Model save

README.md

@@ -15,14 +15,14 @@ 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: 1.7425
-- Answer: {'precision': 0.8685446009389671, 'recall': 0.9057527539779682, 'f1': 0.8867585380467345, 'number': 817}
-- Header: {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119}
-- Question: {'precision': 0.8882931188561215, 'recall': 0.9229340761374187, 'f1': 0.9052823315118397, 'number': 1077}
-- Overall Precision: 0.8661
-- Overall Recall: 0.8967
-- Overall F1: 0.8811
-- Overall Accuracy: 0.7941
+- Loss: 1.6142
+- Answer: {'precision': 0.8502857142857143, 'recall': 0.9106487148102815, 'f1': 0.8794326241134752, 'number': 817}
+- Header: {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}
+- Question: {'precision': 0.9045871559633027, 'recall': 0.9155060352831941, 'f1': 0.9100138440239963, 'number': 1077}
+- Overall Precision: 0.8681
+- Overall Recall: 0.8927
+- Overall F1: 0.8802
+- Overall Accuracy: 0.8247
 
 ## Model description
 
@@ -52,20 +52,20 @@ 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 |
-|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.0432        | 10.5263  | 200  | 1.2673          | {'precision': 0.8699386503067484, 'recall': 0.8678090575275398, 'f1': 0.8688725490196079, 'number': 817} | {'precision': 0.5602836879432624, 'recall': 0.6638655462184874, 'f1': 0.6076923076923078, 'number': 119} | {'precision': 0.8524734982332155, 'recall': 0.8960074280408542, 'f1': 0.8736985061113626, 'number': 1077} | 0.8396            | 0.8708         | 0.8549     | 0.7871           |
-| 0.0111        | 21.0526  | 400  | 1.6035          | {'precision': 0.8281938325991189, 'recall': 0.9204406364749081, 'f1': 0.8718840579710144, 'number': 817} | {'precision': 0.5819672131147541, 'recall': 0.5966386554621849, 'f1': 0.5892116182572614, 'number': 119} | {'precision': 0.8996212121212122, 'recall': 0.8820798514391829, 'f1': 0.8907641819034223, 'number': 1077} | 0.8500            | 0.8808         | 0.8651     | 0.7877           |
-| 0.0052        | 31.5789  | 600  | 1.5662          | {'precision': 0.8514619883040936, 'recall': 0.8910648714810282, 'f1': 0.8708133971291867, 'number': 817} | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119} | {'precision': 0.8783303730017762, 'recall': 0.9182915506035283, 'f1': 0.8978665456196095, 'number': 1077} | 0.8556            | 0.8857         | 0.8704     | 0.7957           |
-| 0.0032        | 42.1053  | 800  | 1.7157          | {'precision': 0.8119565217391305, 'recall': 0.9143206854345165, 'f1': 0.8601036269430052, 'number': 817} | {'precision': 0.6867469879518072, 'recall': 0.4789915966386555, 'f1': 0.5643564356435644, 'number': 119} | {'precision': 0.8967136150234741, 'recall': 0.8867223769730733, 'f1': 0.8916900093370681, 'number': 1077} | 0.8506            | 0.8738         | 0.8620     | 0.7843           |
-| 0.0023        | 52.6316  | 1000 | 1.7425          | {'precision': 0.8685446009389671, 'recall': 0.9057527539779682, 'f1': 0.8867585380467345, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.8882931188561215, 'recall': 0.9229340761374187, 'f1': 0.9052823315118397, 'number': 1077} | 0.8661            | 0.8967         | 0.8811     | 0.7941           |
-| 0.0011        | 63.1579  | 1200 | 1.8113          | {'precision': 0.8514285714285714, 'recall': 0.9118727050183598, 'f1': 0.8806146572104018, 'number': 817} | {'precision': 0.6272727272727273, 'recall': 0.5798319327731093, 'f1': 0.6026200873362446, 'number': 119} | {'precision': 0.8959854014598541, 'recall': 0.9117920148560817, 'f1': 0.9038196042337783, 'number': 1077} | 0.8630            | 0.8922         | 0.8774     | 0.7946           |
-| 0.0005        | 73.6842  | 1400 | 1.8814          | {'precision': 0.8533791523482245, 'recall': 0.9118727050183598, 'f1': 0.8816568047337279, 'number': 817} | {'precision': 0.5362318840579711, 'recall': 0.6218487394957983, 'f1': 0.5758754863813229, 'number': 119} | {'precision': 0.8961646398503275, 'recall': 0.8895078922934077, 'f1': 0.8928238583410998, 'number': 1077} | 0.8543            | 0.8828         | 0.8683     | 0.7877           |
-| 0.0006        | 84.2105  | 1600 | 1.9554          | {'precision': 0.8481735159817352, 'recall': 0.9094247246022031, 'f1': 0.8777318369757826, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119} | {'precision': 0.8986301369863013, 'recall': 0.9136490250696379, 'f1': 0.9060773480662985, 'number': 1077} | 0.8614            | 0.8922         | 0.8765     | 0.7946           |
-| 0.0002        | 94.7368  | 1800 | 1.9811          | {'precision': 0.8302094818081588, 'recall': 0.9216646266829865, 'f1': 0.8735498839907193, 'number': 817} | {'precision': 0.6464646464646465, 'recall': 0.5378151260504201, 'f1': 0.5871559633027523, 'number': 119} | {'precision': 0.9012115563839702, 'recall': 0.8978644382544104, 'f1': 0.8995348837209303, 'number': 1077} | 0.8581            | 0.8862         | 0.8719     | 0.7903           |
-| 0.0004        | 105.2632 | 2000 | 1.8838          | {'precision': 0.8638985005767013, 'recall': 0.9167686658506732, 'f1': 0.8895486935866984, 'number': 817} | {'precision': 0.6146788990825688, 'recall': 0.5630252100840336, 'f1': 0.5877192982456141, 'number': 119} | {'precision': 0.8975069252077562, 'recall': 0.9025069637883009, 'f1': 0.9, 'number': 1077}                | 0.8684            | 0.8882         | 0.8782     | 0.7991           |
-| 0.0001        | 115.7895 | 2200 | 1.9096          | {'precision': 0.856815578465063, 'recall': 0.9155446756425949, 'f1': 0.8852071005917158, 'number': 817}  | {'precision': 0.6203703703703703, 'recall': 0.5630252100840336, 'f1': 0.5903083700440528, 'number': 119} | {'precision': 0.9028944911297853, 'recall': 0.8978644382544104, 'f1': 0.9003724394785848, 'number': 1077} | 0.8684            | 0.8852         | 0.8768     | 0.7952           |
-| 0.0001        | 126.3158 | 2400 | 1.8743          | {'precision': 0.8584579976985041, 'recall': 0.9130966952264382, 'f1': 0.8849347568208777, 'number': 817} | {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119} | {'precision': 0.8976014760147601, 'recall': 0.903435468895079, 'f1': 0.9005090236001851, 'number': 1077}  | 0.8684            | 0.8852         | 0.8768     | 0.7961           |
+| Training Loss | Epoch    | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4043        | 10.5263  | 200  | 0.9334          | {'precision': 0.8331402085747392, 'recall': 0.8800489596083231, 'f1': 0.8559523809523808, 'number': 817} | {'precision': 0.4879518072289157, 'recall': 0.680672268907563, 'f1': 0.5684210526315789, 'number': 119}   | {'precision': 0.8621940163191296, 'recall': 0.883008356545961, 'f1': 0.8724770642201833, 'number': 1077}  | 0.8213            | 0.8698         | 0.8449     | 0.8028           |
+| 0.0458        | 21.0526  | 400  | 1.2878          | {'precision': 0.8611793611793612, 'recall': 0.8580171358629131, 'f1': 0.8595953402820357, 'number': 817} | {'precision': 0.6071428571428571, 'recall': 0.5714285714285714, 'f1': 0.5887445887445888, 'number': 119}  | {'precision': 0.8597246127366609, 'recall': 0.9275766016713092, 'f1': 0.892362661902635, 'number': 1077}  | 0.8467            | 0.8783         | 0.8622     | 0.8073           |
+| 0.0188        | 31.5789  | 600  | 1.2773          | {'precision': 0.8287292817679558, 'recall': 0.9179926560587516, 'f1': 0.8710801393728222, 'number': 817} | {'precision': 0.5892857142857143, 'recall': 0.5546218487394958, 'f1': 0.5714285714285715, 'number': 119}  | {'precision': 0.90549662487946, 'recall': 0.871866295264624, 'f1': 0.8883632923368022, 'number': 1077}    | 0.8544            | 0.8718         | 0.8630     | 0.7988           |
+| 0.007         | 42.1053  | 800  | 1.5029          | {'precision': 0.8728121353558926, 'recall': 0.9155446756425949, 'f1': 0.8936678614097969, 'number': 817} | {'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119}  | {'precision': 0.8888888888888888, 'recall': 0.9136490250696379, 'f1': 0.9010989010989011, 'number': 1077} | 0.8709            | 0.8912         | 0.8809     | 0.8154           |
+| 0.0031        | 52.6316  | 1000 | 1.5006          | {'precision': 0.8540965207631874, 'recall': 0.9314565483476133, 'f1': 0.8911007025761123, 'number': 817} | {'precision': 0.5666666666666667, 'recall': 0.5714285714285714, 'f1': 0.5690376569037656, 'number': 119}  | {'precision': 0.9017447199265382, 'recall': 0.9117920148560817, 'f1': 0.9067405355493999, 'number': 1077} | 0.8624            | 0.8997         | 0.8806     | 0.8124           |
+| 0.0017        | 63.1579  | 1200 | 1.5541          | {'precision': 0.8778718258766627, 'recall': 0.8886168910648715, 'f1': 0.8832116788321168, 'number': 817} | {'precision': 0.6239316239316239, 'recall': 0.6134453781512605, 'f1': 0.6186440677966102, 'number': 119}  | {'precision': 0.8927927927927928, 'recall': 0.9201485608170845, 'f1': 0.9062642889803384, 'number': 1077} | 0.8715            | 0.8892         | 0.8803     | 0.8150           |
+| 0.0022        | 73.6842  | 1400 | 1.6132          | {'precision': 0.8556461001164144, 'recall': 0.8996328029375765, 'f1': 0.8770883054892601, 'number': 817} | {'precision': 0.6304347826086957, 'recall': 0.48739495798319327, 'f1': 0.5497630331753555, 'number': 119} | {'precision': 0.8986046511627906, 'recall': 0.8969359331476323, 'f1': 0.8977695167286245, 'number': 1077} | 0.8682            | 0.8738         | 0.8710     | 0.8127           |
+| 0.0019        | 84.2105  | 1600 | 1.5373          | {'precision': 0.8615916955017301, 'recall': 0.9143206854345165, 'f1': 0.8871733966745844, 'number': 817} | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119}  | {'precision': 0.8936936936936937, 'recall': 0.9210770659238626, 'f1': 0.9071787837219937, 'number': 1077} | 0.8678            | 0.8967         | 0.8820     | 0.8224           |
+| 0.0006        | 94.7368  | 1800 | 1.5759          | {'precision': 0.8616780045351474, 'recall': 0.9302325581395349, 'f1': 0.8946439081812831, 'number': 817} | {'precision': 0.6804123711340206, 'recall': 0.5546218487394958, 'f1': 0.6111111111111112, 'number': 119}  | {'precision': 0.9055404178019982, 'recall': 0.9257195914577531, 'f1': 0.9155188246097338, 'number': 1077} | 0.8764            | 0.9056         | 0.8908     | 0.8294           |
+| 0.0003        | 105.2632 | 2000 | 1.5537          | {'precision': 0.884004884004884, 'recall': 0.8861689106487148, 'f1': 0.8850855745721272, 'number': 817}  | {'precision': 0.6476190476190476, 'recall': 0.5714285714285714, 'f1': 0.6071428571428571, 'number': 119}  | {'precision': 0.8874113475177305, 'recall': 0.9294336118848654, 'f1': 0.9079365079365079, 'number': 1077} | 0.8738            | 0.8907         | 0.8822     | 0.8209           |
+| 0.0005        | 115.7895 | 2200 | 1.5898          | {'precision': 0.8531791907514451, 'recall': 0.9033047735618115, 'f1': 0.8775267538644471, 'number': 817} | {'precision': 0.591304347826087, 'recall': 0.5714285714285714, 'f1': 0.5811965811965812, 'number': 119}   | {'precision': 0.9015496809480401, 'recall': 0.9182915506035283, 'f1': 0.9098436062557498, 'number': 1077} | 0.8642            | 0.8917         | 0.8778     | 0.8223           |
+| 0.0002        | 126.3158 | 2400 | 1.6142          | {'precision': 0.8502857142857143, 'recall': 0.9106487148102815, 'f1': 0.8794326241134752, 'number': 817} | {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}   | {'precision': 0.9045871559633027, 'recall': 0.9155060352831941, 'f1': 0.9100138440239963, 'number': 1077} | 0.8681            | 0.8927         | 0.8802     | 0.8247           |
 
 
 ### Framework versions

logs/events.out.tfevents.1728469660.PC2031.28612.0

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model.safetensors

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