End of training

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: mit
-base_model: microsoft/layoutlm-base-uncased
+base_model: pabloma09/layoutlm-with-funsd
 tags:
 - generated_from_trainer
 datasets:
@@ -16,16 +16,16 @@ should probably proofread and complete it, then remove this comment. -->
 
 # layoutlm-with-funsd
 
-This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
+This model is a fine-tuned version of [pabloma09/layoutlm-with-funsd](https://huggingface.co/pabloma09/layoutlm-with-funsd) on the funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8090
-- Eader: {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}
-- Nswer: {'precision': 0.3763440860215054, 'recall': 0.5, 'f1': 0.4294478527607362, 'number': 70}
-- Uestion: {'precision': 0.3368421052631579, 'recall': 0.41025641025641024, 'f1': 0.3699421965317919, 'number': 78}
-- Overall Precision: 0.3541
-- Overall Recall: 0.4111
-- Overall F1: 0.3805
-- Overall Accuracy: 0.7559
+- Loss: 0.6344
+- Eader: {'precision': 0.4888888888888889, 'recall': 0.38596491228070173, 'f1': 0.4313725490196078, 'number': 57}
+- Nswer: {'precision': 0.577922077922078, 'recall': 0.6312056737588653, 'f1': 0.6033898305084746, 'number': 141}
+- Uestion: {'precision': 0.5172413793103449, 'recall': 0.5590062111801242, 'f1': 0.537313432835821, 'number': 161}
+- Overall Precision: 0.5389
+- Overall Recall: 0.5599
+- Overall F1: 0.5492
+- Overall Accuracy: 0.8364
 
 ## Model description
 
@@ -55,23 +55,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                         | Nswer                                                                                                      | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.2801        | 1.0   | 9    | 1.0648          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.07246376811594203, 'recall': 0.21428571428571427, 'f1': 0.10830324909747292, 'number': 70} | {'precision': 0.08292682926829269, 'recall': 0.21794871794871795, 'f1': 0.12014134275618374, 'number': 78} | 0.0777            | 0.1778         | 0.1081     | 0.6088           |
-| 0.9803        | 2.0   | 18   | 0.8556          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.1875, 'recall': 0.38571428571428573, 'f1': 0.25233644859813087, 'number': 70}              | {'precision': 0.1259259259259259, 'recall': 0.21794871794871795, 'f1': 0.1596244131455399, 'number': 78}   | 0.1577            | 0.2444         | 0.1917     | 0.7064           |
-| 0.769         | 3.0   | 27   | 0.6782          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 32}                                    | {'precision': 0.29591836734693877, 'recall': 0.4142857142857143, 'f1': 0.34523809523809523, 'number': 70}  | {'precision': 0.3333333333333333, 'recall': 0.38461538461538464, 'f1': 0.3571428571428571, 'number': 78}   | 0.2995            | 0.3278         | 0.3130     | 0.7740           |
-| 0.6082        | 4.0   | 36   | 0.6412          | {'precision': 0.2, 'recall': 0.125, 'f1': 0.15384615384615385, 'number': 32}                  | {'precision': 0.3333333333333333, 'recall': 0.4714285714285714, 'f1': 0.3905325443786982, 'number': 70}    | {'precision': 0.367816091954023, 'recall': 0.41025641025641024, 'f1': 0.3878787878787879, 'number': 78}    | 0.3350            | 0.3833         | 0.3575     | 0.7655           |
-| 0.5047        | 5.0   | 45   | 0.7447          | {'precision': 0.42105263157894735, 'recall': 0.25, 'f1': 0.3137254901960784, 'number': 32}    | {'precision': 0.32978723404255317, 'recall': 0.44285714285714284, 'f1': 0.3780487804878049, 'number': 70}  | {'precision': 0.36363636363636365, 'recall': 0.41025641025641024, 'f1': 0.3855421686746988, 'number': 78}  | 0.3532            | 0.3944         | 0.3727     | 0.7275           |
-| 0.422         | 6.0   | 54   | 0.6465          | {'precision': 0.2857142857142857, 'recall': 0.1875, 'f1': 0.22641509433962265, 'number': 32}  | {'precision': 0.43902439024390244, 'recall': 0.5142857142857142, 'f1': 0.4736842105263158, 'number': 70}   | {'precision': 0.46153846153846156, 'recall': 0.46153846153846156, 'f1': 0.46153846153846156, 'number': 78} | 0.4309            | 0.4333         | 0.4321     | 0.7951           |
-| 0.3607        | 7.0   | 63   | 0.7246          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.3953488372093023, 'recall': 0.4857142857142857, 'f1': 0.43589743589743585, 'number': 70}   | {'precision': 0.4069767441860465, 'recall': 0.44871794871794873, 'f1': 0.4268292682926829, 'number': 78}   | 0.3979            | 0.4222         | 0.4097     | 0.7559           |
-| 0.3106        | 8.0   | 72   | 0.7467          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.38202247191011235, 'recall': 0.4857142857142857, 'f1': 0.42767295597484273, 'number': 70}  | {'precision': 0.3333333333333333, 'recall': 0.41025641025641024, 'f1': 0.36781609195402293, 'number': 78}  | 0.3527            | 0.4056         | 0.3773     | 0.7288           |
-| 0.2649        | 9.0   | 81   | 0.7238          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.3953488372093023, 'recall': 0.4857142857142857, 'f1': 0.43589743589743585, 'number': 70}   | {'precision': 0.3404255319148936, 'recall': 0.41025641025641024, 'f1': 0.37209302325581395, 'number': 78}  | 0.3632            | 0.4056         | 0.3832     | 0.7758           |
-| 0.239         | 10.0  | 90   | 0.8137          | {'precision': 0.30434782608695654, 'recall': 0.21875, 'f1': 0.2545454545454546, 'number': 32} | {'precision': 0.4069767441860465, 'recall': 0.5, 'f1': 0.4487179487179487, 'number': 70}                   | {'precision': 0.37209302325581395, 'recall': 0.41025641025641024, 'f1': 0.3902439024390244, 'number': 78}  | 0.3795            | 0.4111         | 0.3947     | 0.7288           |
-| 0.2141        | 11.0  | 99   | 0.7518          | {'precision': 0.25, 'recall': 0.1875, 'f1': 0.21428571428571427, 'number': 32}                | {'precision': 0.3645833333333333, 'recall': 0.5, 'f1': 0.42168674698795183, 'number': 70}                  | {'precision': 0.31958762886597936, 'recall': 0.3974358974358974, 'f1': 0.3542857142857143, 'number': 78}   | 0.3318            | 0.4            | 0.3627     | 0.7589           |
-| 0.1978        | 12.0  | 108  | 0.8165          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.41975308641975306, 'recall': 0.4857142857142857, 'f1': 0.4503311258278146, 'number': 70}   | {'precision': 0.37209302325581395, 'recall': 0.41025641025641024, 'f1': 0.3902439024390244, 'number': 78}  | 0.3883            | 0.4056         | 0.3967     | 0.7438           |
-| 0.1807        | 13.0  | 117  | 0.7946          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.358695652173913, 'recall': 0.4714285714285714, 'f1': 0.4074074074074074, 'number': 70}     | {'precision': 0.31958762886597936, 'recall': 0.3974358974358974, 'f1': 0.3542857142857143, 'number': 78}   | 0.3365            | 0.3944         | 0.3632     | 0.7565           |
-| 0.1705        | 14.0  | 126  | 0.8007          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.3684210526315789, 'recall': 0.5, 'f1': 0.4242424242424242, 'number': 70}                   | {'precision': 0.32989690721649484, 'recall': 0.41025641025641024, 'f1': 0.36571428571428566, 'number': 78} | 0.3507            | 0.4111         | 0.3785     | 0.7601           |
-| 0.1676        | 15.0  | 135  | 0.8090          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.3763440860215054, 'recall': 0.5, 'f1': 0.4294478527607362, 'number': 70}                   | {'precision': 0.3368421052631579, 'recall': 0.41025641025641024, 'f1': 0.3699421965317919, 'number': 78}   | 0.3541            | 0.4111         | 0.3805     | 0.7559           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                      | Nswer                                                                                                    | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3894        | 1.0   | 9    | 0.5238          | {'precision': 0.34782608695652173, 'recall': 0.2807017543859649, 'f1': 0.3106796116504854, 'number': 57}   | {'precision': 0.515527950310559, 'recall': 0.5886524822695035, 'f1': 0.5496688741721855, 'number': 141}  | {'precision': 0.4010989010989011, 'recall': 0.453416149068323, 'f1': 0.4256559766763849, 'number': 161}    | 0.4422            | 0.4791         | 0.4599     | 0.8174           |
+| 0.3489        | 2.0   | 18   | 0.5037          | {'precision': 0.2978723404255319, 'recall': 0.24561403508771928, 'f1': 0.2692307692307692, 'number': 57}   | {'precision': 0.5125, 'recall': 0.5815602836879432, 'f1': 0.5448504983388704, 'number': 141}             | {'precision': 0.4, 'recall': 0.4472049689440994, 'f1': 0.42228739002932547, 'number': 161}                 | 0.4341            | 0.4680         | 0.4504     | 0.8270           |
+| 0.2657        | 3.0   | 27   | 0.5258          | {'precision': 0.3333333333333333, 'recall': 0.2807017543859649, 'f1': 0.3047619047619048, 'number': 57}    | {'precision': 0.5123456790123457, 'recall': 0.5886524822695035, 'f1': 0.5478547854785478, 'number': 141} | {'precision': 0.3901098901098901, 'recall': 0.4409937888198758, 'f1': 0.4139941690962099, 'number': 161}   | 0.4337            | 0.4735         | 0.4527     | 0.8261           |
+| 0.1907        | 4.0   | 36   | 0.5390          | {'precision': 0.38461538461538464, 'recall': 0.2631578947368421, 'f1': 0.3125, 'number': 57}               | {'precision': 0.5827814569536424, 'recall': 0.624113475177305, 'f1': 0.6027397260273973, 'number': 141}  | {'precision': 0.47878787878787876, 'recall': 0.4906832298136646, 'f1': 0.48466257668711654, 'number': 161} | 0.5127            | 0.5070         | 0.5098     | 0.8286           |
+| 0.175         | 5.0   | 45   | 0.5489          | {'precision': 0.42105263157894735, 'recall': 0.2807017543859649, 'f1': 0.3368421052631579, 'number': 57}   | {'precision': 0.5246913580246914, 'recall': 0.6028368794326241, 'f1': 0.561056105610561, 'number': 141}  | {'precision': 0.449438202247191, 'recall': 0.4968944099378882, 'f1': 0.471976401179941, 'number': 161}     | 0.4788            | 0.5042         | 0.4912     | 0.8361           |
+| 0.1685        | 6.0   | 54   | 0.5678          | {'precision': 0.4, 'recall': 0.2807017543859649, 'f1': 0.32989690721649484, 'number': 57}                  | {'precision': 0.5769230769230769, 'recall': 0.6382978723404256, 'f1': 0.6060606060606061, 'number': 141} | {'precision': 0.45901639344262296, 'recall': 0.5217391304347826, 'f1': 0.4883720930232558, 'number': 161}  | 0.5013            | 0.5292         | 0.5149     | 0.8370           |
+| 0.1156        | 7.0   | 63   | 0.5749          | {'precision': 0.4864864864864865, 'recall': 0.3157894736842105, 'f1': 0.3829787234042553, 'number': 57}    | {'precision': 0.50920245398773, 'recall': 0.5886524822695035, 'f1': 0.5460526315789473, 'number': 141}   | {'precision': 0.43575418994413406, 'recall': 0.484472049689441, 'f1': 0.45882352941176474, 'number': 161}  | 0.4723            | 0.4986         | 0.4851     | 0.8409           |
+| 0.1019        | 8.0   | 72   | 0.5907          | {'precision': 0.43137254901960786, 'recall': 0.38596491228070173, 'f1': 0.40740740740740744, 'number': 57} | {'precision': 0.5408805031446541, 'recall': 0.6099290780141844, 'f1': 0.5733333333333333, 'number': 141} | {'precision': 0.5113636363636364, 'recall': 0.5590062111801242, 'f1': 0.5341246290801187, 'number': 161}   | 0.5130            | 0.5515         | 0.5315     | 0.8337           |
+| 0.0885        | 9.0   | 81   | 0.5899          | {'precision': 0.5, 'recall': 0.43859649122807015, 'f1': 0.46728971962616817, 'number': 57}                 | {'precision': 0.55, 'recall': 0.624113475177305, 'f1': 0.584717607973422, 'number': 141}                 | {'precision': 0.5084745762711864, 'recall': 0.5590062111801242, 'f1': 0.5325443786982249, 'number': 161}   | 0.5245            | 0.5655         | 0.5442     | 0.8400           |
+| 0.0852        | 10.0  | 90   | 0.6170          | {'precision': 0.45454545454545453, 'recall': 0.3508771929824561, 'f1': 0.396039603960396, 'number': 57}    | {'precision': 0.564935064935065, 'recall': 0.6170212765957447, 'f1': 0.5898305084745763, 'number': 141}  | {'precision': 0.5027932960893855, 'recall': 0.5590062111801242, 'f1': 0.5294117647058824, 'number': 161}   | 0.5225            | 0.5487         | 0.5353     | 0.8364           |
+| 0.0854        | 11.0  | 99   | 0.6107          | {'precision': 0.5111111111111111, 'recall': 0.40350877192982454, 'f1': 0.45098039215686275, 'number': 57}  | {'precision': 0.5506329113924051, 'recall': 0.6170212765957447, 'f1': 0.5819397993311038, 'number': 141} | {'precision': 0.5113636363636364, 'recall': 0.5590062111801242, 'f1': 0.5341246290801187, 'number': 161}   | 0.5277            | 0.5571         | 0.5420     | 0.8358           |
+| 0.0665        | 12.0  | 108  | 0.6090          | {'precision': 0.5111111111111111, 'recall': 0.40350877192982454, 'f1': 0.45098039215686275, 'number': 57}  | {'precision': 0.5365853658536586, 'recall': 0.624113475177305, 'f1': 0.5770491803278689, 'number': 141}  | {'precision': 0.4946236559139785, 'recall': 0.5714285714285714, 'f1': 0.5302593659942363, 'number': 161}   | 0.5139            | 0.5655         | 0.5385     | 0.8464           |
+| 0.0632        | 13.0  | 117  | 0.6200          | {'precision': 0.44680851063829785, 'recall': 0.3684210526315789, 'f1': 0.40384615384615385, 'number': 57}  | {'precision': 0.5370370370370371, 'recall': 0.6170212765957447, 'f1': 0.5742574257425743, 'number': 141} | {'precision': 0.4945054945054945, 'recall': 0.5590062111801242, 'f1': 0.5247813411078717, 'number': 161}   | 0.5064            | 0.5515         | 0.528      | 0.8412           |
+| 0.0758        | 14.0  | 126  | 0.6326          | {'precision': 0.5, 'recall': 0.38596491228070173, 'f1': 0.43564356435643564, 'number': 57}                 | {'precision': 0.5705128205128205, 'recall': 0.6312056737588653, 'f1': 0.5993265993265993, 'number': 141} | {'precision': 0.5142857142857142, 'recall': 0.5590062111801242, 'f1': 0.5357142857142856, 'number': 161}   | 0.536             | 0.5599         | 0.5477     | 0.8382           |
+| 0.0573        | 15.0  | 135  | 0.6344          | {'precision': 0.4888888888888889, 'recall': 0.38596491228070173, 'f1': 0.4313725490196078, 'number': 57}   | {'precision': 0.577922077922078, 'recall': 0.6312056737588653, 'f1': 0.6033898305084746, 'number': 141}  | {'precision': 0.5172413793103449, 'recall': 0.5590062111801242, 'f1': 0.537313432835821, 'number': 161}    | 0.5389            | 0.5599         | 0.5492     | 0.8364           |
 
 
 ### Framework versions

logs/events.out.tfevents.1741101046.DESKTOP-HA84SVN.2309656.4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:36bb8c2b571b30c351150b5f8dfcdb5c61c4355ca2ed45c2b3a8fccdcdae079f
-size 15094
+oid sha256:c4f973591ec3b75ead46f157b3497974880eeef7174aba0967437dea0972bc65
+size 16163

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ae50dd754eb0dd412b9c201a2dd4bcce937a884a4c4a429424048f5d8b043bc0
+oid sha256:31a3bcedd1b501a277f2e4ebf39b7eb3e8de16354e2911ebf63e463291993c68
 size 450548984