347826087, 'recall': 0.18487394957983194, 'f1': 0.2085308056872038, 'number': 119}   | {'precision': 0.4, 'recall': 0.5295774647887324, 'f1': 0.45575757575757575, 'number': 1065}                 | 0.3376            | 0.4902         | 0.3998     | 0.5630           |
+| 1.07          | 5.0   | 50   | 1.1546          | {'precision': 0.30014025245441794, 'recall': 0.5290482076637825, 'f1': 0.38299776286353465, 'number': 809}  | {'precision': 0.3188405797101449, 'recall': 0.18487394957983194, 'f1': 0.23404255319148937, 'number': 119}  | {'precision': 0.4058373870743572, 'recall': 0.5483568075117371, 'f1': 0.4664536741214057, 'number': 1065}   | 0.3524            | 0.5188         | 0.4197     | 0.5383           |
+| 0.9914        | 6.0   | 60   | 1.0507          | {'precision': 0.3119065010956903, 'recall': 0.5278121137206427, 'f1': 0.3921028466483012, 'number': 809}    | {'precision': 0.2345679012345679, 'recall': 0.15966386554621848, 'f1': 0.18999999999999997, 'number': 119}  | {'precision': 0.4122938530734633, 'recall': 0.5164319248826291, 'f1': 0.45852438516048355, 'number': 1065}  | 0.3578            | 0.4997         | 0.4170     | 0.6002           |
+| 0.9373        | 7.0   | 70   | 1.0652          | {'precision': 0.3710691823899371, 'recall': 0.43757725587144625, 'f1': 0.4015882019285309, 'number': 809}   | {'precision': 0.25510204081632654, 'recall': 0.21008403361344538, 'f1': 0.23041474654377883, 'number': 119} | {'precision': 0.4739583333333333, 'recall': 0.5981220657276995, 'f1': 0.5288501452885015, 'number': 1065}   | 0.4240            | 0.5098         | 0.4630     | 0.6006           |
+| 0.8833        | 8.0   | 80   | 1.0389          | {'precision': 0.3351605324980423, 'recall': 0.5290482076637825, 'f1': 0.4103547459252157, 'number': 809}    | {'precision': 0.375, 'recall': 0.20168067226890757, 'f1': 0.2622950819672132, 'number': 119}                | {'precision': 0.44528301886792454, 'recall': 0.5539906103286385, 'f1': 0.49372384937238495, 'number': 1065} | 0.3908            | 0.5228         | 0.4473     | 0.6143           |
+| 0.8029        | 9.0   | 90   | 1.0520          | {'precision': 0.3685612788632327, 'recall': 0.5129789864029666, 'f1': 0.4289405684754522, 'number': 809}    | {'precision': 0.28695652173913044, 'recall': 0.2773109243697479, 'f1': 0.2820512820512821, 'number': 119}   | {'precision': 0.4902874902874903, 'recall': 0.5924882629107981, 'f1': 0.5365646258503401, 'number': 1065}   | 0.4268            | 0.5414         | 0.4773     | 0.6023           |
+| 0.7658        | 10.0  | 100  | 1.0764          | {'precision': 0.3386511965192168, 'recall': 0.5772558714462299, 'f1': 0.42687385740402195, 'number': 809}   | {'precision': 0.3709677419354839, 'recall': 0.19327731092436976, 'f1': 0.2541436464088398, 'number': 119}   | {'precision': 0.4847986852917009, 'recall': 0.5539906103286385, 'f1': 0.5170902716914987, 'number': 1065}   | 0.4063            | 0.5419         | 0.4644     | 0.6066           |
+| 0.7112        | 11.0  | 110  | 1.0675          | {'precision': 0.3728963684676705, 'recall': 0.5203955500618047, 'f1': 0.43446852425180593, 'number': 809}   | {'precision': 0.3333333333333333, 'recall': 0.21008403361344538, 'f1': 0.2577319587628866, 'number': 119}   | {'precision': 0.4918032786885246, 'recall': 0.5915492957746479, 'f1': 0.5370843989769821, 'number': 1065}   | 0.4330            | 0.5399         | 0.4806     | 0.6124           |
+| 0.6875        | 12.0  | 120  | 1.1100          | {'precision': 0.37746256895193064, 'recall': 0.5920889987639061, 'f1': 0.46102021174205965, 'number': 809}  | {'precision': 0.33783783783783783, 'recall': 0.21008403361344538, 'f1': 0.25906735751295334, 'number': 119} | {'precision': 0.514554794520548, 'recall': 0.564319248826291, 'f1': 0.5382892969099866, 'number': 1065}     | 0.4401            | 0.5544         | 0.4907     | 0.6102           |
+| 0.6571        | 13.0  | 130  | 1.0804          | {'precision': 0.36231884057971014, 'recall': 0.5253399258343634, 'f1': 0.4288597376387487, 'number': 809}   | {'precision': 0.313953488372093, 'recall': 0.226890756302521, 'f1': 0.2634146341463415, 'number': 119}      | {'precision': 0.46940244780417567, 'recall': 0.612206572769953, 'f1': 0.5313773431132844, 'number': 1065}   | 0.4169            | 0.5539         | 0.4758     | 0.6141           |
+| 0.6564        | 14.0  | 140  | 1.0934          | {'precision': 0.37943262411347517, 'recall': 0.5290482076637825, 'f1': 0.44192049561177077, 'number': 809}  | {'precision': 0.37662337662337664, 'recall': 0.24369747899159663, 'f1': 0.29591836734693877, 'number': 119} | {'precision': 0.49803613511390415, 'recall': 0.5953051643192488, 'f1': 0.542343883661249, 'number': 1065}   | 0.4403            | 0.5474         | 0.4880     | 0.6215           |
+| 0.6558        | 15.0  | 150  | 1.1050          | {'precision': 0.37133808392715756, 'recall': 0.5797280593325093, 'f1': 0.45270270270270274, 'number': 809}  | {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}   | {'precision': 0.49682539682539684, 'recall': 0.5877934272300469, 'f1': 0.538494623655914, 'number': 1065}   | 0.4307            | 0.5630         | 0.4880     | 0.6093           |
 
 
 ### Framework versions

logs/events.out.tfevents.1711516332.DESKTOP-3M5IIL5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:876ca6fdeb58824c75835144a30bf6272f72627f777ef3359e4351e9cf901044
-size 4757
+oid sha256:b4deca92653057ddea573cea8ac48ce0e0b81919ffb5fa9df82c1f03fae14db7
+size 14681

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0daa5562b7f36a291a4a4b46c0359cc9317126800f9b771185e1dbef9ff64c6e
+oid sha256:e5bddeb885c8ab741dc6c18a38736e70bcbe87870a7f222321f174df84c2268d
 size 450558212

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,