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 an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.7701
-- Answer: {'precision': 0.8803317535545023, 'recall': 0.9094247246022031, 'f1': 0.8946417820590005, 'number': 817}
-- Header: {'precision': 0.5901639344262295, 'recall': 0.6050420168067226, 'f1': 0.5975103734439834, 'number': 119}
-- Question: {'precision': 0.896551724137931, 'recall': 0.9173630454967502, 'f1': 0.9068379990821477, 'number': 1077}
-- Overall Precision: 0.8719
-- Overall Recall: 0.8957
-- Overall F1: 0.8836
-- Overall Accuracy: 0.8000
+- Loss: 1.5659
+- Answer: {'precision': 0.8772348033373063, 'recall': 0.9008567931456548, 'f1': 0.8888888888888888, 'number': 817}
+- Header: {'precision': 0.5826086956521739, 'recall': 0.5630252100840336, 'f1': 0.5726495726495726, 'number': 119}
+- Question: {'precision': 0.9031657355679702, 'recall': 0.9006499535747446, 'f1': 0.901906090190609, 'number': 1077}
+- Overall Precision: 0.8743
+- Overall Recall: 0.8808
+- Overall F1: 0.8775
+- Overall Accuracy: 0.8124
 
 ## Model description
 
@@ -55,18 +55,18 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch    | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
 |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4168        | 10.5263  | 200  | 1.1023          | {'precision': 0.8440046565774156, 'recall': 0.8873929008567931, 'f1': 0.8651551312649164, 'number': 817} | {'precision': 0.43884892086330934, 'recall': 0.5126050420168067, 'f1': 0.4728682170542636, 'number': 119} | {'precision': 0.8705673758865248, 'recall': 0.9117920148560817, 'f1': 0.890702947845805, 'number': 1077}  | 0.8316            | 0.8783         | 0.8543     | 0.7820           |
-| 0.0476        | 21.0526  | 400  | 1.2706          | {'precision': 0.8250276854928018, 'recall': 0.9118727050183598, 'f1': 0.866279069767442, 'number': 817}  | {'precision': 0.5384615384615384, 'recall': 0.5294117647058824, 'f1': 0.5338983050847458, 'number': 119}  | {'precision': 0.8871701546860783, 'recall': 0.9052924791086351, 'f1': 0.896139705882353, 'number': 1077}  | 0.8414            | 0.8857         | 0.8630     | 0.8023           |
-| 0.014         | 31.5789  | 600  | 1.4921          | {'precision': 0.8704600484261501, 'recall': 0.8800489596083231, 'f1': 0.8752282410225197, 'number': 817} | {'precision': 0.496551724137931, 'recall': 0.6050420168067226, 'f1': 0.5454545454545454, 'number': 119}   | {'precision': 0.8803339517625232, 'recall': 0.8811513463324049, 'f1': 0.8807424593967518, 'number': 1077} | 0.8492            | 0.8644         | 0.8567     | 0.8020           |
-| 0.0101        | 42.1053  | 800  | 1.4732          | {'precision': 0.8352668213457076, 'recall': 0.8812729498164015, 'f1': 0.8576533650982727, 'number': 817} | {'precision': 0.6228070175438597, 'recall': 0.5966386554621849, 'f1': 0.6094420600858369, 'number': 119}  | {'precision': 0.8831521739130435, 'recall': 0.9052924791086351, 'f1': 0.8940852819807427, 'number': 1077} | 0.8490            | 0.8773         | 0.8629     | 0.7839           |
-| 0.0044        | 52.6316  | 1000 | 1.5208          | {'precision': 0.8543922984356197, 'recall': 0.8690330477356181, 'f1': 0.8616504854368933, 'number': 817} | {'precision': 0.6422018348623854, 'recall': 0.5882352941176471, 'f1': 0.6140350877192983, 'number': 119}  | {'precision': 0.8970189701897019, 'recall': 0.9220055710306406, 'f1': 0.9093406593406592, 'number': 1077} | 0.8661            | 0.8808         | 0.8734     | 0.8048           |
-| 0.003         | 63.1579  | 1200 | 1.7060          | {'precision': 0.8451178451178452, 'recall': 0.9216646266829865, 'f1': 0.8817330210772834, 'number': 817} | {'precision': 0.6853932584269663, 'recall': 0.5126050420168067, 'f1': 0.5865384615384615, 'number': 119}  | {'precision': 0.8949730700179533, 'recall': 0.9257195914577531, 'f1': 0.9100867183934277, 'number': 1077} | 0.8649            | 0.8997         | 0.8819     | 0.7982           |
-| 0.0012        | 73.6842  | 1400 | 1.7775          | {'precision': 0.870023419203747, 'recall': 0.9094247246022031, 'f1': 0.8892878515858766, 'number': 817}  | {'precision': 0.5803571428571429, 'recall': 0.5462184873949579, 'f1': 0.5627705627705628, 'number': 119}  | {'precision': 0.8925022583559169, 'recall': 0.9173630454967502, 'f1': 0.9047619047619048, 'number': 1077} | 0.8664            | 0.8922         | 0.8791     | 0.7922           |
-| 0.001         | 84.2105  | 1600 | 1.7301          | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6146788990825688, 'recall': 0.5630252100840336, 'f1': 0.5877192982456141, 'number': 119}  | {'precision': 0.9037927844588344, 'recall': 0.9071494893221913, 'f1': 0.9054680259499537, 'number': 1077} | 0.8668            | 0.8892         | 0.8779     | 0.8001           |
-| 0.0007        | 94.7368  | 1800 | 1.7705          | {'precision': 0.8801897983392646, 'recall': 0.9082007343941249, 'f1': 0.8939759036144578, 'number': 817} | {'precision': 0.5581395348837209, 'recall': 0.6050420168067226, 'f1': 0.5806451612903225, 'number': 119}  | {'precision': 0.9056956115779645, 'recall': 0.9006499535747446, 'f1': 0.9031657355679702, 'number': 1077} | 0.8732            | 0.8862         | 0.8797     | 0.7972           |
-| 0.0003        | 105.2632 | 2000 | 1.7701          | {'precision': 0.8803317535545023, 'recall': 0.9094247246022031, 'f1': 0.8946417820590005, 'number': 817} | {'precision': 0.5901639344262295, 'recall': 0.6050420168067226, 'f1': 0.5975103734439834, 'number': 119}  | {'precision': 0.896551724137931, 'recall': 0.9173630454967502, 'f1': 0.9068379990821477, 'number': 1077}  | 0.8719            | 0.8957         | 0.8836     | 0.8000           |
-| 0.0004        | 115.7895 | 2200 | 1.8060          | {'precision': 0.8546910755148741, 'recall': 0.9143206854345165, 'f1': 0.8835008870490834, 'number': 817} | {'precision': 0.6018518518518519, 'recall': 0.5462184873949579, 'f1': 0.5726872246696034, 'number': 119}  | {'precision': 0.8975297346752058, 'recall': 0.9108635097493036, 'f1': 0.904147465437788, 'number': 1077}  | 0.8641            | 0.8907         | 0.8772     | 0.7971           |
-| 0.0002        | 126.3158 | 2400 | 1.7987          | {'precision': 0.8646441073512252, 'recall': 0.9069767441860465, 'f1': 0.8853046594982078, 'number': 817} | {'precision': 0.544, 'recall': 0.5714285714285714, 'f1': 0.5573770491803279, 'number': 119}               | {'precision': 0.9048938134810711, 'recall': 0.9099350046425255, 'f1': 0.9074074074074073, 'number': 1077} | 0.8663            | 0.8887         | 0.8774     | 0.7953           |
+| 0.4346        | 10.5263  | 200  | 0.9773          | {'precision': 0.8395480225988701, 'recall': 0.9094247246022031, 'f1': 0.8730904817861339, 'number': 817} | {'precision': 0.5, 'recall': 0.5798319327731093, 'f1': 0.5369649805447471, 'number': 119}                 | {'precision': 0.8733031674208145, 'recall': 0.8960074280408542, 'f1': 0.8845096241979835, 'number': 1077} | 0.8351            | 0.8828         | 0.8582     | 0.8061           |
+| 0.0497        | 21.0526  | 400  | 1.2488          | {'precision': 0.8596698113207547, 'recall': 0.8922888616891065, 'f1': 0.8756756756756756, 'number': 817} | {'precision': 0.5225225225225225, 'recall': 0.48739495798319327, 'f1': 0.5043478260869565, 'number': 119} | {'precision': 0.8717720391807658, 'recall': 0.9090064995357474, 'f1': 0.8899999999999999, 'number': 1077} | 0.8482            | 0.8773         | 0.8625     | 0.8099           |
+| 0.0156        | 31.5789  | 600  | 1.3758          | {'precision': 0.8043478260869565, 'recall': 0.9510403916768666, 'f1': 0.8715647784632641, 'number': 817} | {'precision': 0.5064102564102564, 'recall': 0.6638655462184874, 'f1': 0.5745454545454546, 'number': 119}  | {'precision': 0.9111328125, 'recall': 0.8662952646239555, 'f1': 0.8881485007139458, 'number': 1077}       | 0.8336            | 0.8887         | 0.8603     | 0.7875           |
+| 0.0072        | 42.1053  | 800  | 1.4574          | {'precision': 0.8394648829431438, 'recall': 0.9216646266829865, 'f1': 0.8786464410735123, 'number': 817} | {'precision': 0.525, 'recall': 0.5294117647058824, 'f1': 0.5271966527196653, 'number': 119}               | {'precision': 0.888268156424581, 'recall': 0.8857938718662952, 'f1': 0.8870292887029289, 'number': 1077}  | 0.8465            | 0.8793         | 0.8626     | 0.8020           |
+| 0.0039        | 52.6316  | 1000 | 1.6322          | {'precision': 0.8585365853658536, 'recall': 0.8616891064871481, 'f1': 0.8601099572388515, 'number': 817} | {'precision': 0.5185185185185185, 'recall': 0.5882352941176471, 'f1': 0.5511811023622047, 'number': 119}  | {'precision': 0.8744354110207768, 'recall': 0.8987929433611885, 'f1': 0.8864468864468864, 'number': 1077} | 0.8448            | 0.8654         | 0.8550     | 0.7783           |
+| 0.0037        | 63.1579  | 1200 | 1.6199          | {'precision': 0.8275862068965517, 'recall': 0.9106487148102815, 'f1': 0.867132867132867, 'number': 817}  | {'precision': 0.5384615384615384, 'recall': 0.5882352941176471, 'f1': 0.5622489959839357, 'number': 119}  | {'precision': 0.9043478260869565, 'recall': 0.8690807799442897, 'f1': 0.8863636363636365, 'number': 1077} | 0.8479            | 0.8693         | 0.8585     | 0.7901           |
+| 0.0015        | 73.6842  | 1400 | 1.6549          | {'precision': 0.8115015974440895, 'recall': 0.9326805385556916, 'f1': 0.867881548974943, 'number': 817}  | {'precision': 0.5769230769230769, 'recall': 0.5042016806722689, 'f1': 0.5381165919282511, 'number': 119}  | {'precision': 0.9180487804878049, 'recall': 0.8737233054781801, 'f1': 0.895337773549001, 'number': 1077}  | 0.8525            | 0.8758         | 0.8640     | 0.7951           |
+| 0.001         | 84.2105  | 1600 | 1.6181          | {'precision': 0.8700834326579261, 'recall': 0.8935128518971848, 'f1': 0.8816425120772947, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5882352941176471, 'f1': 0.5857740585774059, 'number': 119}  | {'precision': 0.8992537313432836, 'recall': 0.8950789229340761, 'f1': 0.8971614704513727, 'number': 1077} | 0.8685            | 0.8763         | 0.8724     | 0.8010           |
+| 0.0007        | 94.7368  | 1800 | 1.5533          | {'precision': 0.8535754824063564, 'recall': 0.9204406364749081, 'f1': 0.8857479387514723, 'number': 817} | {'precision': 0.5677966101694916, 'recall': 0.5630252100840336, 'f1': 0.5654008438818565, 'number': 119}  | {'precision': 0.9200779727095516, 'recall': 0.8765088207985144, 'f1': 0.8977650974797908, 'number': 1077} | 0.8706            | 0.8758         | 0.8732     | 0.8192           |
+| 0.0004        | 105.2632 | 2000 | 1.5659          | {'precision': 0.8772348033373063, 'recall': 0.9008567931456548, 'f1': 0.8888888888888888, 'number': 817} | {'precision': 0.5826086956521739, 'recall': 0.5630252100840336, 'f1': 0.5726495726495726, 'number': 119}  | {'precision': 0.9031657355679702, 'recall': 0.9006499535747446, 'f1': 0.901906090190609, 'number': 1077}  | 0.8743            | 0.8808         | 0.8775     | 0.8124           |
+| 0.0004        | 115.7895 | 2200 | 1.5713          | {'precision': 0.8487584650112867, 'recall': 0.9204406364749081, 'f1': 0.8831473869641807, 'number': 817} | {'precision': 0.6, 'recall': 0.5798319327731093, 'f1': 0.5897435897435898, 'number': 119}                 | {'precision': 0.9030131826741996, 'recall': 0.8904363974001857, 'f1': 0.8966806919121085, 'number': 1077} | 0.8628            | 0.8843         | 0.8734     | 0.8143           |
+| 0.0001        | 126.3158 | 2400 | 1.5663          | {'precision': 0.8584474885844748, 'recall': 0.9204406364749081, 'f1': 0.8883638511518015, 'number': 817} | {'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.8955637707948244, 'recall': 0.8997214484679665, 'f1': 0.8976377952755905, 'number': 1077} | 0.8652            | 0.8867         | 0.8759     | 0.8150           |
 
 
 ### Framework versions

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model.safetensors

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