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: 1.5213
-- Answer: {'precision': 0.8422247446083996, 'recall': 0.9082007343941249, 'f1': 0.8739693757361603, 'number': 817}
-- Header: {'precision': 0.6633663366336634, 'recall': 0.5630252100840336, 'f1': 0.6090909090909091, 'number': 119}
-- Question: {'precision': 0.8962093862815884, 'recall': 0.9220055710306406, 'f1': 0.9089244851258581, 'number': 1077}
-- Overall Precision: 0.8622
-- Overall Recall: 0.8952
-- Overall F1: 0.8784
-- Overall Accuracy: 0.8101
+- 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
 
 ## 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 |
 |:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4065        | 10.5263  | 200  | 0.9595          | {'precision': 0.7962347729789591, 'recall': 0.8800489596083231, 'f1': 0.8360465116279069, 'number': 817} | {'precision': 0.5544554455445545, 'recall': 0.47058823529411764, 'f1': 0.509090909090909, 'number': 119}  | {'precision': 0.8766999093381687, 'recall': 0.8978644382544104, 'f1': 0.8871559633027524, 'number': 1077} | 0.8268            | 0.8654         | 0.8456     | 0.7996           |
-| 0.051         | 21.0526  | 400  | 1.3283          | {'precision': 0.8666666666666667, 'recall': 0.8910648714810282, 'f1': 0.8786964393482196, 'number': 817} | {'precision': 0.5227272727272727, 'recall': 0.5798319327731093, 'f1': 0.5498007968127491, 'number': 119}  | {'precision': 0.8887841658812441, 'recall': 0.8755803156917363, 'f1': 0.882132834424696, 'number': 1077}  | 0.8559            | 0.8644         | 0.8601     | 0.7931           |
-| 0.0133        | 31.5789  | 600  | 1.4326          | {'precision': 0.8370786516853933, 'recall': 0.9118727050183598, 'f1': 0.8728763913298183, 'number': 817} | {'precision': 0.5144927536231884, 'recall': 0.5966386554621849, 'f1': 0.5525291828793774, 'number': 119}  | {'precision': 0.9006882989183874, 'recall': 0.8505106778087279, 'f1': 0.8748806112702961, 'number': 1077} | 0.8469            | 0.8604         | 0.8536     | 0.7973           |
-| 0.0065        | 42.1053  | 800  | 1.5819          | {'precision': 0.8505096262740657, 'recall': 0.9192166462668299, 'f1': 0.8835294117647059, 'number': 817} | {'precision': 0.6794871794871795, 'recall': 0.44537815126050423, 'f1': 0.5380710659898478, 'number': 119} | {'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077}  | 0.8668            | 0.8828         | 0.8747     | 0.8080           |
-| 0.0064        | 52.6316  | 1000 | 1.4306          | {'precision': 0.866902237926973, 'recall': 0.9008567931456548, 'f1': 0.8835534213685473, 'number': 817}  | {'precision': 0.6185567010309279, 'recall': 0.5042016806722689, 'f1': 0.5555555555555556, 'number': 119}  | {'precision': 0.8900279589934762, 'recall': 0.8867223769730733, 'f1': 0.8883720930232558, 'number': 1077} | 0.8673            | 0.8698         | 0.8686     | 0.7997           |
-| 0.0027        | 63.1579  | 1200 | 1.4304          | {'precision': 0.851258581235698, 'recall': 0.9106487148102815, 'f1': 0.8799526907155529, 'number': 817}  | {'precision': 0.648936170212766, 'recall': 0.5126050420168067, 'f1': 0.5727699530516431, 'number': 119}   | {'precision': 0.8839285714285714, 'recall': 0.9192200557103064, 'f1': 0.9012289485662267, 'number': 1077} | 0.8597            | 0.8917         | 0.8754     | 0.8212           |
-| 0.0013        | 73.6842  | 1400 | 1.4734          | {'precision': 0.851808634772462, 'recall': 0.8935128518971848, 'f1': 0.8721624850657107, 'number': 817}  | {'precision': 0.6521739130434783, 'recall': 0.5042016806722689, 'f1': 0.5687203791469194, 'number': 119}  | {'precision': 0.8790035587188612, 'recall': 0.9173630454967502, 'f1': 0.8977737392094502, 'number': 1077} | 0.8577            | 0.8833         | 0.8703     | 0.8152           |
-| 0.0007        | 84.2105  | 1600 | 1.6066          | {'precision': 0.8348115299334812, 'recall': 0.9216646266829865, 'f1': 0.8760907504363001, 'number': 817} | {'precision': 0.6333333333333333, 'recall': 0.4789915966386555, 'f1': 0.5454545454545454, 'number': 119}  | {'precision': 0.8842676311030742, 'recall': 0.9080779944289693, 'f1': 0.8960146587265231, 'number': 1077} | 0.8522            | 0.8882         | 0.8699     | 0.8039           |
-| 0.0005        | 94.7368  | 1800 | 1.5574          | {'precision': 0.8433598183881952, 'recall': 0.9094247246022031, 'f1': 0.8751472320376914, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}              | {'precision': 0.9031657355679702, 'recall': 0.9006499535747446, 'f1': 0.901906090190609, 'number': 1077}  | 0.8659            | 0.8823         | 0.8740     | 0.8016           |
-| 0.0007        | 105.2632 | 2000 | 1.5402          | {'precision': 0.8447488584474886, 'recall': 0.9057527539779682, 'f1': 0.874187832250443, 'number': 817}  | {'precision': 0.6326530612244898, 'recall': 0.5210084033613446, 'f1': 0.5714285714285716, 'number': 119}  | {'precision': 0.8877917414721723, 'recall': 0.9182915506035283, 'f1': 0.9027841168416247, 'number': 1077} | 0.8578            | 0.8897         | 0.8734     | 0.8049           |
-| 0.0002        | 115.7895 | 2200 | 1.5213          | {'precision': 0.8422247446083996, 'recall': 0.9082007343941249, 'f1': 0.8739693757361603, 'number': 817} | {'precision': 0.6633663366336634, 'recall': 0.5630252100840336, 'f1': 0.6090909090909091, 'number': 119}  | {'precision': 0.8962093862815884, 'recall': 0.9220055710306406, 'f1': 0.9089244851258581, 'number': 1077} | 0.8622            | 0.8952         | 0.8784     | 0.8101           |
-| 0.0003        | 126.3158 | 2400 | 1.5403          | {'precision': 0.8424036281179138, 'recall': 0.9094247246022031, 'f1': 0.8746321365509123, 'number': 817} | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119}  | {'precision': 0.8925318761384335, 'recall': 0.9099350046425255, 'f1': 0.9011494252873563, 'number': 1077} | 0.8598            | 0.8892         | 0.8742     | 0.8084           |
+| 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           |
 
 
 ### Framework versions

logs/events.out.tfevents.1744262209.f3ec25b9a925.416.0

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 version https://git-lfs.github.com/spec/v1
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+size 14373

logs/events.out.tfevents.1744264105.f3ec25b9a925.416.1

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model.safetensors

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 version https://git-lfs.github.com/spec/v1
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tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 512
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 1,
+    "pad_type_id": 0,
+    "pad_token": "<pad>"
+  },
   "added_tokens": [
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