End of training

README.md

@@ -16,14 +16,14 @@ 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: 1.7343
-- Answer: {'precision': 0.865979381443299, 'recall': 0.9253365973072215, 'f1': 0.8946745562130176, 'number': 817}
-- Header: {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}
-- Question: {'precision': 0.8937329700272479, 'recall': 0.9136490250696379, 'f1': 0.9035812672176309, 'number': 1077}
-- Overall Precision: 0.8696
-- Overall Recall: 0.8977
-- Overall F1: 0.8834
-- Overall Accuracy: 0.8048
+- Loss: 1.5278
+- Answer: {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817}
+- Header: {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}
+- Question: {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077}
+- Overall Precision: 0.8845
+- Overall Recall: 0.8942
+- Overall F1: 0.8893
+- Overall Accuracy: 0.8213
 
 ## Model description
 
@@ -49,23 +49,24 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - training_steps: 2500
+- mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
 |:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4108        | 10.53  | 200  | 0.9777          | {'precision': 0.8268590455049944, 'recall': 0.9118727050183598, 'f1': 0.8672875436554133, 'number': 817} | {'precision': 0.6464646464646465, 'recall': 0.5378151260504201, 'f1': 0.5871559633027523, 'number': 119}  | {'precision': 0.8931860036832413, 'recall': 0.9006499535747446, 'f1': 0.8969024503005086, 'number': 1077} | 0.8528            | 0.8838         | 0.8680     | 0.8028           |
-| 0.0416        | 21.05  | 400  | 1.3626          | {'precision': 0.8482446206115515, 'recall': 0.9167686658506732, 'f1': 0.8811764705882352, 'number': 817} | {'precision': 0.5266666666666666, 'recall': 0.6638655462184874, 'f1': 0.5873605947955389, 'number': 119}  | {'precision': 0.891566265060241, 'recall': 0.89322191272052, 'f1': 0.8923933209647495, 'number': 1077}    | 0.8475            | 0.8892         | 0.8679     | 0.8033           |
-| 0.017         | 31.58  | 600  | 1.4784          | {'precision': 0.8646341463414634, 'recall': 0.8678090575275398, 'f1': 0.8662186927306048, 'number': 817} | {'precision': 0.6486486486486487, 'recall': 0.6050420168067226, 'f1': 0.6260869565217391, 'number': 119}  | {'precision': 0.8519148936170213, 'recall': 0.9294336118848654, 'f1': 0.8889875666074601, 'number': 1077} | 0.8462            | 0.8852         | 0.8653     | 0.7964           |
-| 0.0098        | 42.11  | 800  | 1.4397          | {'precision': 0.8267543859649122, 'recall': 0.9228886168910648, 'f1': 0.8721804511278195, 'number': 817} | {'precision': 0.5865384615384616, 'recall': 0.5126050420168067, 'f1': 0.5470852017937219, 'number': 119}  | {'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077} | 0.8493            | 0.8932         | 0.8707     | 0.8077           |
-| 0.004         | 52.63  | 1000 | 1.5432          | {'precision': 0.8721893491124261, 'recall': 0.9020807833537332, 'f1': 0.8868832731648616, 'number': 817} | {'precision': 0.5681818181818182, 'recall': 0.6302521008403361, 'f1': 0.597609561752988, 'number': 119}   | {'precision': 0.8956999085086916, 'recall': 0.9090064995357474, 'f1': 0.9023041474654377, 'number': 1077} | 0.8652            | 0.8897         | 0.8773     | 0.8162           |
-| 0.0025        | 63.16  | 1200 | 1.6970          | {'precision': 0.8733727810650888, 'recall': 0.9033047735618115, 'f1': 0.888086642599278, 'number': 817}  | {'precision': 0.7215189873417721, 'recall': 0.4789915966386555, 'f1': 0.5757575757575758, 'number': 119}  | {'precision': 0.8804251550044287, 'recall': 0.9229340761374187, 'f1': 0.901178603807797, 'number': 1077}  | 0.8714            | 0.8887         | 0.8800     | 0.7970           |
-| 0.0012        | 73.68  | 1400 | 1.6351          | {'precision': 0.8643274853801169, 'recall': 0.9045287637698899, 'f1': 0.8839712918660287, 'number': 817} | {'precision': 0.6115702479338843, 'recall': 0.6218487394957983, 'f1': 0.6166666666666667, 'number': 119}  | {'precision': 0.8899821109123435, 'recall': 0.9238625812441968, 'f1': 0.9066059225512528, 'number': 1077} | 0.8634            | 0.8982         | 0.8804     | 0.8059           |
-| 0.0006        | 84.21  | 1600 | 1.5729          | {'precision': 0.8616279069767442, 'recall': 0.9069767441860465, 'f1': 0.8837209302325582, 'number': 817} | {'precision': 0.6973684210526315, 'recall': 0.44537815126050423, 'f1': 0.5435897435897435, 'number': 119} | {'precision': 0.878868258178603, 'recall': 0.9229340761374187, 'f1': 0.9003623188405797, 'number': 1077}  | 0.8650            | 0.8882         | 0.8765     | 0.8149           |
-| 0.0008        | 94.74  | 1800 | 1.8110          | {'precision': 0.8455467869222097, 'recall': 0.9179926560587516, 'f1': 0.880281690140845, 'number': 817}  | {'precision': 0.5522388059701493, 'recall': 0.6218487394957983, 'f1': 0.5849802371541502, 'number': 119}  | {'precision': 0.9101851851851852, 'recall': 0.9127205199628597, 'f1': 0.9114510894761243, 'number': 1077} | 0.8601            | 0.8977         | 0.8785     | 0.7979           |
-| 0.0003        | 105.26 | 2000 | 1.7278          | {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817} | {'precision': 0.591304347826087, 'recall': 0.5714285714285714, 'f1': 0.5811965811965812, 'number': 119}   | {'precision': 0.8986301369863013, 'recall': 0.9136490250696379, 'f1': 0.9060773480662985, 'number': 1077} | 0.8670            | 0.8967         | 0.8816     | 0.8005           |
-| 0.0004        | 115.79 | 2200 | 1.7088          | {'precision': 0.8802816901408451, 'recall': 0.9179926560587516, 'f1': 0.8987417615338527, 'number': 817} | {'precision': 0.6120689655172413, 'recall': 0.5966386554621849, 'f1': 0.6042553191489363, 'number': 119}  | {'precision': 0.8869801084990958, 'recall': 0.9108635097493036, 'f1': 0.8987631699496106, 'number': 1077} | 0.8689            | 0.8952         | 0.8818     | 0.8051           |
-| 0.0003        | 126.32 | 2400 | 1.7343          | {'precision': 0.865979381443299, 'recall': 0.9253365973072215, 'f1': 0.8946745562130176, 'number': 817}  | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119}   | {'precision': 0.8937329700272479, 'recall': 0.9136490250696379, 'f1': 0.9035812672176309, 'number': 1077} | 0.8696            | 0.8977         | 0.8834     | 0.8048           |
+| 0.4258        | 10.53  | 200  | 0.9877          | {'precision': 0.8354143019296254, 'recall': 0.9008567931456548, 'f1': 0.866902237926973, 'number': 817}  | {'precision': 0.5491803278688525, 'recall': 0.5630252100840336, 'f1': 0.5560165975103735, 'number': 119}  | {'precision': 0.8691756272401434, 'recall': 0.9006499535747446, 'f1': 0.8846329229366164, 'number': 1077} | 0.8367            | 0.8808         | 0.8582     | 0.8030           |
+| 0.0396        | 21.05  | 400  | 1.2891          | {'precision': 0.8314855875831486, 'recall': 0.9179926560587516, 'f1': 0.8726003490401396, 'number': 817} | {'precision': 0.5289256198347108, 'recall': 0.5378151260504201, 'f1': 0.5333333333333334, 'number': 119}  | {'precision': 0.9008579599618685, 'recall': 0.8774373259052924, 'f1': 0.8889934148635935, 'number': 1077} | 0.8489            | 0.8738         | 0.8612     | 0.8071           |
+| 0.0145        | 31.58  | 600  | 1.1878          | {'precision': 0.8541666666666666, 'recall': 0.9033047735618115, 'f1': 0.8780487804878049, 'number': 817} | {'precision': 0.5943396226415094, 'recall': 0.5294117647058824, 'f1': 0.5599999999999999, 'number': 119}  | {'precision': 0.8793565683646113, 'recall': 0.9136490250696379, 'f1': 0.896174863387978, 'number': 1077}  | 0.8545            | 0.8867         | 0.8703     | 0.8139           |
+| 0.0093        | 42.11  | 800  | 1.3968          | {'precision': 0.8727272727272727, 'recall': 0.8812729498164015, 'f1': 0.8769792935444579, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.6050420168067226, 'f1': 0.5737051792828685, 'number': 119}  | {'precision': 0.8951686417502279, 'recall': 0.9117920148560817, 'f1': 0.9034038638454462, 'number': 1077} | 0.8637            | 0.8813         | 0.8724     | 0.8054           |
+| 0.0042        | 52.63  | 1000 | 1.5509          | {'precision': 0.8372093023255814, 'recall': 0.9253365973072215, 'f1': 0.8790697674418605, 'number': 817} | {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} | {'precision': 0.9044048734770385, 'recall': 0.8960074280408542, 'f1': 0.9001865671641791, 'number': 1077} | 0.8628            | 0.8838         | 0.8731     | 0.8044           |
+| 0.0026        | 63.16  | 1200 | 1.5696          | {'precision': 0.8618266978922716, 'recall': 0.9008567931456548, 'f1': 0.8809096349491322, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5210084033613446, 'f1': 0.5849056603773585, 'number': 119}  | {'precision': 0.8935978358881875, 'recall': 0.9201485608170845, 'f1': 0.9066788655077767, 'number': 1077} | 0.8701            | 0.8887         | 0.8793     | 0.8116           |
+| 0.001         | 73.68  | 1400 | 1.7209          | {'precision': 0.8396860986547086, 'recall': 0.9167686658506732, 'f1': 0.8765359859566998, 'number': 817} | {'precision': 0.6781609195402298, 'recall': 0.4957983193277311, 'f1': 0.5728155339805825, 'number': 119}  | {'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077} | 0.8628            | 0.8813         | 0.8720     | 0.7977           |
+| 0.0011        | 84.21  | 1600 | 1.5329          | {'precision': 0.8646188850967008, 'recall': 0.9302325581395349, 'f1': 0.8962264150943396, 'number': 817} | {'precision': 0.6666666666666666, 'recall': 0.5042016806722689, 'f1': 0.5741626794258373, 'number': 119}  | {'precision': 0.9050691244239631, 'recall': 0.9117920148560817, 'f1': 0.9084181313598519, 'number': 1077} | 0.8773            | 0.8952         | 0.8862     | 0.8267           |
+| 0.0006        | 94.74  | 1800 | 1.5523          | {'precision': 0.8748510131108462, 'recall': 0.8984088127294981, 'f1': 0.8864734299516908, 'number': 817} | {'precision': 0.5811965811965812, 'recall': 0.5714285714285714, 'f1': 0.576271186440678, 'number': 119}   | {'precision': 0.9045412418906394, 'recall': 0.9062209842154132, 'f1': 0.9053803339517627, 'number': 1077} | 0.8737            | 0.8833         | 0.8785     | 0.8196           |
+| 0.0005        | 105.26 | 2000 | 1.5178          | {'precision': 0.8758949880668258, 'recall': 0.8984088127294981, 'f1': 0.8870090634441088, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}  | {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} | 0.8775            | 0.8897         | 0.8836     | 0.8253           |
+| 0.0004        | 115.79 | 2200 | 1.5493          | {'precision': 0.8597701149425288, 'recall': 0.9155446756425949, 'f1': 0.8867812685240072, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119}  | {'precision': 0.9107635694572217, 'recall': 0.9192200557103064, 'f1': 0.9149722735674676, 'number': 1077} | 0.8777            | 0.8947         | 0.8861     | 0.8217           |
+| 0.0003        | 126.32 | 2400 | 1.5278          | {'precision': 0.8726415094339622, 'recall': 0.9057527539779682, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.6701030927835051, 'recall': 0.5462184873949579, 'f1': 0.6018518518518517, 'number': 119}  | {'precision': 0.9128440366972477, 'recall': 0.9238625812441968, 'f1': 0.9183202584217812, 'number': 1077} | 0.8845            | 0.8942         | 0.8893     | 0.8213           |
 
 
 ### Framework versions

logs/events.out.tfevents.1682631950.datascience-1-0-ml-g4dn-xlarge-94fad2f4401e538ca1255dfa1e84.1621.0

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+size 12628

logs/events.out.tfevents.1682634099.datascience-1-0-ml-g4dn-xlarge-94fad2f4401e538ca1255dfa1e84.1621.2

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