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: 1.5759
-- Answer: {'precision': 0.8616780045351474, 'recall': 0.9302325581395349, 'f1': 0.8946439081812831, 'number': 817}
-- Header: {'precision': 0.6804123711340206, 'recall': 0.5546218487394958, 'f1': 0.6111111111111112, 'number': 119}
-- Question: {'precision': 0.9055404178019982, 'recall': 0.9257195914577531, 'f1': 0.9155188246097338, 'number': 1077}
-- Overall Precision: 0.8764
-- Overall Recall: 0.9056
-- Overall F1: 0.8908
-- Overall Accuracy: 0.8294
+- Loss: 1.7527
+- Answer: {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817}
+- Header: {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119}
+- Question: {'precision': 0.8963963963963963, 'recall': 0.9238625812441968, 'f1': 0.9099222679469592, 'number': 1077}
+- Overall Precision: 0.8648
+- Overall Recall: 0.8962
+- Overall F1: 0.8802
+- Overall Accuracy: 0.8057
 
 ## 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 |
-|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4043        | 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           |
+| Training Loss | Epoch    | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.3859        | 10.5263  | 200  | 1.1901          | {'precision': 0.8349514563106796, 'recall': 0.8421052631578947, 'f1': 0.8385131017672149, 'number': 817} | {'precision': 0.42953020134228187, 'recall': 0.5378151260504201, 'f1': 0.47761194029850745, 'number': 119} | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8270            | 0.8594         | 0.8429     | 0.7738           |
+| 0.0461        | 21.0526  | 400  | 1.3985          | {'precision': 0.8586448598130841, 'recall': 0.8996328029375765, 'f1': 0.8786610878661089, 'number': 817} | {'precision': 0.5, 'recall': 0.6050420168067226, 'f1': 0.5475285171102661, 'number': 119}                  | {'precision': 0.8864468864468864, 'recall': 0.8987929433611885, 'f1': 0.8925772245274319, 'number': 1077} | 0.8485            | 0.8818         | 0.8648     | 0.7846           |
+| 0.0139        | 31.5789  | 600  | 1.4340          | {'precision': 0.8617021276595744, 'recall': 0.8922888616891065, 'f1': 0.8767288033674082, 'number': 817} | {'precision': 0.5263157894736842, 'recall': 0.5882352941176471, 'f1': 0.5555555555555555, 'number': 119}   | {'precision': 0.8739946380697051, 'recall': 0.9080779944289693, 'f1': 0.8907103825136613, 'number': 1077} | 0.8470            | 0.8828         | 0.8645     | 0.8029           |
+| 0.0074        | 42.1053  | 800  | 1.5489          | {'precision': 0.8450057405281286, 'recall': 0.9008567931456548, 'f1': 0.8720379146919431, 'number': 817} | {'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119}   | {'precision': 0.8733738074588031, 'recall': 0.9350046425255338, 'f1': 0.9031390134529148, 'number': 1077} | 0.8512            | 0.8952         | 0.8726     | 0.7957           |
+| 0.0026        | 52.6316  | 1000 | 1.6408          | {'precision': 0.8661800486618005, 'recall': 0.8714810281517748, 'f1': 0.8688224527150702, 'number': 817} | {'precision': 0.5932203389830508, 'recall': 0.5882352941176471, 'f1': 0.5907172995780592, 'number': 119}   | {'precision': 0.8859964093357271, 'recall': 0.9164345403899722, 'f1': 0.9009584664536742, 'number': 1077} | 0.8612            | 0.8788         | 0.8699     | 0.7988           |
+| 0.005         | 63.1579  | 1200 | 1.5299          | {'precision': 0.8518518518518519, 'recall': 0.9008567931456548, 'f1': 0.875669244497323, 'number': 817}  | {'precision': 0.6407766990291263, 'recall': 0.5546218487394958, 'f1': 0.5945945945945947, 'number': 119}   | {'precision': 0.883128295254833, 'recall': 0.9331476323119777, 'f1': 0.90744920993228, 'number': 1077}    | 0.8584            | 0.8977         | 0.8776     | 0.8010           |
+| 0.004         | 73.6842  | 1400 | 1.5962          | {'precision': 0.8402699662542182, 'recall': 0.9143206854345165, 'f1': 0.8757327080890973, 'number': 817} | {'precision': 0.5625, 'recall': 0.5294117647058824, 'f1': 0.5454545454545455, 'number': 119}               | {'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077} | 0.8528            | 0.8922         | 0.8721     | 0.8084           |
+| 0.0007        | 84.2105  | 1600 | 1.6587          | {'precision': 0.8458904109589042, 'recall': 0.9069767441860465, 'f1': 0.8753691671588896, 'number': 817} | {'precision': 0.6355140186915887, 'recall': 0.5714285714285714, 'f1': 0.6017699115044248, 'number': 119}   | {'precision': 0.8946894689468947, 'recall': 0.9229340761374187, 'f1': 0.9085923217550275, 'number': 1077} | 0.8610            | 0.8957         | 0.8780     | 0.8051           |
+| 0.0007        | 94.7368  | 1800 | 1.5919          | {'precision': 0.8495475113122172, 'recall': 0.9192166462668299, 'f1': 0.8830099941211051, 'number': 817} | {'precision': 0.6055045871559633, 'recall': 0.5546218487394958, 'f1': 0.5789473684210525, 'number': 119}   | {'precision': 0.9030797101449275, 'recall': 0.9257195914577531, 'f1': 0.9142595139844107, 'number': 1077} | 0.8650            | 0.9011         | 0.8827     | 0.8102           |
+| 0.0004        | 105.2632 | 2000 | 1.7501          | {'precision': 0.8614318706697459, 'recall': 0.9130966952264382, 'f1': 0.8865121806298276, 'number': 817} | {'precision': 0.6052631578947368, 'recall': 0.5798319327731093, 'f1': 0.5922746781115881, 'number': 119}   | {'precision': 0.9106976744186046, 'recall': 0.9090064995357474, 'f1': 0.9098513011152416, 'number': 1077} | 0.8730            | 0.8912         | 0.8820     | 0.8070           |
+| 0.0003        | 115.7895 | 2200 | 1.7584          | {'precision': 0.8773364485981309, 'recall': 0.9192166462668299, 'f1': 0.8977884040645547, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}   | {'precision': 0.9097605893186004, 'recall': 0.9173630454967502, 'f1': 0.9135460009246417, 'number': 1077} | 0.8833            | 0.8952         | 0.8892     | 0.8076           |
+| 0.0001        | 126.3158 | 2400 | 1.7527          | {'precision': 0.8525714285714285, 'recall': 0.9130966952264382, 'f1': 0.8817966903073285, 'number': 817} | {'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119}   | {'precision': 0.8963963963963963, 'recall': 0.9238625812441968, 'f1': 0.9099222679469592, 'number': 1077} | 0.8648            | 0.8962         | 0.8802     | 0.8057           |
 
 
 ### Framework versions

logs/events.out.tfevents.1729233359.PC2031.504.0

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model.safetensors

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