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: 0.6718
-- Answer: {'precision': 0.7074235807860262, 'recall': 0.8009888751545118, 'f1': 0.7513043478260869, 'number': 809}
-- Header: {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}
-- Question: {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}
-- Overall Precision: 0.7242
-- Overall Recall: 0.7852
-- Overall F1: 0.7535
-- Overall Accuracy: 0.8120
+- Loss: 0.6932
+- Answer: {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}
+- Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
+- Question: {'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065}
+- Overall Precision: 0.7093
+- Overall Recall: 0.7822
+- Overall F1: 0.7440
+- Overall Accuracy: 0.8018
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.8183        | 1.0   | 10   | 1.6239          | {'precision': 0.010256410256410256, 'recall': 0.004944375772558714, 'f1': 0.006672226855713093, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.18360655737704917, 'recall': 0.05258215962441314, 'f1': 0.08175182481751825, 'number': 1065} | 0.0863            | 0.0301         | 0.0446     | 0.3196           |
-| 1.4789        | 2.0   | 20   | 1.2907          | {'precision': 0.12058465286236297, 'recall': 0.12237330037082818, 'f1': 0.12147239263803682, 'number': 809}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.4493717664449372, 'recall': 0.5708920187793427, 'f1': 0.5028949545078577, 'number': 1065}    | 0.3252            | 0.3547         | 0.3393     | 0.5843           |
-| 1.139         | 3.0   | 30   | 0.9533          | {'precision': 0.4473409801876955, 'recall': 0.5302843016069221, 'f1': 0.4852941176470588, 'number': 809}       | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.5831399845320959, 'recall': 0.707981220657277, 'f1': 0.6395250212044105, 'number': 1065}     | 0.5232            | 0.5936         | 0.5562     | 0.7090           |
-| 0.8802        | 4.0   | 40   | 0.7961          | {'precision': 0.5869565217391305, 'recall': 0.7676143386897404, 'f1': 0.6652383502945903, 'number': 809}       | {'precision': 0.05714285714285714, 'recall': 0.01680672268907563, 'f1': 0.025974025974025972, 'number': 119} | {'precision': 0.6845878136200717, 'recall': 0.7173708920187793, 'f1': 0.7005960568546539, 'number': 1065}    | 0.6279            | 0.6959         | 0.6602     | 0.7604           |
-| 0.6957        | 5.0   | 50   | 0.7201          | {'precision': 0.6137040714995035, 'recall': 0.7639060568603214, 'f1': 0.6806167400881058, 'number': 809}       | {'precision': 0.18518518518518517, 'recall': 0.12605042016806722, 'f1': 0.15, 'number': 119}                 | {'precision': 0.6730769230769231, 'recall': 0.7887323943661971, 'f1': 0.7263294422827496, 'number': 1065}    | 0.6306            | 0.7391         | 0.6805     | 0.7774           |
-| 0.5881        | 6.0   | 60   | 0.6950          | {'precision': 0.6294820717131474, 'recall': 0.7812113720642769, 'f1': 0.6971869829012686, 'number': 809}       | {'precision': 0.23684210526315788, 'recall': 0.15126050420168066, 'f1': 0.1846153846153846, 'number': 119}   | {'precision': 0.7171453437771975, 'recall': 0.7737089201877935, 'f1': 0.7443541102077688, 'number': 1065}    | 0.6613            | 0.7396         | 0.6982     | 0.7890           |
-| 0.5129        | 7.0   | 70   | 0.6612          | {'precision': 0.6733615221987315, 'recall': 0.7873918417799752, 'f1': 0.7259259259259259, 'number': 809}       | {'precision': 0.25, 'recall': 0.24369747899159663, 'f1': 0.24680851063829787, 'number': 119}                 | {'precision': 0.7306052855924978, 'recall': 0.8046948356807512, 'f1': 0.7658623771224308, 'number': 1065}    | 0.6814            | 0.7642         | 0.7204     | 0.8005           |
-| 0.4565        | 8.0   | 80   | 0.6582          | {'precision': 0.6840458811261731, 'recall': 0.8108776266996292, 'f1': 0.7420814479638008, 'number': 809}       | {'precision': 0.28846153846153844, 'recall': 0.25210084033613445, 'f1': 0.26905829596412556, 'number': 119}  | {'precision': 0.7543859649122807, 'recall': 0.8075117370892019, 'f1': 0.780045351473923, 'number': 1065}     | 0.7018            | 0.7757         | 0.7369     | 0.8050           |
-| 0.4033        | 9.0   | 90   | 0.6490          | {'precision': 0.7037037037037037, 'recall': 0.7985166872682324, 'f1': 0.7481181239143023, 'number': 809}       | {'precision': 0.30357142857142855, 'recall': 0.2857142857142857, 'f1': 0.2943722943722944, 'number': 119}    | {'precision': 0.7824116047144152, 'recall': 0.8103286384976526, 'f1': 0.7961254612546125, 'number': 1065}    | 0.7234            | 0.7742         | 0.7479     | 0.8103           |
-| 0.3954        | 10.0  | 100  | 0.6486          | {'precision': 0.7044711014176663, 'recall': 0.7985166872682324, 'f1': 0.7485515643105446, 'number': 809}       | {'precision': 0.3185840707964602, 'recall': 0.3025210084033613, 'f1': 0.3103448275862069, 'number': 119}     | {'precision': 0.7813333333333333, 'recall': 0.8253521126760563, 'f1': 0.8027397260273973, 'number': 1065}    | 0.7244            | 0.7832         | 0.7527     | 0.8159           |
-| 0.3404        | 11.0  | 110  | 0.6524          | {'precision': 0.7085152838427947, 'recall': 0.8022249690976514, 'f1': 0.7524637681159421, 'number': 809}       | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119}   | {'precision': 0.7857142857142857, 'recall': 0.8262910798122066, 'f1': 0.8054919908466819, 'number': 1065}    | 0.7263            | 0.7858         | 0.7549     | 0.8134           |
-| 0.3191        | 12.0  | 120  | 0.6599          | {'precision': 0.7135016465422612, 'recall': 0.8034610630407911, 'f1': 0.7558139534883722, 'number': 809}       | {'precision': 0.3025210084033613, 'recall': 0.3025210084033613, 'f1': 0.3025210084033613, 'number': 119}     | {'precision': 0.7857785778577858, 'recall': 0.819718309859155, 'f1': 0.8023897058823529, 'number': 1065}     | 0.7282            | 0.7822         | 0.7542     | 0.8138           |
-| 0.3046        | 13.0  | 130  | 0.6726          | {'precision': 0.7176339285714286, 'recall': 0.7948084054388134, 'f1': 0.7542521994134898, 'number': 809}       | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}  | {'precision': 0.7784642541924095, 'recall': 0.828169014084507, 'f1': 0.802547770700637, 'number': 1065}      | 0.7262            | 0.7852         | 0.7546     | 0.8159           |
-| 0.2839        | 14.0  | 140  | 0.6710          | {'precision': 0.7043478260869566, 'recall': 0.8009888751545118, 'f1': 0.7495662232504339, 'number': 809}       | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}  | {'precision': 0.7802491103202847, 'recall': 0.8234741784037559, 'f1': 0.801279122887163, 'number': 1065}     | 0.7212            | 0.7852         | 0.7519     | 0.8139           |
-| 0.2826        | 15.0  | 150  | 0.6718          | {'precision': 0.7074235807860262, 'recall': 0.8009888751545118, 'f1': 0.7513043478260869, 'number': 809}       | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}  | {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}    | 0.7242            | 0.7852         | 0.7535     | 0.8120           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                                                                       | Question                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8301        | 1.0   | 10   | 1.5866          | {'precision': 0.006765899864682003, 'recall': 0.006180469715698393, 'f1': 0.006459948320413437, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.2246153846153846, 'recall': 0.13708920187793427, 'f1': 0.17026239067055393, 'number': 1065} | 0.1087            | 0.0758         | 0.0893     | 0.3526           |
+| 1.4768        | 2.0   | 20   | 1.2757          | {'precision': 0.280557834290402, 'recall': 0.4227441285537701, 'f1': 0.3372781065088757, 'number': 809}        | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}                                                  | {'precision': 0.3888491779842745, 'recall': 0.5107981220657277, 'f1': 0.44155844155844154, 'number': 1065}  | 0.3380            | 0.4446         | 0.3840     | 0.6011           |
+| 1.1406        | 3.0   | 30   | 0.9524          | {'precision': 0.46350710900473935, 'recall': 0.6044499381953028, 'f1': 0.5246781115879828, 'number': 809}      | {'precision': 0.06382978723404255, 'recall': 0.025210084033613446, 'f1': 0.03614457831325301, 'number': 119} | {'precision': 0.53671875, 'recall': 0.6450704225352113, 'f1': 0.5859275053304905, 'number': 1065}           | 0.4950            | 0.5916         | 0.5390     | 0.6937           |
+| 0.8606        | 4.0   | 40   | 0.7865          | {'precision': 0.5620437956204379, 'recall': 0.761433868974042, 'f1': 0.6467191601049869, 'number': 809}        | {'precision': 0.16666666666666666, 'recall': 0.10084033613445378, 'f1': 0.1256544502617801, 'number': 119}   | {'precision': 0.6464285714285715, 'recall': 0.67981220657277, 'f1': 0.662700228832952, 'number': 1065}      | 0.5909            | 0.6784         | 0.6316     | 0.7552           |
+| 0.6873        | 5.0   | 50   | 0.7157          | {'precision': 0.6341719077568134, 'recall': 0.7478368355995055, 'f1': 0.6863301191151445, 'number': 809}       | {'precision': 0.375, 'recall': 0.25210084033613445, 'f1': 0.3015075376884422, 'number': 119}                 | {'precision': 0.6704730831973899, 'recall': 0.7718309859154929, 'f1': 0.7175905718027062, 'number': 1065}   | 0.6447            | 0.7311         | 0.6852     | 0.7767           |
+| 0.5888        | 6.0   | 60   | 0.6909          | {'precision': 0.6243949661181026, 'recall': 0.7972805933250927, 'f1': 0.7003257328990228, 'number': 809}       | {'precision': 0.35064935064935066, 'recall': 0.226890756302521, 'f1': 0.2755102040816326, 'number': 119}     | {'precision': 0.7193923145665773, 'recall': 0.755868544600939, 'f1': 0.7371794871794871, 'number': 1065}    | 0.6626            | 0.7411         | 0.6997     | 0.7806           |
+| 0.5097        | 7.0   | 70   | 0.6576          | {'precision': 0.6656050955414012, 'recall': 0.7750309023485785, 'f1': 0.7161621930325527, 'number': 809}       | {'precision': 0.32323232323232326, 'recall': 0.2689075630252101, 'f1': 0.29357798165137616, 'number': 119}   | {'precision': 0.7382198952879581, 'recall': 0.7943661971830986, 'f1': 0.7652645861601085, 'number': 1065}   | 0.6882            | 0.7551         | 0.7201     | 0.7963           |
+| 0.4507        | 8.0   | 80   | 0.6668          | {'precision': 0.6615698267074414, 'recall': 0.8022249690976514, 'f1': 0.7251396648044692, 'number': 809}       | {'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}    | {'precision': 0.7389380530973452, 'recall': 0.784037558685446, 'f1': 0.7608200455580865, 'number': 1065}    | 0.6809            | 0.7612         | 0.7188     | 0.7909           |
+| 0.3998        | 9.0   | 90   | 0.6639          | {'precision': 0.6715481171548117, 'recall': 0.7935723114956736, 'f1': 0.7274787535410764, 'number': 809}       | {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}     | {'precision': 0.7542448614834674, 'recall': 0.7924882629107981, 'f1': 0.7728937728937729, 'number': 1065}   | 0.6950            | 0.7637         | 0.7277     | 0.7942           |
+| 0.3899        | 10.0  | 100  | 0.6686          | {'precision': 0.6840981856990395, 'recall': 0.792336217552534, 'f1': 0.734249713631157, 'number': 809}         | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119}  | {'precision': 0.752828546562228, 'recall': 0.812206572769953, 'f1': 0.7813911472448057, 'number': 1065}     | 0.6998            | 0.7742         | 0.7351     | 0.7987           |
+| 0.3345        | 11.0  | 110  | 0.6688          | {'precision': 0.6878980891719745, 'recall': 0.8009888751545118, 'f1': 0.7401484865790977, 'number': 809}       | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119}   | {'precision': 0.7567332754126846, 'recall': 0.8178403755868544, 'f1': 0.7861010830324908, 'number': 1065}   | 0.7028            | 0.7817         | 0.7401     | 0.8019           |
+| 0.3227        | 12.0  | 120  | 0.6747          | {'precision': 0.6944444444444444, 'recall': 0.8034610630407911, 'f1': 0.7449856733524356, 'number': 809}       | {'precision': 0.35714285714285715, 'recall': 0.33613445378151263, 'f1': 0.34632034632034636, 'number': 119}  | {'precision': 0.7703306523681859, 'recall': 0.8093896713615023, 'f1': 0.7893772893772893, 'number': 1065}   | 0.7162            | 0.7787         | 0.7462     | 0.8047           |
+| 0.3068        | 13.0  | 130  | 0.6875          | {'precision': 0.6957470010905126, 'recall': 0.788627935723115, 'f1': 0.7392815758980301, 'number': 809}        | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119}    | {'precision': 0.7596899224806202, 'recall': 0.828169014084507, 'f1': 0.7924528301886793, 'number': 1065}    | 0.7083            | 0.7832         | 0.7439     | 0.8024           |
+| 0.2826        | 14.0  | 140  | 0.6897          | {'precision': 0.6963519313304721, 'recall': 0.8022249690976514, 'f1': 0.7455485353245261, 'number': 809}       | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119}    | {'precision': 0.7651183172655566, 'recall': 0.819718309859155, 'f1': 0.7914777878513146, 'number': 1065}    | 0.7113            | 0.7837         | 0.7458     | 0.8007           |
+| 0.2785        | 15.0  | 150  | 0.6932          | {'precision': 0.6896186440677966, 'recall': 0.8046971569839307, 'f1': 0.7427267541357673, 'number': 809}       | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}   | {'precision': 0.766107678729038, 'recall': 0.8150234741784037, 'f1': 0.7898089171974523, 'number': 1065}    | 0.7093            | 0.7822         | 0.7440     | 0.8018           |
 
 
 ### Framework versions

logs/events.out.tfevents.1723638587.venkanna-vm01.1122146.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fe0ad5d5722c1c2e156146f4d4c584fe5ef8543333b87f55ea556450d7fa370
+size 16080

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:18880c5342a42aeada6e89ea4d0a373d5a14a78965a255f372538403d07f7b12
+oid sha256:0db9d6c86eb1bca9a4c4707a8a157e72c7d5c983e2e1c0326451213cf4a34c68
 size 450558212