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: 1.0890
-- Answer: {'precision': 0.38420107719928187, 'recall': 0.5290482076637825, 'f1': 0.4451378055122205, 'number': 809}
-- Header: {'precision': 0.28888888888888886, 'recall': 0.2184873949579832, 'f1': 0.24880382775119617, 'number': 119}
-- Question: {'precision': 0.48959136468774095, 'recall': 0.596244131455399, 'f1': 0.5376799322607958, 'number': 1065}
-- Overall Precision: 0.4354
-- Overall Recall: 0.5464
-- Overall F1: 0.4846
-- Overall Accuracy: 0.6258
+- Loss: 1.1237
+- Answer: {'precision': 0.38014311270125223, 'recall': 0.5253399258343634, 'f1': 0.44110015568240785, 'number': 809}
+- Header: {'precision': 0.32608695652173914, 'recall': 0.25210084033613445, 'f1': 0.2843601895734597, 'number': 119}
+- Question: {'precision': 0.5316760224538893, 'recall': 0.6225352112676056, 'f1': 0.5735294117647058, 'number': 1065}
+- Overall Precision: 0.4550
+- Overall Recall: 0.5610
+- Overall F1: 0.5025
+- Overall Accuracy: 0.6009
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.7643        | 1.0   | 10   | 1.5177          | {'precision': 0.052202283849918436, 'recall': 0.07911001236093942, 'f1': 0.06289926289926291, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.2581360946745562, 'recall': 0.3276995305164319, 'f1': 0.2887877534133223, 'number': 1065}   | 0.1602            | 0.2072         | 0.1807     | 0.3823           |
-| 1.4448        | 2.0   | 20   | 1.3359          | {'precision': 0.18779342723004694, 'recall': 0.39555006180469715, 'f1': 0.2546756864305611, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.2733245729303548, 'recall': 0.39061032863849765, 'f1': 0.321608040201005, 'number': 1065}   | 0.2273            | 0.3693         | 0.2814     | 0.4206           |
-| 1.2967        | 3.0   | 30   | 1.2160          | {'precision': 0.2261437908496732, 'recall': 0.4276885043263288, 'f1': 0.29585292860196666, 'number': 809}    | {'precision': 0.02040816326530612, 'recall': 0.008403361344537815, 'f1': 0.011904761904761904, 'number': 119} | {'precision': 0.34987113402061853, 'recall': 0.5098591549295775, 'f1': 0.41497898356897206, 'number': 1065} | 0.2843            | 0.4466         | 0.3474     | 0.4803           |
-| 1.172         | 4.0   | 40   | 1.1080          | {'precision': 0.2609299097848716, 'recall': 0.4647713226205192, 'f1': 0.3342222222222222, 'number': 809}     | {'precision': 0.2, 'recall': 0.12605042016806722, 'f1': 0.15463917525773196, 'number': 119}                   | {'precision': 0.39096126255380204, 'recall': 0.5117370892018779, 'f1': 0.4432696217974787, 'number': 1065}  | 0.3216            | 0.4696         | 0.3818     | 0.5682           |
-| 1.0668        | 5.0   | 50   | 1.1224          | {'precision': 0.2859304084720121, 'recall': 0.4672435105067985, 'f1': 0.3547630220553731, 'number': 809}     | {'precision': 0.2571428571428571, 'recall': 0.15126050420168066, 'f1': 0.19047619047619044, 'number': 119}    | {'precision': 0.39935691318327976, 'recall': 0.5830985915492958, 'f1': 0.47404580152671755, 'number': 1065} | 0.3451            | 0.5103         | 0.4117     | 0.5719           |
-| 1.0053        | 6.0   | 60   | 1.0842          | {'precision': 0.31098430813124106, 'recall': 0.5389369592088998, 'f1': 0.3943916779737675, 'number': 809}    | {'precision': 0.3333333333333333, 'recall': 0.17647058823529413, 'f1': 0.23076923076923078, 'number': 119}    | {'precision': 0.4626998223801066, 'recall': 0.4892018779342723, 'f1': 0.4755819260611593, 'number': 1065}   | 0.3775            | 0.4907         | 0.4267     | 0.5869           |
-| 0.9367        | 7.0   | 70   | 1.0354          | {'precision': 0.33884297520661155, 'recall': 0.4561186650185414, 'f1': 0.38883034773445735, 'number': 809}   | {'precision': 0.27848101265822783, 'recall': 0.18487394957983194, 'f1': 0.2222222222222222, 'number': 119}    | {'precision': 0.4579100145137881, 'recall': 0.5924882629107981, 'f1': 0.5165779778960293, 'number': 1065}   | 0.4014            | 0.5128         | 0.4503     | 0.6069           |
-| 0.8736        | 8.0   | 80   | 1.0367          | {'precision': 0.3433583959899749, 'recall': 0.5080346106304079, 'f1': 0.4097706879361914, 'number': 809}     | {'precision': 0.24675324675324675, 'recall': 0.15966386554621848, 'f1': 0.19387755102040818, 'number': 119}   | {'precision': 0.4403292181069959, 'recall': 0.6028169014084507, 'f1': 0.5089179548156956, 'number': 1065}   | 0.3924            | 0.5379         | 0.4538     | 0.6083           |
-| 0.8322        | 9.0   | 90   | 1.0585          | {'precision': 0.38257575757575757, 'recall': 0.49938195302843014, 'f1': 0.43324396782841823, 'number': 809}  | {'precision': 0.1919191919191919, 'recall': 0.15966386554621848, 'f1': 0.17431192660550457, 'number': 119}    | {'precision': 0.48465266558966075, 'recall': 0.5633802816901409, 'f1': 0.5210594876248372, 'number': 1065}  | 0.4275            | 0.5133         | 0.4665     | 0.6171           |
-| 0.8201        | 10.0  | 100  | 1.0589          | {'precision': 0.3753527751646284, 'recall': 0.4932014833127318, 'f1': 0.42628205128205127, 'number': 809}    | {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119}                 | {'precision': 0.4782945736434108, 'recall': 0.5793427230046948, 'f1': 0.5239915074309979, 'number': 1065}   | 0.4266            | 0.5208         | 0.4690     | 0.6086           |
-| 0.7451        | 11.0  | 110  | 1.0393          | {'precision': 0.3754716981132076, 'recall': 0.4919653893695921, 'f1': 0.42589620117710003, 'number': 809}    | {'precision': 0.2804878048780488, 'recall': 0.19327731092436976, 'f1': 0.22885572139303487, 'number': 119}    | {'precision': 0.4541832669322709, 'recall': 0.6422535211267606, 'f1': 0.5320886814469078, 'number': 1065}   | 0.4173            | 0.5544         | 0.4762     | 0.6132           |
-| 0.7445        | 12.0  | 120  | 1.0649          | {'precision': 0.3752166377816291, 'recall': 0.5352286773794809, 'f1': 0.4411614875191034, 'number': 809}     | {'precision': 0.2653061224489796, 'recall': 0.2184873949579832, 'f1': 0.23963133640552997, 'number': 119}     | {'precision': 0.49351701782820095, 'recall': 0.571830985915493, 'f1': 0.5297955632883862, 'number': 1065}   | 0.4296            | 0.5359         | 0.4769     | 0.6145           |
-| 0.7064        | 13.0  | 130  | 1.1267          | {'precision': 0.3775933609958506, 'recall': 0.5624227441285538, 'f1': 0.45183714001986097, 'number': 809}    | {'precision': 0.3116883116883117, 'recall': 0.20168067226890757, 'f1': 0.24489795918367344, 'number': 119}    | {'precision': 0.5072094995759118, 'recall': 0.5615023474178403, 'f1': 0.5329768270944741, 'number': 1065}   | 0.4376            | 0.5404         | 0.4836     | 0.6174           |
-| 0.6846        | 14.0  | 140  | 1.0692          | {'precision': 0.3945841392649903, 'recall': 0.5043263288009888, 'f1': 0.44275637547476937, 'number': 809}    | {'precision': 0.29411764705882354, 'recall': 0.21008403361344538, 'f1': 0.2450980392156863, 'number': 119}    | {'precision': 0.48787878787878786, 'recall': 0.6046948356807512, 'f1': 0.5400419287211741, 'number': 1065}  | 0.4416            | 0.5404         | 0.4860     | 0.6198           |
-| 0.6688        | 15.0  | 150  | 1.0890          | {'precision': 0.38420107719928187, 'recall': 0.5290482076637825, 'f1': 0.4451378055122205, 'number': 809}    | {'precision': 0.28888888888888886, 'recall': 0.2184873949579832, 'f1': 0.24880382775119617, 'number': 119}    | {'precision': 0.48959136468774095, 'recall': 0.596244131455399, 'f1': 0.5376799322607958, 'number': 1065}   | 0.4354            | 0.5464         | 0.4846     | 0.6258           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.7571        | 1.0   | 10   | 1.5578          | {'precision': 0.03216374269005848, 'recall': 0.027194066749072928, 'f1': 0.029470864032150032, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.24550898203592814, 'recall': 0.1539906103286385, 'f1': 0.189267166762839, 'number': 1065}   | 0.1376            | 0.0933         | 0.1112     | 0.3461           |
+| 1.4754        | 2.0   | 20   | 1.3886          | {'precision': 0.18979987088444158, 'recall': 0.36341161928306553, 'f1': 0.24936386768447838, 'number': 809}   | {'precision': 0.0851063829787234, 'recall': 0.03361344537815126, 'f1': 0.048192771084337345, 'number': 119} | {'precision': 0.2655198204936425, 'recall': 0.3333333333333333, 'f1': 0.29558701082431305, 'number': 1065}  | 0.2226            | 0.3276         | 0.2651     | 0.4190           |
+| 1.2882        | 3.0   | 30   | 1.2556          | {'precision': 0.25, 'recall': 0.5030902348578492, 'f1': 0.3340172343044727, 'number': 809}                    | {'precision': 0.07547169811320754, 'recall': 0.03361344537815126, 'f1': 0.04651162790697674, 'number': 119} | {'precision': 0.3400431344356578, 'recall': 0.444131455399061, 'f1': 0.38517915309446255, 'number': 1065}   | 0.2878            | 0.4436         | 0.3491     | 0.4540           |
+| 1.1508        | 4.0   | 40   | 1.1427          | {'precision': 0.27153558052434457, 'recall': 0.5377008652657602, 'f1': 0.36084612194110327, 'number': 809}    | {'precision': 0.23595505617977527, 'recall': 0.17647058823529413, 'f1': 0.20192307692307693, 'number': 119} | {'precision': 0.4009397024275646, 'recall': 0.4807511737089202, 'f1': 0.43723313407344155, 'number': 1065}  | 0.3261            | 0.4857         | 0.3902     | 0.5272           |
+| 1.0506        | 5.0   | 50   | 1.1546          | {'precision': 0.28481455563331, 'recall': 0.5030902348578492, 'f1': 0.36371760500446826, 'number': 809}       | {'precision': 0.24719101123595505, 'recall': 0.18487394957983194, 'f1': 0.21153846153846156, 'number': 119} | {'precision': 0.4018324607329843, 'recall': 0.5765258215962441, 'f1': 0.4735827227150019, 'number': 1065}   | 0.3424            | 0.5233         | 0.4140     | 0.5441           |
+| 0.9855        | 6.0   | 60   | 1.1005          | {'precision': 0.31229012760241776, 'recall': 0.5747836835599506, 'f1': 0.4046997389033942, 'number': 809}     | {'precision': 0.328125, 'recall': 0.17647058823529413, 'f1': 0.22950819672131148, 'number': 119}            | {'precision': 0.47493403693931396, 'recall': 0.5070422535211268, 'f1': 0.49046321525885556, 'number': 1065} | 0.3814            | 0.5148         | 0.4382     | 0.5656           |
+| 0.9039        | 7.0   | 70   | 1.0551          | {'precision': 0.32831608654750705, 'recall': 0.43139678615574784, 'f1': 0.37286324786324787, 'number': 809}   | {'precision': 0.2743362831858407, 'recall': 0.2605042016806723, 'f1': 0.26724137931034486, 'number': 119}   | {'precision': 0.4689306358381503, 'recall': 0.6093896713615023, 'f1': 0.5300122498979176, 'number': 1065}   | 0.4020            | 0.5163         | 0.4520     | 0.5981           |
+| 0.841         | 8.0   | 80   | 1.0710          | {'precision': 0.3379032258064516, 'recall': 0.5179233621755254, 'f1': 0.40897999023914106, 'number': 809}     | {'precision': 0.2926829268292683, 'recall': 0.20168067226890757, 'f1': 0.23880597014925373, 'number': 119}  | {'precision': 0.4723435225618632, 'recall': 0.6093896713615023, 'f1': 0.5321853218532185, 'number': 1065}   | 0.4050            | 0.5479         | 0.4658     | 0.5885           |
+| 0.7758        | 9.0   | 90   | 1.0917          | {'precision': 0.3506916192026037, 'recall': 0.5327564894932015, 'f1': 0.4229636898920511, 'number': 809}      | {'precision': 0.3076923076923077, 'recall': 0.23529411764705882, 'f1': 0.26666666666666666, 'number': 119}  | {'precision': 0.4916286149162861, 'recall': 0.6065727699530516, 'f1': 0.5430853299705759, 'number': 1065}   | 0.4195            | 0.5544         | 0.4776     | 0.5892           |
+| 0.7737        | 10.0  | 100  | 1.1005          | {'precision': 0.36325503355704697, 'recall': 0.5352286773794809, 'f1': 0.43278360819590206, 'number': 809}    | {'precision': 0.3902439024390244, 'recall': 0.2689075630252101, 'f1': 0.31840796019900497, 'number': 119}   | {'precision': 0.5075456711675933, 'recall': 0.6, 'f1': 0.5499139414802064, 'number': 1065}                  | 0.4358            | 0.5539         | 0.4878     | 0.5934           |
+| 0.6942        | 11.0  | 110  | 1.0974          | {'precision': 0.3707136237256719, 'recall': 0.49443757725587145, 'f1': 0.423728813559322, 'number': 809}      | {'precision': 0.34, 'recall': 0.2857142857142857, 'f1': 0.31050228310502287, 'number': 119}                 | {'precision': 0.5255775577557755, 'recall': 0.5981220657276995, 'f1': 0.559508124725516, 'number': 1065}    | 0.4479            | 0.5374         | 0.4886     | 0.6107           |
+| 0.691         | 12.0  | 120  | 1.0991          | {'precision': 0.381950774840474, 'recall': 0.5179233621755254, 'f1': 0.43966421825813223, 'number': 809}      | {'precision': 0.36666666666666664, 'recall': 0.2773109243697479, 'f1': 0.31578947368421056, 'number': 119}  | {'precision': 0.5208825847123719, 'recall': 0.6206572769953052, 'f1': 0.5664095972579263, 'number': 1065}   | 0.4532            | 0.5585         | 0.5003     | 0.6116           |
+| 0.6595        | 13.0  | 130  | 1.1179          | {'precision': 0.3776223776223776, 'recall': 0.5339925834363412, 'f1': 0.44239631336405527, 'number': 809}     | {'precision': 0.3563218390804598, 'recall': 0.2605042016806723, 'f1': 0.30097087378640774, 'number': 119}   | {'precision': 0.530562347188264, 'recall': 0.6112676056338028, 'f1': 0.5680628272251308, 'number': 1065}    | 0.4532            | 0.5590         | 0.5006     | 0.6010           |
+| 0.6288        | 14.0  | 140  | 1.1441          | {'precision': 0.3689075630252101, 'recall': 0.5426452410383189, 'f1': 0.4392196098049025, 'number': 809}      | {'precision': 0.3595505617977528, 'recall': 0.2689075630252101, 'f1': 0.3076923076923077, 'number': 119}    | {'precision': 0.54614733276884, 'recall': 0.6056338028169014, 'f1': 0.5743544078361531, 'number': 1065}     | 0.4537            | 0.5600         | 0.5012     | 0.5913           |
+| 0.6245        | 15.0  | 150  | 1.1237          | {'precision': 0.38014311270125223, 'recall': 0.5253399258343634, 'f1': 0.44110015568240785, 'number': 809}    | {'precision': 0.32608695652173914, 'recall': 0.25210084033613445, 'f1': 0.2843601895734597, 'number': 119}  | {'precision': 0.5316760224538893, 'recall': 0.6225352112676056, 'f1': 0.5735294117647058, 'number': 1065}   | 0.4550            | 0.5610         | 0.5025     | 0.6009           |
 
 
 ### Framework versions

logs/events.out.tfevents.1710355327.1b7450536ea5.7044.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:808c6b1f38064ce111e043236631add4ccc2818275f66efd44bb8d6f2fec3241
-size 13954
+oid sha256:5d89b4b817be3c0898f9032dd8830ae8e70a1d3b38dc2cf1263c89cc908eedb7
+size 15738

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:db4a3f114adf48f673216b6f6d897a3aa4fdf8156c343a08dd3d00fc5f2142b9
+oid sha256:8d04244e7ad08f3795b0cba441c6ad40d65e32354436ba44315d22e7df94278b
 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,