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: 0.6891
-- Answer: {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809}
-- Header: {'precision': 0.3620689655172414, 'recall': 0.35294117647058826, 'f1': 0.3574468085106383, 'number': 119}
-- Question: {'precision': 0.7939609236234458, 'recall': 0.8394366197183099, 'f1': 0.8160657234139663, 'number': 1065}
-- Overall Precision: 0.7436
-- Overall Recall: 0.7988
-- Overall F1: 0.7702
-- Overall Accuracy: 0.8108
+- Loss: 0.7028
+- Answer: {'precision': 0.7362637362637363, 'recall': 0.8281829419035847, 'f1': 0.779522978475858, 'number': 809}
+- Header: {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}
+- Question: {'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}
+- Overall Precision: 0.7387
+- Overall Recall: 0.8083
+- Overall F1: 0.7719
+- Overall Accuracy: 0.8094
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8006        | 1.0   | 10   | 1.5983          | {'precision': 0.015552099533437015, 'recall': 0.012360939431396786, 'f1': 0.013774104683195591, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.20512820512820512, 'recall': 0.12018779342723004, 'f1': 0.15156897572528122, 'number': 1065} | 0.1089            | 0.0692         | 0.0847     | 0.3439           |
-| 1.4817        | 2.0   | 20   | 1.2758          | {'precision': 0.23966065747614, 'recall': 0.27935723114956734, 'f1': 0.2579908675799087, 'number': 809}        | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.4002998500749625, 'recall': 0.5014084507042254, 'f1': 0.4451854939558149, 'number': 1065}    | 0.3338            | 0.3813         | 0.3560     | 0.5953           |
-| 1.1479        | 3.0   | 30   | 0.9589          | {'precision': 0.48957298907646474, 'recall': 0.6093943139678616, 'f1': 0.5429515418502202, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                | {'precision': 0.5823389021479713, 'recall': 0.6873239436619718, 'f1': 0.6304909560723513, 'number': 1065}    | 0.5361            | 0.6147         | 0.5727     | 0.6955           |
-| 0.872         | 4.0   | 40   | 0.8144          | {'precision': 0.574447646493756, 'recall': 0.7391841779975278, 'f1': 0.6464864864864865, 'number': 809}        | {'precision': 0.075, 'recall': 0.025210084033613446, 'f1': 0.03773584905660377, 'number': 119}             | {'precision': 0.6640281442392261, 'recall': 0.7089201877934272, 'f1': 0.6857402361489555, 'number': 1065}    | 0.6114            | 0.6804         | 0.6440     | 0.7492           |
-| 0.6944        | 5.0   | 50   | 0.7241          | {'precision': 0.638682252922423, 'recall': 0.7428924598269468, 'f1': 0.6868571428571428, 'number': 809}        | {'precision': 0.1875, 'recall': 0.12605042016806722, 'f1': 0.1507537688442211, 'number': 119}              | {'precision': 0.674493927125506, 'recall': 0.7821596244131456, 'f1': 0.7243478260869566, 'number': 1065}     | 0.6423            | 0.7270         | 0.6820     | 0.7876           |
-| 0.588         | 6.0   | 60   | 0.6902          | {'precision': 0.6445115810674723, 'recall': 0.7911001236093943, 'f1': 0.7103218645948945, 'number': 809}       | {'precision': 0.265625, 'recall': 0.14285714285714285, 'f1': 0.18579234972677594, 'number': 119}           | {'precision': 0.7232219365895458, 'recall': 0.7924882629107981, 'f1': 0.7562724014336917, 'number': 1065}    | 0.6749            | 0.7531         | 0.7119     | 0.7922           |
-| 0.5155        | 7.0   | 70   | 0.6651          | {'precision': 0.6762820512820513, 'recall': 0.7824474660074165, 'f1': 0.7255014326647564, 'number': 809}       | {'precision': 0.22115384615384615, 'recall': 0.19327731092436976, 'f1': 0.2062780269058296, 'number': 119} | {'precision': 0.7402597402597403, 'recall': 0.8028169014084507, 'f1': 0.7702702702702703, 'number': 1065}    | 0.6884            | 0.7582         | 0.7216     | 0.7979           |
-| 0.4567        | 8.0   | 80   | 0.6544          | {'precision': 0.682062298603652, 'recall': 0.7849196538936959, 'f1': 0.7298850574712644, 'number': 809}        | {'precision': 0.21359223300970873, 'recall': 0.18487394957983194, 'f1': 0.1981981981981982, 'number': 119} | {'precision': 0.759515570934256, 'recall': 0.8244131455399061, 'f1': 0.790634849167042, 'number': 1065}      | 0.7009            | 0.7702         | 0.7339     | 0.8047           |
-| 0.4044        | 9.0   | 90   | 0.6556          | {'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}       | {'precision': 0.2621359223300971, 'recall': 0.226890756302521, 'f1': 0.24324324324324326, 'number': 119}   | {'precision': 0.7710320901994796, 'recall': 0.8347417840375587, 'f1': 0.8016230838593327, 'number': 1065}    | 0.7182            | 0.7837         | 0.7495     | 0.8089           |
-| 0.3974        | 10.0  | 100  | 0.6652          | {'precision': 0.7141292442497261, 'recall': 0.8059332509270705, 'f1': 0.7572590011614402, 'number': 809}       | {'precision': 0.30357142857142855, 'recall': 0.2857142857142857, 'f1': 0.2943722943722944, 'number': 119}  | {'precision': 0.7917414721723519, 'recall': 0.828169014084507, 'f1': 0.8095456631482332, 'number': 1065}     | 0.7331            | 0.7868         | 0.7590     | 0.8094           |
-| 0.3327        | 11.0  | 110  | 0.6705          | {'precision': 0.720620842572062, 'recall': 0.8034610630407911, 'f1': 0.7597895967270601, 'number': 809}        | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119}  | {'precision': 0.7891939769707705, 'recall': 0.8366197183098592, 'f1': 0.812215132178669, 'number': 1065}     | 0.7365            | 0.7923         | 0.7634     | 0.8096           |
-| 0.3194        | 12.0  | 120  | 0.6719          | {'precision': 0.7239057239057239, 'recall': 0.7972805933250927, 'f1': 0.7588235294117647, 'number': 809}       | {'precision': 0.3853211009174312, 'recall': 0.35294117647058826, 'f1': 0.36842105263157904, 'number': 119} | {'precision': 0.7911111111111111, 'recall': 0.8356807511737089, 'f1': 0.812785388127854, 'number': 1065}     | 0.7421            | 0.7913         | 0.7659     | 0.8115           |
-| 0.301         | 13.0  | 130  | 0.6828          | {'precision': 0.7256637168141593, 'recall': 0.8108776266996292, 'f1': 0.7659077641564506, 'number': 809}       | {'precision': 0.41414141414141414, 'recall': 0.3445378151260504, 'f1': 0.3761467889908257, 'number': 119}  | {'precision': 0.8005415162454874, 'recall': 0.8328638497652582, 'f1': 0.8163828808099403, 'number': 1065}    | 0.7504            | 0.7948         | 0.7719     | 0.8099           |
-| 0.286         | 14.0  | 140  | 0.6856          | {'precision': 0.7279821627647715, 'recall': 0.8071693448702101, 'f1': 0.7655334114888628, 'number': 809}       | {'precision': 0.3853211009174312, 'recall': 0.35294117647058826, 'f1': 0.36842105263157904, 'number': 119} | {'precision': 0.7931034482758621, 'recall': 0.8422535211267606, 'f1': 0.8169398907103825, 'number': 1065}    | 0.7450            | 0.7988         | 0.7709     | 0.8108           |
-| 0.2789        | 15.0  | 150  | 0.6891          | {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809}       | {'precision': 0.3620689655172414, 'recall': 0.35294117647058826, 'f1': 0.3574468085106383, 'number': 119}  | {'precision': 0.7939609236234458, 'recall': 0.8394366197183099, 'f1': 0.8160657234139663, 'number': 1065}    | 0.7436            | 0.7988         | 0.7702     | 0.8108           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                        | Header                                                                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8038        | 1.0   | 10   | 1.5774          | {'precision': 0.020026702269692925, 'recall': 0.018541409147095178, 'f1': 0.01925545571245186, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.15968586387434555, 'recall': 0.05727699530516432, 'f1': 0.08431237042156187, 'number': 1065} | 0.0672            | 0.0381         | 0.0487     | 0.3785           |
+| 1.4615        | 2.0   | 20   | 1.2492          | {'precision': 0.13497652582159625, 'recall': 0.14215080346106304, 'f1': 0.13847080072245638, 'number': 809}   | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.4067796610169492, 'recall': 0.4732394366197183, 'f1': 0.4375, 'number': 1065}                | 0.2960            | 0.3106         | 0.3031     | 0.5839           |
+| 1.121         | 3.0   | 30   | 0.9545          | {'precision': 0.4633642930856553, 'recall': 0.5550061804697157, 'f1': 0.5050618672665917, 'number': 809}      | {'precision': 0.1111111111111111, 'recall': 0.008403361344537815, 'f1': 0.015625, 'number': 119}            | {'precision': 0.5644546147978642, 'recall': 0.6948356807511737, 'f1': 0.6228956228956228, 'number': 1065}    | 0.5199            | 0.5971         | 0.5558     | 0.7097           |
+| 0.8446        | 4.0   | 40   | 0.7984          | {'precision': 0.5806451612903226, 'recall': 0.6897404202719407, 'f1': 0.6305084745762712, 'number': 809}      | {'precision': 0.04878048780487805, 'recall': 0.01680672268907563, 'f1': 0.025, 'number': 119}               | {'precision': 0.6506024096385542, 'recall': 0.7605633802816901, 'f1': 0.7012987012987013, 'number': 1065}    | 0.6097            | 0.6874         | 0.6462     | 0.7567           |
+| 0.6751        | 5.0   | 50   | 0.7137          | {'precision': 0.6598049837486457, 'recall': 0.7527812113720643, 'f1': 0.7032332563510393, 'number': 809}      | {'precision': 0.18666666666666668, 'recall': 0.11764705882352941, 'f1': 0.14432989690721648, 'number': 119} | {'precision': 0.694006309148265, 'recall': 0.8262910798122066, 'f1': 0.7543934847835405, 'number': 1065}     | 0.6633            | 0.7541         | 0.7058     | 0.7900           |
+| 0.5714        | 6.0   | 60   | 0.6914          | {'precision': 0.6613065326633166, 'recall': 0.8133498145859085, 'f1': 0.729490022172949, 'number': 809}       | {'precision': 0.26153846153846155, 'recall': 0.14285714285714285, 'f1': 0.18478260869565216, 'number': 119} | {'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065}    | 0.6858            | 0.7632         | 0.7224     | 0.7861           |
+| 0.5064        | 7.0   | 70   | 0.6675          | {'precision': 0.6880927291886196, 'recall': 0.8071693448702101, 'f1': 0.7428896473265074, 'number': 809}      | {'precision': 0.2125984251968504, 'recall': 0.226890756302521, 'f1': 0.21951219512195122, 'number': 119}    | {'precision': 0.7450812660393499, 'recall': 0.8178403755868544, 'f1': 0.7797672336615935, 'number': 1065}    | 0.6909            | 0.7782         | 0.7319     | 0.7985           |
+| 0.4472        | 8.0   | 80   | 0.6608          | {'precision': 0.6995798319327731, 'recall': 0.823238566131026, 'f1': 0.7563884156729132, 'number': 809}       | {'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119} | {'precision': 0.7419087136929461, 'recall': 0.8394366197183099, 'f1': 0.7876651982378854, 'number': 1065}    | 0.6977            | 0.7968         | 0.7440     | 0.8048           |
+| 0.3918        | 9.0   | 90   | 0.6697          | {'precision': 0.7226435536294691, 'recall': 0.8244746600741656, 'f1': 0.7702078521939953, 'number': 809}      | {'precision': 0.2647058823529412, 'recall': 0.3025210084033613, 'f1': 0.2823529411764706, 'number': 119}    | {'precision': 0.7542808219178082, 'recall': 0.8272300469483568, 'f1': 0.7890729959695477, 'number': 1065}    | 0.7113            | 0.7948         | 0.7507     | 0.8038           |
+| 0.3803        | 10.0  | 100  | 0.6890          | {'precision': 0.7176724137931034, 'recall': 0.823238566131026, 'f1': 0.7668393782383419, 'number': 809}       | {'precision': 0.2890625, 'recall': 0.31092436974789917, 'f1': 0.29959514170040485, 'number': 119}           | {'precision': 0.7899910634495085, 'recall': 0.8300469483568075, 'f1': 0.8095238095238095, 'number': 1065}    | 0.7297            | 0.7963         | 0.7615     | 0.8062           |
+| 0.323         | 11.0  | 110  | 0.6893          | {'precision': 0.7279651795429815, 'recall': 0.826946847960445, 'f1': 0.7743055555555555, 'number': 809}       | {'precision': 0.3161764705882353, 'recall': 0.36134453781512604, 'f1': 0.3372549019607843, 'number': 119}   | {'precision': 0.7728055077452668, 'recall': 0.8431924882629108, 'f1': 0.8064660978895375, 'number': 1065}    | 0.7262            | 0.8078         | 0.7648     | 0.8068           |
+| 0.3093        | 12.0  | 120  | 0.6906          | {'precision': 0.7358490566037735, 'recall': 0.8195302843016069, 'f1': 0.775438596491228, 'number': 809}       | {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}  | {'precision': 0.7902654867256638, 'recall': 0.8384976525821596, 'f1': 0.8136674259681094, 'number': 1065}    | 0.7382            | 0.8038         | 0.7696     | 0.8096           |
+| 0.2963        | 13.0  | 130  | 0.6953          | {'precision': 0.7271739130434782, 'recall': 0.826946847960445, 'f1': 0.7738577212261423, 'number': 809}       | {'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}   | {'precision': 0.7943262411347518, 'recall': 0.8413145539906103, 'f1': 0.8171454628362973, 'number': 1065}    | 0.7372            | 0.8063         | 0.7702     | 0.8099           |
+| 0.2773        | 14.0  | 140  | 0.6991          | {'precision': 0.7343578485181119, 'recall': 0.826946847960445, 'f1': 0.7779069767441861, 'number': 809}       | {'precision': 0.3283582089552239, 'recall': 0.3697478991596639, 'f1': 0.34782608695652173, 'number': 119}   | {'precision': 0.7982222222222223, 'recall': 0.8431924882629108, 'f1': 0.8200913242009132, 'number': 1065}    | 0.7424            | 0.8083         | 0.7740     | 0.8099           |
+| 0.2687        | 15.0  | 150  | 0.7028          | {'precision': 0.7362637362637363, 'recall': 0.8281829419035847, 'f1': 0.779522978475858, 'number': 809}       | {'precision': 0.33093525179856115, 'recall': 0.3865546218487395, 'f1': 0.35658914728682173, 'number': 119}  | {'precision': 0.7906360424028268, 'recall': 0.8403755868544601, 'f1': 0.81474738279472, 'number': 1065}      | 0.7387            | 0.8083         | 0.7719     | 0.8094           |
 
 
 ### Framework versions

logs/events.out.tfevents.1714565598.DESKTOP-3CFO1LV.4799.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dccfea9a2a41252fb91f9e4b00e96ef4705c73a028611856e79963f8d63b6808
-size 12645
+oid sha256:be57fe7894430bb82844257a65d24ee9f0f58b958403aeb0ce56176c4cfc1577
+size 15846

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:17ae85e9b80528aadbcc8bb9672d8210297fa8b842aa04c066431543aab08374
+oid sha256:18a14a8f8c46a423eed0a91c32856e2c20c5ceb52926fd5b05e4c9128bedc943
 size 450558212

preprocessor_config.json

@@ -11,7 +11,7 @@
     "data_format",
     "input_data_format"
   ],
-  "apply_ocr": false,
+  "apply_ocr": true,
   "do_resize": true,
   "image_processor_type": "LayoutLMv2ImageProcessor",
   "ocr_lang": null,