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: 1.0436
-- Answer: {'precision': 0.3978685612788632, 'recall': 0.553770086526576, 'f1': 0.4630490956072351, 'number': 809}
-- Header: {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}
-- Question: {'precision': 0.5241157556270096, 'recall': 0.612206572769953, 'f1': 0.5647466435686443, 'number': 1065}
-- Overall Precision: 0.4596
-- Overall Recall: 0.5655
-- Overall F1: 0.5071
-- Overall Accuracy: 0.6267
+- Loss: 1.1246
+- Answer: {'precision': 0.3804878048780488, 'recall': 0.4820766378244747, 'f1': 0.425299890948746, 'number': 809}
+- Header: {'precision': 0.34408602150537637, 'recall': 0.2689075630252101, 'f1': 0.3018867924528302, 'number': 119}
+- Question: {'precision': 0.4845360824742268, 'recall': 0.6178403755868545, 'f1': 0.5431283532810565, 'number': 1065}
+- Overall Precision: 0.4362
+- Overall Recall: 0.5419
+- Overall F1: 0.4833
+- Overall Accuracy: 0.6171
 
 ## Model description
 
@@ -53,28 +53,28 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7148        | 1.0   | 10   | 1.5016          | {'precision': 0.08819018404907976, 'recall': 0.14215080346106304, 'f1': 0.10884997633696165, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.2198581560283688, 'recall': 0.08732394366197183, 'f1': 0.125, 'number': 1065}               | 0.1204            | 0.1044         | 0.1118     | 0.3613           |
-| 1.4202        | 2.0   | 20   | 1.3572          | {'precision': 0.21160042964554243, 'recall': 0.48702101359703337, 'f1': 0.29502059153874954, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24895977808599168, 'recall': 0.3370892018779343, 'f1': 0.28639808536098926, 'number': 1065} | 0.2265            | 0.3778         | 0.2832     | 0.4216           |
-| 1.2863        | 3.0   | 30   | 1.2150          | {'precision': 0.25656167979002625, 'recall': 0.48331273176761436, 'f1': 0.33519074153450495, 'number': 809} | {'precision': 0.06779661016949153, 'recall': 0.03361344537815126, 'f1': 0.0449438202247191, 'number': 119}  | {'precision': 0.3437908496732026, 'recall': 0.49389671361502346, 'f1': 0.4053949903660886, 'number': 1065}  | 0.2959            | 0.4621         | 0.3608     | 0.4790           |
-| 1.1633        | 4.0   | 40   | 1.1144          | {'precision': 0.2625454545454545, 'recall': 0.446229913473424, 'f1': 0.3305860805860806, 'number': 809}     | {'precision': 0.3253012048192771, 'recall': 0.226890756302521, 'f1': 0.26732673267326734, 'number': 119}    | {'precision': 0.37986577181208053, 'recall': 0.5314553990610329, 'f1': 0.4430528375733855, 'number': 1065}  | 0.3236            | 0.4787         | 0.3862     | 0.5442           |
-| 1.0585        | 5.0   | 50   | 1.0827          | {'precision': 0.3039940828402367, 'recall': 0.5080346106304079, 'f1': 0.38037945395650163, 'number': 809}   | {'precision': 0.32432432432432434, 'recall': 0.20168067226890757, 'f1': 0.24870466321243526, 'number': 119} | {'precision': 0.4149933065595716, 'recall': 0.5821596244131455, 'f1': 0.48456428292301673, 'number': 1065}  | 0.3613            | 0.5294         | 0.4295     | 0.5700           |
-| 0.9987        | 6.0   | 60   | 1.0373          | {'precision': 0.326783114992722, 'recall': 0.5550061804697157, 'f1': 0.4113605130554283, 'number': 809}     | {'precision': 0.4074074074074074, 'recall': 0.18487394957983194, 'f1': 0.2543352601156069, 'number': 119}   | {'precision': 0.453125, 'recall': 0.5173708920187794, 'f1': 0.4831214379658045, 'number': 1065}             | 0.3865            | 0.5128         | 0.4408     | 0.6016           |
-| 0.9315        | 7.0   | 70   | 1.0055          | {'precision': 0.34718100890207715, 'recall': 0.4338689740420272, 'f1': 0.3857142857142857, 'number': 809}   | {'precision': 0.3229166666666667, 'recall': 0.2605042016806723, 'f1': 0.28837209302325584, 'number': 119}   | {'precision': 0.4558011049723757, 'recall': 0.6197183098591549, 'f1': 0.5252686032630322, 'number': 1065}   | 0.4078            | 0.5228         | 0.4582     | 0.6164           |
-| 0.8716        | 8.0   | 80   | 1.0112          | {'precision': 0.33733013589128696, 'recall': 0.5216316440049443, 'f1': 0.40970873786407763, 'number': 809}  | {'precision': 0.3717948717948718, 'recall': 0.24369747899159663, 'f1': 0.29441624365482233, 'number': 119}  | {'precision': 0.44542372881355935, 'recall': 0.6169014084507042, 'f1': 0.5173228346456693, 'number': 1065}  | 0.3951            | 0.5559         | 0.4620     | 0.6153           |
-| 0.8102        | 9.0   | 90   | 1.0152          | {'precision': 0.3773062730627306, 'recall': 0.5055624227441285, 'f1': 0.4321183306920232, 'number': 809}    | {'precision': 0.3611111111111111, 'recall': 0.2184873949579832, 'f1': 0.27225130890052357, 'number': 119}   | {'precision': 0.4880860876249039, 'recall': 0.596244131455399, 'f1': 0.536770921386306, 'number': 1065}     | 0.4355            | 0.5369         | 0.4809     | 0.6226           |
-| 0.8003        | 10.0  | 100  | 1.0342          | {'precision': 0.3804878048780488, 'recall': 0.5784919653893696, 'f1': 0.45904855321235905, 'number': 809}   | {'precision': 0.32, 'recall': 0.20168067226890757, 'f1': 0.24742268041237112, 'number': 119}                | {'precision': 0.5183887915936952, 'recall': 0.5558685446009389, 'f1': 0.5364748527412777, 'number': 1065}   | 0.4430            | 0.5439         | 0.4883     | 0.6143           |
-| 0.728         | 11.0  | 110  | 1.0330          | {'precision': 0.3871559633027523, 'recall': 0.5216316440049443, 'f1': 0.4444444444444445, 'number': 809}    | {'precision': 0.29213483146067415, 'recall': 0.2184873949579832, 'f1': 0.25, 'number': 119}                 | {'precision': 0.4981791697013838, 'recall': 0.6422535211267606, 'f1': 0.561115668580804, 'number': 1065}    | 0.4436            | 0.5680         | 0.4981     | 0.6221           |
-| 0.7175        | 12.0  | 120  | 1.0841          | {'precision': 0.38127090301003347, 'recall': 0.5636588380716935, 'f1': 0.45486284289276807, 'number': 809}  | {'precision': 0.3684210526315789, 'recall': 0.23529411764705882, 'f1': 0.28717948717948716, 'number': 119}  | {'precision': 0.5153225806451613, 'recall': 0.6, 'f1': 0.5544468546637744, 'number': 1065}                  | 0.4471            | 0.5635         | 0.4986     | 0.6243           |
-| 0.6893        | 13.0  | 130  | 1.0501          | {'precision': 0.3815126050420168, 'recall': 0.5611866501854141, 'f1': 0.4542271135567784, 'number': 809}    | {'precision': 0.30952380952380953, 'recall': 0.2184873949579832, 'f1': 0.2561576354679803, 'number': 119}   | {'precision': 0.5256950294860994, 'recall': 0.5859154929577465, 'f1': 0.5541740674955595, 'number': 1065}   | 0.4486            | 0.5539         | 0.4957     | 0.6228           |
-| 0.653         | 14.0  | 140  | 1.0222          | {'precision': 0.39345794392523364, 'recall': 0.5203955500618047, 'f1': 0.4481106971793507, 'number': 809}   | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119}   | {'precision': 0.5045180722891566, 'recall': 0.6291079812206573, 'f1': 0.55996656916005, 'number': 1065}     | 0.4515            | 0.5610         | 0.5003     | 0.6269           |
-| 0.6494        | 15.0  | 150  | 1.0436          | {'precision': 0.3978685612788632, 'recall': 0.553770086526576, 'f1': 0.4630490956072351, 'number': 809}     | {'precision': 0.32926829268292684, 'recall': 0.226890756302521, 'f1': 0.26865671641791045, 'number': 119}   | {'precision': 0.5241157556270096, 'recall': 0.612206572769953, 'f1': 0.5647466435686443, 'number': 1065}    | 0.4596            | 0.5655         | 0.5071     | 0.6267           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                      | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7202        | 1.0   | 10   | 1.4980          | {'precision': 0.05310734463276836, 'recall': 0.0580964153275649, 'f1': 0.05548996458087367, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.26246719160104987, 'recall': 0.28169014084507044, 'f1': 0.27173913043478265, 'number': 1065} | 0.1711            | 0.1741         | 0.1726     | 0.3625           |
+| 1.4151        | 2.0   | 20   | 1.3029          | {'precision': 0.19834183673469388, 'recall': 0.38442521631644005, 'f1': 0.26167437946992006, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.266388557806913, 'recall': 0.4197183098591549, 'f1': 0.32592052497265767, 'number': 1065}    | 0.2325            | 0.3803         | 0.2886     | 0.4280           |
+| 1.259         | 3.0   | 30   | 1.1884          | {'precision': 0.2627235213204952, 'recall': 0.4721878862793572, 'f1': 0.3376049491825011, 'number': 809}    | {'precision': 0.06349206349206349, 'recall': 0.03361344537815126, 'f1': 0.04395604395604396, 'number': 119} | {'precision': 0.3270588235294118, 'recall': 0.5220657276995305, 'f1': 0.4021699819168174, 'number': 1065}    | 0.2928            | 0.4727         | 0.3616     | 0.4939           |
+| 1.1328        | 4.0   | 40   | 1.0951          | {'precision': 0.30996309963099633, 'recall': 0.519159456118665, 'f1': 0.3881700554528651, 'number': 809}    | {'precision': 0.2857142857142857, 'recall': 0.18487394957983194, 'f1': 0.22448979591836735, 'number': 119}  | {'precision': 0.4103139013452915, 'recall': 0.5154929577464789, 'f1': 0.4569288389513109, 'number': 1065}    | 0.3578            | 0.4972         | 0.4161     | 0.5748           |
+| 1.0223        | 5.0   | 50   | 1.0810          | {'precision': 0.28736581337737405, 'recall': 0.43016069221260816, 'f1': 0.3445544554455445, 'number': 809}  | {'precision': 0.37142857142857144, 'recall': 0.2184873949579832, 'f1': 0.2751322751322751, 'number': 119}   | {'precision': 0.38396624472573837, 'recall': 0.5981220657276995, 'f1': 0.4676945668135095, 'number': 1065}   | 0.3439            | 0.5073         | 0.4099     | 0.5856           |
+| 0.9408        | 6.0   | 60   | 1.0602          | {'precision': 0.3160667251975417, 'recall': 0.44499381953028433, 'f1': 0.3696098562628337, 'number': 809}   | {'precision': 0.345679012345679, 'recall': 0.23529411764705882, 'f1': 0.27999999999999997, 'number': 119}   | {'precision': 0.4154838709677419, 'recall': 0.6046948356807512, 'f1': 0.49254302103250486, 'number': 1065}   | 0.3726            | 0.5178         | 0.4333     | 0.5983           |
+| 0.8629        | 7.0   | 70   | 1.0853          | {'precision': 0.3160220994475138, 'recall': 0.3535228677379481, 'f1': 0.33372228704784135, 'number': 809}   | {'precision': 0.375, 'recall': 0.2773109243697479, 'f1': 0.31884057971014496, 'number': 119}                | {'precision': 0.42748091603053434, 'recall': 0.6309859154929578, 'f1': 0.50967007963595, 'number': 1065}     | 0.3864            | 0.4972         | 0.4348     | 0.5961           |
+| 0.8089        | 8.0   | 80   | 1.0864          | {'precision': 0.35083114610673666, 'recall': 0.4956736711990111, 'f1': 0.4108606557377049, 'number': 809}   | {'precision': 0.36904761904761907, 'recall': 0.2605042016806723, 'f1': 0.30541871921182273, 'number': 119}  | {'precision': 0.4398051496172582, 'recall': 0.5934272300469483, 'f1': 0.5051958433253397, 'number': 1065}    | 0.3994            | 0.5339         | 0.4569     | 0.6110           |
+| 0.7662        | 9.0   | 90   | 1.0967          | {'precision': 0.36006974716652135, 'recall': 0.5105067985166872, 'f1': 0.42229038854805717, 'number': 809}  | {'precision': 0.4266666666666667, 'recall': 0.2689075630252101, 'f1': 0.32989690721649484, 'number': 119}   | {'precision': 0.4724770642201835, 'recall': 0.5802816901408451, 'f1': 0.5208596713021492, 'number': 1065}    | 0.4202            | 0.5334         | 0.4700     | 0.6115           |
+| 0.7718        | 10.0  | 100  | 1.1450          | {'precision': 0.375, 'recall': 0.5414091470951793, 'f1': 0.44309559939301973, 'number': 809}                | {'precision': 0.4050632911392405, 'recall': 0.2689075630252101, 'f1': 0.3232323232323232, 'number': 119}    | {'precision': 0.5078125, 'recall': 0.5492957746478874, 'f1': 0.5277401894451962, 'number': 1065}             | 0.4398            | 0.5294         | 0.4804     | 0.6057           |
+| 0.6988        | 11.0  | 110  | 1.1180          | {'precision': 0.36609829488465395, 'recall': 0.4511742892459827, 'f1': 0.4042081949058693, 'number': 809}   | {'precision': 0.3333333333333333, 'recall': 0.2689075630252101, 'f1': 0.29767441860465116, 'number': 119}   | {'precision': 0.4661602209944751, 'recall': 0.6338028169014085, 'f1': 0.5372065260644648, 'number': 1065}    | 0.4219            | 0.5379         | 0.4729     | 0.6089           |
+| 0.6905        | 12.0  | 120  | 1.1064          | {'precision': 0.36837029893924783, 'recall': 0.4721878862793572, 'f1': 0.41386782231852653, 'number': 809}  | {'precision': 0.3793103448275862, 'recall': 0.2773109243697479, 'f1': 0.32038834951456313, 'number': 119}   | {'precision': 0.47112676056338026, 'recall': 0.6281690140845071, 'f1': 0.5384305835010061, 'number': 1065}   | 0.4261            | 0.5439         | 0.4778     | 0.6149           |
+| 0.666         | 13.0  | 130  | 1.1045          | {'precision': 0.36981132075471695, 'recall': 0.484548825710754, 'f1': 0.4194756554307116, 'number': 809}    | {'precision': 0.3516483516483517, 'recall': 0.2689075630252101, 'f1': 0.3047619047619048, 'number': 119}    | {'precision': 0.48205128205128206, 'recall': 0.6178403755868545, 'f1': 0.5415637860082304, 'number': 1065}   | 0.4300            | 0.5429         | 0.4799     | 0.6174           |
+| 0.6335        | 14.0  | 140  | 1.1195          | {'precision': 0.3810463968410661, 'recall': 0.47713226205191595, 'f1': 0.42371020856201974, 'number': 809}  | {'precision': 0.34831460674157305, 'recall': 0.2605042016806723, 'f1': 0.2980769230769231, 'number': 119}   | {'precision': 0.4817204301075269, 'recall': 0.6309859154929578, 'f1': 0.5463414634146342, 'number': 1065}    | 0.4361            | 0.5464         | 0.4851     | 0.6187           |
+| 0.6277        | 15.0  | 150  | 1.1246          | {'precision': 0.3804878048780488, 'recall': 0.4820766378244747, 'f1': 0.425299890948746, 'number': 809}     | {'precision': 0.34408602150537637, 'recall': 0.2689075630252101, 'f1': 0.3018867924528302, 'number': 119}   | {'precision': 0.4845360824742268, 'recall': 0.6178403755868545, 'f1': 0.5431283532810565, 'number': 1065}    | 0.4362            | 0.5419         | 0.4833     | 0.6171           |
 
 
 ### Framework versions
 
 - Transformers 4.38.2
-- Pytorch 2.1.0+cu121
+- Pytorch 2.2.1+cu121
 - Datasets 2.18.0
 - Tokenizers 0.15.2

logs/events.out.tfevents.1710154970.ip-172-31-23-111.10094.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d846738c883ceb2925ba374a0a206407a50db7a981ac7c7e5d12b36e5edadb6f
-size 14670
+oid sha256:20204db4d0915948a2ad4e07c53e09ecb12905bd9a010d8956584edfad9392eb
+size 15739