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 [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.5541
-- Answer: {'precision': 0.8696655132641292, 'recall': 0.9228886168910648, 'f1': 0.8954869358669834, 'number': 817}
-- Header: {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}
-- Question: {'precision': 0.9031963470319635, 'recall': 0.9182915506035283, 'f1': 0.9106813996316759, 'number': 1077}
-- Overall Precision: 0.8779
+- Loss: 1.5815
+- Answer: {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817}
+- Header: {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119}
+- Question: {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077}
+- Overall Precision: 0.8745
 - Overall Recall: 0.8962
-- Overall F1: 0.8869
-- Overall Accuracy: 0.8228
+- Overall F1: 0.8852
+- Overall Accuracy: 0.8209
 
 ## Model description
 
@@ -54,20 +54,20 @@ 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 |
-|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4364        | 10.53  | 200  | 0.9844          | {'precision': 0.834106728538283, 'recall': 0.8800489596083231, 'f1': 0.8564621798689696, 'number': 817}  | {'precision': 0.41935483870967744, 'recall': 0.6554621848739496, 'f1': 0.5114754098360655, 'number': 119} | {'precision': 0.8516833484986351, 'recall': 0.8690807799442897, 'f1': 0.860294117647059, 'number': 1077}  | 0.8072            | 0.8609         | 0.8332     | 0.7792           |
-| 0.0425        | 21.05  | 400  | 1.1086          | {'precision': 0.8603550295857988, 'recall': 0.8898408812729498, 'f1': 0.8748495788206979, 'number': 817} | {'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119}  | {'precision': 0.8763250883392226, 'recall': 0.9210770659238626, 'f1': 0.8981439565414214, 'number': 1077} | 0.8582            | 0.8843         | 0.8711     | 0.8199           |
-| 0.0132        | 31.58  | 600  | 1.3613          | {'precision': 0.8785276073619632, 'recall': 0.8763769889840881, 'f1': 0.8774509803921567, 'number': 817} | {'precision': 0.5182481751824818, 'recall': 0.5966386554621849, 'f1': 0.5546875, 'number': 119}           | {'precision': 0.8656195462478184, 'recall': 0.9210770659238626, 'f1': 0.8924876293297346, 'number': 1077} | 0.8480            | 0.8838         | 0.8655     | 0.8090           |
-| 0.0061        | 42.11  | 800  | 1.5515          | {'precision': 0.8825, 'recall': 0.8641370869033048, 'f1': 0.8732220160791588, 'number': 817}             | {'precision': 0.5689655172413793, 'recall': 0.5546218487394958, 'f1': 0.5617021276595745, 'number': 119}  | {'precision': 0.8888888888888888, 'recall': 0.8987929433611885, 'f1': 0.8938134810710988, 'number': 1077} | 0.8678            | 0.8644         | 0.8661     | 0.7964           |
-| 0.0041        | 52.63  | 1000 | 1.5132          | {'precision': 0.8808664259927798, 'recall': 0.8959608323133414, 'f1': 0.8883495145631067, 'number': 817} | {'precision': 0.6296296296296297, 'recall': 0.5714285714285714, 'f1': 0.5991189427312775, 'number': 119}  | {'precision': 0.8793256433007985, 'recall': 0.9201485608170845, 'f1': 0.8992740471869328, 'number': 1077} | 0.8669            | 0.8897         | 0.8782     | 0.8021           |
-| 0.0023        | 63.16  | 1200 | 1.6099          | {'precision': 0.8483466362599772, 'recall': 0.9106487148102815, 'f1': 0.8783943329397875, 'number': 817} | {'precision': 0.6470588235294118, 'recall': 0.46218487394957986, 'f1': 0.5392156862745099, 'number': 119} | {'precision': 0.8795718108831401, 'recall': 0.9155060352831941, 'f1': 0.897179253867152, 'number': 1077}  | 0.8569            | 0.8867         | 0.8716     | 0.8007           |
-| 0.0013        | 73.68  | 1400 | 1.5668          | {'precision': 0.8819277108433735, 'recall': 0.8959608323133414, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.5775862068965517, 'recall': 0.5630252100840336, 'f1': 0.5702127659574467, 'number': 119}  | {'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077} | 0.8728            | 0.8828         | 0.8777     | 0.8079           |
-| 0.0009        | 84.21  | 1600 | 1.7323          | {'precision': 0.8639534883720931, 'recall': 0.9094247246022031, 'f1': 0.8861061419200955, 'number': 817} | {'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119}  | {'precision': 0.8962962962962963, 'recall': 0.8987929433611885, 'f1': 0.8975428836346777, 'number': 1077} | 0.8682            | 0.8803         | 0.8742     | 0.8078           |
-| 0.0008        | 94.74  | 1800 | 1.5326          | {'precision': 0.8741258741258742, 'recall': 0.9179926560587516, 'f1': 0.8955223880597015, 'number': 817} | {'precision': 0.6226415094339622, 'recall': 0.5546218487394958, 'f1': 0.5866666666666668, 'number': 119}  | {'precision': 0.9044117647058824, 'recall': 0.9136490250696379, 'f1': 0.9090069284064666, 'number': 1077} | 0.8772            | 0.8942         | 0.8856     | 0.8208           |
-| 0.0003        | 105.26 | 2000 | 1.5560          | {'precision': 0.8625429553264605, 'recall': 0.9216646266829865, 'f1': 0.8911242603550296, 'number': 817} | {'precision': 0.616822429906542, 'recall': 0.5546218487394958, 'f1': 0.5840707964601769, 'number': 119}   | {'precision': 0.9008264462809917, 'recall': 0.9108635097493036, 'f1': 0.9058171745152355, 'number': 1077} | 0.8700            | 0.8942         | 0.8819     | 0.8189           |
-| 0.0002        | 115.79 | 2200 | 1.5541          | {'precision': 0.8696655132641292, 'recall': 0.9228886168910648, 'f1': 0.8954869358669834, 'number': 817} | {'precision': 0.6559139784946236, 'recall': 0.5126050420168067, 'f1': 0.5754716981132076, 'number': 119}  | {'precision': 0.9031963470319635, 'recall': 0.9182915506035283, 'f1': 0.9106813996316759, 'number': 1077} | 0.8779            | 0.8962         | 0.8869     | 0.8228           |
-| 0.0002        | 126.32 | 2400 | 1.5664          | {'precision': 0.8670520231213873, 'recall': 0.9179926560587516, 'f1': 0.89179548156956, 'number': 817}   | {'precision': 0.6595744680851063, 'recall': 0.5210084033613446, 'f1': 0.5821596244131456, 'number': 119}  | {'precision': 0.9135687732342007, 'recall': 0.9127205199628597, 'f1': 0.913144449605202, 'number': 1077}  | 0.8821            | 0.8917         | 0.8869     | 0.8262           |
+| Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                   | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4131        | 10.53  | 200  | 0.9920          | {'precision': 0.7944444444444444, 'recall': 0.8751529987760098, 'f1': 0.8328479906814211, 'number': 817} | {'precision': 0.5267857142857143, 'recall': 0.4957983193277311, 'f1': 0.5108225108225107, 'number': 119} | {'precision': 0.8690265486725663, 'recall': 0.9117920148560817, 'f1': 0.8898957861350248, 'number': 1077} | 0.8198            | 0.8723         | 0.8452     | 0.7912           |
+| 0.0453        | 21.05  | 400  | 1.3055          | {'precision': 0.8215077605321508, 'recall': 0.9069767441860465, 'f1': 0.8621291448516578, 'number': 817} | {'precision': 0.5961538461538461, 'recall': 0.5210084033613446, 'f1': 0.5560538116591929, 'number': 119} | {'precision': 0.8818755635707844, 'recall': 0.9080779944289693, 'f1': 0.8947849954254347, 'number': 1077} | 0.8421            | 0.8847         | 0.8629     | 0.7971           |
+| 0.0129        | 31.58  | 600  | 1.6559          | {'precision': 0.8261826182618262, 'recall': 0.9192166462668299, 'f1': 0.8702201622247971, 'number': 817} | {'precision': 0.4957983193277311, 'recall': 0.4957983193277311, 'f1': 0.4957983193277311, 'number': 119} | {'precision': 0.9050814956855225, 'recall': 0.8765088207985144, 'f1': 0.8905660377358492, 'number': 1077} | 0.8469            | 0.8713         | 0.8590     | 0.7952           |
+| 0.0083        | 42.11  | 800  | 1.6136          | {'precision': 0.8760529482551144, 'recall': 0.8910648714810282, 'f1': 0.883495145631068, 'number': 817}  | {'precision': 0.6145833333333334, 'recall': 0.4957983193277311, 'f1': 0.5488372093023256, 'number': 119} | {'precision': 0.8963922294172063, 'recall': 0.8997214484679665, 'f1': 0.8980537534754401, 'number': 1077} | 0.8745            | 0.8723         | 0.8734     | 0.8060           |
+| 0.0058        | 52.63  | 1000 | 1.6826          | {'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119} | {'precision': 0.8902991840435177, 'recall': 0.9117920148560817, 'f1': 0.9009174311926607, 'number': 1077} | 0.8626            | 0.8917         | 0.8769     | 0.7928           |
+| 0.0027        | 63.16  | 1200 | 1.5511          | {'precision': 0.8640661938534279, 'recall': 0.8947368421052632, 'f1': 0.8791340950090198, 'number': 817} | {'precision': 0.576, 'recall': 0.6050420168067226, 'f1': 0.5901639344262294, 'number': 119}              | {'precision': 0.8985374771480804, 'recall': 0.9127205199628597, 'f1': 0.9055734684477199, 'number': 1077} | 0.8649            | 0.8872         | 0.8759     | 0.8110           |
+| 0.0014        | 73.68  | 1400 | 1.5130          | {'precision': 0.8801452784503632, 'recall': 0.8898408812729498, 'f1': 0.8849665246500303, 'number': 817} | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} | {'precision': 0.8748906386701663, 'recall': 0.9285051067780873, 'f1': 0.900900900900901, 'number': 1077}  | 0.8644            | 0.8897         | 0.8769     | 0.8092           |
+| 0.001         | 84.21  | 1600 | 1.5433          | {'precision': 0.8373893805309734, 'recall': 0.9265605875152999, 'f1': 0.8797210923881464, 'number': 817} | {'precision': 0.6033057851239669, 'recall': 0.6134453781512605, 'f1': 0.6083333333333334, 'number': 119} | {'precision': 0.9138257575757576, 'recall': 0.8960074280408542, 'f1': 0.9048288795124239, 'number': 1077} | 0.8626            | 0.8917         | 0.8769     | 0.8139           |
+| 0.0006        | 94.74  | 1800 | 1.5585          | {'precision': 0.8500576701268743, 'recall': 0.9020807833537332, 'f1': 0.8752969121140143, 'number': 817} | {'precision': 0.6371681415929203, 'recall': 0.6050420168067226, 'f1': 0.6206896551724138, 'number': 119} | {'precision': 0.8933454876937101, 'recall': 0.9099350046425255, 'f1': 0.9015639374425023, 'number': 1077} | 0.8613            | 0.8887         | 0.8748     | 0.8197           |
+| 0.0003        | 105.26 | 2000 | 1.5719          | {'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817} | {'precision': 0.6605504587155964, 'recall': 0.6050420168067226, 'f1': 0.6315789473684209, 'number': 119} | {'precision': 0.9113805970149254, 'recall': 0.9071494893221913, 'f1': 0.9092601209865054, 'number': 1077} | 0.8721            | 0.8942         | 0.8830     | 0.8246           |
+| 0.0004        | 115.79 | 2200 | 1.5578          | {'precision': 0.8554913294797688, 'recall': 0.9057527539779682, 'f1': 0.8799048751486326, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.9059907834101383, 'recall': 0.9127205199628597, 'f1': 0.9093432007400555, 'number': 1077} | 0.8696            | 0.8912         | 0.8803     | 0.8194           |
+| 0.0003        | 126.32 | 2400 | 1.5815          | {'precision': 0.8604118993135011, 'recall': 0.9204406364749081, 'f1': 0.8894145476049675, 'number': 817} | {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119} | {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077} | 0.8745            | 0.8962         | 0.8852     | 0.8209           |
 
 
 ### Framework versions

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