End of training

README.md

@@ -18,14 +18,14 @@ 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: 0.7039
-- Answer: {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}
-- Header: {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}
-- Question: {'precision': 0.7771929824561403, 'recall': 0.831924882629108, 'f1': 0.8036281179138323, 'number': 1065}
-- Overall Precision: 0.7230
-- Overall Recall: 0.7898
-- Overall F1: 0.7549
-- Overall Accuracy: 0.8028
+- Loss: 0.6898
+- Answer: {'precision': 0.6987951807228916, 'recall': 0.788627935723115, 'f1': 0.7409988385598142, 'number': 809}
+- Header: {'precision': 0.3157894736842105, 'recall': 0.35294117647058826, 'f1': 0.33333333333333337, 'number': 119}
+- Question: {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}
+- Overall Precision: 0.7168
+- Overall Recall: 0.7863
+- Overall F1: 0.7499
+- Overall Accuracy: 0.8057
 
 ## Model description
 
@@ -55,23 +55,23 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8301        | 1.0   | 10   | 1.5849          | {'precision': 0.008086253369272238, 'recall': 0.007416563658838072, 'f1': 0.007736943907156674, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.22358346094946402, 'recall': 0.13708920187793427, 'f1': 0.16996507566938301, 'number': 1065} | 0.1090            | 0.0763         | 0.0897     | 0.3514           |
-| 1.4704        | 2.0   | 20   | 1.2710          | {'precision': 0.2843881856540084, 'recall': 0.41656365883807167, 'f1': 0.3380140421263791, 'number': 809}      | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                 | {'precision': 0.3906474820143885, 'recall': 0.5098591549295775, 'f1': 0.44236252545824845, 'number': 1065}   | 0.3408            | 0.4415         | 0.3847     | 0.6020           |
-| 1.1259        | 3.0   | 30   | 0.9451          | {'precision': 0.47373447946513847, 'recall': 0.6131025957972805, 'f1': 0.5344827586206896, 'number': 809}      | {'precision': 0.0625, 'recall': 0.025210084033613446, 'f1': 0.035928143712574856, 'number': 119}            | {'precision': 0.5223654283548143, 'recall': 0.6469483568075117, 'f1': 0.5780201342281879, 'number': 1065}    | 0.4921            | 0.5961         | 0.5391     | 0.7000           |
-| 0.8549        | 4.0   | 40   | 0.7891          | {'precision': 0.5652985074626866, 'recall': 0.7490729295426453, 'f1': 0.6443381180223287, 'number': 809}       | {'precision': 0.20833333333333334, 'recall': 0.12605042016806722, 'f1': 0.15706806282722513, 'number': 119} | {'precision': 0.6485013623978202, 'recall': 0.6704225352112676, 'f1': 0.6592797783933518, 'number': 1065}    | 0.5947            | 0.6698         | 0.6300     | 0.7562           |
-| 0.6872        | 5.0   | 50   | 0.7203          | {'precision': 0.6393617021276595, 'recall': 0.7428924598269468, 'f1': 0.6872498570611778, 'number': 809}       | {'precision': 0.358974358974359, 'recall': 0.23529411764705882, 'f1': 0.28426395939086296, 'number': 119}   | {'precision': 0.6650563607085346, 'recall': 0.7755868544600939, 'f1': 0.716081491114001, 'number': 1065}     | 0.6438            | 0.7301         | 0.6842     | 0.7798           |
-| 0.5872        | 6.0   | 60   | 0.6889          | {'precision': 0.6236559139784946, 'recall': 0.788627935723115, 'f1': 0.6965065502183407, 'number': 809}        | {'precision': 0.35802469135802467, 'recall': 0.24369747899159663, 'f1': 0.29000000000000004, 'number': 119} | {'precision': 0.7190517998244074, 'recall': 0.7690140845070422, 'f1': 0.7431941923774955, 'number': 1065}    | 0.6625            | 0.7456         | 0.7016     | 0.7797           |
-| 0.5065        | 7.0   | 70   | 0.6618          | {'precision': 0.681283422459893, 'recall': 0.7873918417799752, 'f1': 0.7305045871559632, 'number': 809}        | {'precision': 0.336734693877551, 'recall': 0.2773109243697479, 'f1': 0.30414746543778803, 'number': 119}    | {'precision': 0.748471615720524, 'recall': 0.8046948356807512, 'f1': 0.7755656108597285, 'number': 1065}     | 0.7011            | 0.7662         | 0.7322     | 0.7934           |
-| 0.4527        | 8.0   | 80   | 0.6639          | {'precision': 0.671161825726141, 'recall': 0.799752781211372, 'f1': 0.7298364354201917, 'number': 809}         | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119}   | {'precision': 0.7473867595818815, 'recall': 0.8056338028169014, 'f1': 0.7754179846362403, 'number': 1065}    | 0.6908            | 0.7747         | 0.7304     | 0.7955           |
-| 0.3952        | 9.0   | 90   | 0.6666          | {'precision': 0.686358754027927, 'recall': 0.7898640296662547, 'f1': 0.7344827586206897, 'number': 809}        | {'precision': 0.3523809523809524, 'recall': 0.31092436974789917, 'f1': 0.33035714285714285, 'number': 119}  | {'precision': 0.7519247219846023, 'recall': 0.8253521126760563, 'f1': 0.7869292748433303, 'number': 1065}    | 0.7052            | 0.7802         | 0.7408     | 0.7969           |
-| 0.3863        | 10.0  | 100  | 0.6806          | {'precision': 0.6849894291754757, 'recall': 0.8009888751545118, 'f1': 0.7384615384615385, 'number': 809}       | {'precision': 0.3333333333333333, 'recall': 0.31932773109243695, 'f1': 0.3261802575107296, 'number': 119}   | {'precision': 0.7670157068062827, 'recall': 0.8253521126760563, 'f1': 0.7951153324287653, 'number': 1065}    | 0.7094            | 0.7852         | 0.7454     | 0.7985           |
-| 0.3307        | 11.0  | 110  | 0.6859          | {'precision': 0.6938775510204082, 'recall': 0.7985166872682324, 'f1': 0.7425287356321839, 'number': 809}       | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}   | {'precision': 0.764402407566638, 'recall': 0.8347417840375587, 'f1': 0.7980251346499103, 'number': 1065}     | 0.7118            | 0.7908         | 0.7492     | 0.8004           |
-| 0.3126        | 12.0  | 120  | 0.6896          | {'precision': 0.697198275862069, 'recall': 0.799752781211372, 'f1': 0.7449625791594704, 'number': 809}         | {'precision': 0.36283185840707965, 'recall': 0.3445378151260504, 'f1': 0.35344827586206895, 'number': 119}  | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}    | 0.7222            | 0.7852         | 0.7524     | 0.8012           |
-| 0.2979        | 13.0  | 130  | 0.6997          | {'precision': 0.6992399565689468, 'recall': 0.796044499381953, 'f1': 0.7445086705202313, 'number': 809}        | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}   | {'precision': 0.7763157894736842, 'recall': 0.8309859154929577, 'f1': 0.802721088435374, 'number': 1065}     | 0.7199            | 0.7878         | 0.7523     | 0.8007           |
-| 0.2712        | 14.0  | 140  | 0.7039          | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809}       | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119}                | {'precision': 0.7771929824561403, 'recall': 0.831924882629108, 'f1': 0.8036281179138323, 'number': 1065}     | 0.7230            | 0.7898         | 0.7549     | 0.8028           |
-| 0.2738        | 15.0  | 150  | 0.7055          | {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}       | {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119} | {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065}    | 0.7216            | 0.7883         | 0.7535     | 0.8028           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                          | Header                                                                                                        | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8366        | 1.0   | 10   | 1.6180          | {'precision': 0.003418803418803419, 'recall': 0.002472187886279357, 'f1': 0.0028694404591104736, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.1913214990138067, 'recall': 0.09107981220657277, 'f1': 0.12340966921119592, 'number': 1065} | 0.0907            | 0.0497         | 0.0642     | 0.3481           |
+| 1.4796        | 2.0   | 20   | 1.2677          | {'precision': 0.2348860257680872, 'recall': 0.29295426452410384, 'f1': 0.2607260726072607, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.448690728945506, 'recall': 0.5953051643192488, 'f1': 0.5117029862792575, 'number': 1065}    | 0.3596            | 0.4370         | 0.3946     | 0.6123           |
+| 1.112         | 3.0   | 30   | 0.9348          | {'precision': 0.46387832699619774, 'recall': 0.6032138442521632, 'f1': 0.5244492208490059, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                   | {'precision': 0.5677842565597667, 'recall': 0.7314553990610329, 'f1': 0.6393106278210915, 'number': 1065}   | 0.5220            | 0.6357         | 0.5733     | 0.7072           |
+| 0.8556        | 4.0   | 40   | 0.7811          | {'precision': 0.5760765550239234, 'recall': 0.7441285537700866, 'f1': 0.6494066882416398, 'number': 809}        | {'precision': 0.043478260869565216, 'recall': 0.01680672268907563, 'f1': 0.024242424242424242, 'number': 119} | {'precision': 0.6784810126582278, 'recall': 0.7549295774647887, 'f1': 0.7146666666666667, 'number': 1065}   | 0.6186            | 0.7065         | 0.6596     | 0.7636           |
+| 0.679         | 5.0   | 50   | 0.7063          | {'precision': 0.6343519494204426, 'recall': 0.7441285537700866, 'f1': 0.6848691695108078, 'number': 809}        | {'precision': 0.15294117647058825, 'recall': 0.1092436974789916, 'f1': 0.12745098039215685, 'number': 119}    | {'precision': 0.6796812749003984, 'recall': 0.8009389671361502, 'f1': 0.7353448275862068, 'number': 1065}   | 0.6413            | 0.7366         | 0.6857     | 0.7854           |
+| 0.5692        | 6.0   | 60   | 0.6788          | {'precision': 0.6491769547325102, 'recall': 0.7799752781211372, 'f1': 0.708590679393599, 'number': 809}         | {'precision': 0.26436781609195403, 'recall': 0.19327731092436976, 'f1': 0.22330097087378642, 'number': 119}   | {'precision': 0.7327510917030567, 'recall': 0.787793427230047, 'f1': 0.7592760180995476, 'number': 1065}    | 0.6774            | 0.7491         | 0.7115     | 0.7889           |
+| 0.4895        | 7.0   | 70   | 0.6565          | {'precision': 0.6697722567287785, 'recall': 0.799752781211372, 'f1': 0.7290140845070422, 'number': 809}         | {'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119}    | {'precision': 0.7526315789473684, 'recall': 0.8056338028169014, 'f1': 0.7782312925170067, 'number': 1065}   | 0.6965            | 0.7692         | 0.7310     | 0.7999           |
+| 0.441         | 8.0   | 80   | 0.6647          | {'precision': 0.6814345991561181, 'recall': 0.7985166872682324, 'f1': 0.7353443369379624, 'number': 809}        | {'precision': 0.25196850393700787, 'recall': 0.2689075630252101, 'f1': 0.2601626016260163, 'number': 119}     | {'precision': 0.7489177489177489, 'recall': 0.812206572769953, 'f1': 0.7792792792792793, 'number': 1065}    | 0.6919            | 0.7742         | 0.7308     | 0.8008           |
+| 0.3834        | 9.0   | 90   | 0.6705          | {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}        | {'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119}    | {'precision': 0.7519116397621071, 'recall': 0.8309859154929577, 'f1': 0.7894736842105263, 'number': 1065}   | 0.7079            | 0.7807         | 0.7425     | 0.8010           |
+| 0.3793        | 10.0  | 100  | 0.6591          | {'precision': 0.6965811965811965, 'recall': 0.8059332509270705, 'f1': 0.7472779369627507, 'number': 809}        | {'precision': 0.3211009174311927, 'recall': 0.29411764705882354, 'f1': 0.30701754385964913, 'number': 119}    | {'precision': 0.7831431079894644, 'recall': 0.8375586854460094, 'f1': 0.809437386569873, 'number': 1065}    | 0.7230            | 0.7923         | 0.7560     | 0.8096           |
+| 0.3189        | 11.0  | 110  | 0.6794          | {'precision': 0.6991247264770241, 'recall': 0.7898640296662547, 'f1': 0.7417295414973882, 'number': 809}        | {'precision': 0.3111111111111111, 'recall': 0.35294117647058826, 'f1': 0.33070866141732286, 'number': 119}    | {'precision': 0.779646017699115, 'recall': 0.8272300469483568, 'f1': 0.8027334851936219, 'number': 1065}    | 0.7168            | 0.7837         | 0.7488     | 0.8043           |
+| 0.3037        | 12.0  | 120  | 0.6780          | {'precision': 0.7, 'recall': 0.7873918417799752, 'f1': 0.7411285631180919, 'number': 809}                       | {'precision': 0.32558139534883723, 'recall': 0.35294117647058826, 'f1': 0.33870967741935487, 'number': 119}   | {'precision': 0.7782646801051709, 'recall': 0.8338028169014085, 'f1': 0.8050770625566637, 'number': 1065}   | 0.7188            | 0.7863         | 0.7510     | 0.8046           |
+| 0.2878        | 13.0  | 130  | 0.6864          | {'precision': 0.7065934065934066, 'recall': 0.7948084054388134, 'f1': 0.748109365910413, 'number': 809}         | {'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119}   | {'precision': 0.7889087656529516, 'recall': 0.828169014084507, 'f1': 0.8080622995877232, 'number': 1065}    | 0.7271            | 0.7863         | 0.7555     | 0.8057           |
+| 0.2626        | 14.0  | 140  | 0.6874          | {'precision': 0.7023153252480706, 'recall': 0.7873918417799752, 'f1': 0.7424242424242424, 'number': 809}        | {'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}     | {'precision': 0.7798245614035088, 'recall': 0.8347417840375587, 'f1': 0.8063492063492064, 'number': 1065}   | 0.7196            | 0.7868         | 0.7517     | 0.8056           |
+| 0.2683        | 15.0  | 150  | 0.6898          | {'precision': 0.6987951807228916, 'recall': 0.788627935723115, 'f1': 0.7409988385598142, 'number': 809}         | {'precision': 0.3157894736842105, 'recall': 0.35294117647058826, 'f1': 0.33333333333333337, 'number': 119}    | {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}   | 0.7168            | 0.7863         | 0.7499     | 0.8057           |
 
 
 ### Framework versions

logs/events.out.tfevents.1746870943.4712aaacaded.1725.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 15053
+oid sha256:f307b16e38746bc407cbba14f9515c934d0a6fc019fe9d82ef66081e20c9fb00
+size 16122

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:941e539cca7b14f196fea900c869dc1b6716e692db31651cf80108a39b115a0c
 size 450558212