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: 1.7071
-- Answer: {'precision': 0.8837209302325582, 'recall': 0.8837209302325582, 'f1': 0.8837209302325582, 'number': 817}
-- Header: {'precision': 0.7021276595744681, 'recall': 0.5546218487394958, 'f1': 0.619718309859155, 'number': 119}
-- Question: {'precision': 0.893048128342246, 'recall': 0.9303621169916435, 'f1': 0.9113233287858117, 'number': 1077}
-- Overall Precision: 0.8805
-- Overall Recall: 0.8892
-- Overall F1: 0.8848
-- Overall Accuracy: 0.8010
+- Loss: 1.5087
+- Answer: {'precision': 0.8725146198830409, 'recall': 0.9130966952264382, 'f1': 0.8923444976076554, 'number': 817}
+- Header: {'precision': 0.5757575757575758, 'recall': 0.4789915966386555, 'f1': 0.5229357798165138, 'number': 119}
+- Question: {'precision': 0.895067264573991, 'recall': 0.9266480965645311, 'f1': 0.9105839416058394, 'number': 1077}
+- Overall Precision: 0.8705
+- Overall Recall: 0.8947
+- Overall F1: 0.8824
+- Overall Accuracy: 0.8227
 
 ## Model description
 
@@ -53,20 +53,20 @@ 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 |
-|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.4169        | 10.5263  | 200  | 1.0899          | {'precision': 0.8287752675386445, 'recall': 0.8531211750305998, 'f1': 0.8407720144752715, 'number': 817} | {'precision': 0.4339622641509434, 'recall': 0.5798319327731093, 'f1': 0.49640287769784175, 'number': 119} | {'precision': 0.8842203548085901, 'recall': 0.8792943361188487, 'f1': 0.8817504655493483, 'number': 1077} | 0.8271            | 0.8510         | 0.8389     | 0.7777           |
-| 0.0513        | 21.0526  | 400  | 1.2119          | {'precision': 0.8325688073394495, 'recall': 0.8886168910648715, 'f1': 0.8596802841918295, 'number': 817} | {'precision': 0.5606060606060606, 'recall': 0.6218487394957983, 'f1': 0.5896414342629481, 'number': 119}  | {'precision': 0.8788150807899462, 'recall': 0.9090064995357474, 'f1': 0.8936558649018712, 'number': 1077} | 0.8399            | 0.8838         | 0.8613     | 0.8029           |
-| 0.0142        | 31.5789  | 600  | 1.6716          | {'precision': 0.8363028953229399, 'recall': 0.9192166462668299, 'f1': 0.8758017492711371, 'number': 817} | {'precision': 0.4956521739130435, 'recall': 0.4789915966386555, 'f1': 0.48717948717948717, 'number': 119} | {'precision': 0.9023923444976076, 'recall': 0.8755803156917363, 'f1': 0.8887841658812441, 'number': 1077} | 0.8508            | 0.8698         | 0.8602     | 0.7907           |
-| 0.0084        | 42.1053  | 800  | 1.4810          | {'precision': 0.8514970059880239, 'recall': 0.8702570379436965, 'f1': 0.860774818401937, 'number': 817}  | {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119}  | {'precision': 0.8904235727440147, 'recall': 0.8978644382544104, 'f1': 0.8941285251964864, 'number': 1077} | 0.8607            | 0.8654         | 0.8630     | 0.7929           |
-| 0.0041        | 52.6316  | 1000 | 1.7178          | {'precision': 0.8329545454545455, 'recall': 0.8971848225214198, 'f1': 0.863877430760165, 'number': 817}  | {'precision': 0.6421052631578947, 'recall': 0.5126050420168067, 'f1': 0.5700934579439252, 'number': 119}  | {'precision': 0.8951241950321988, 'recall': 0.903435468895079, 'f1': 0.8992606284658041, 'number': 1077}  | 0.8569            | 0.8778         | 0.8672     | 0.7932           |
-| 0.0021        | 63.1579  | 1200 | 1.6473          | {'precision': 0.8661037394451147, 'recall': 0.8788249694002448, 'f1': 0.8724179829890644, 'number': 817} | {'precision': 0.6260869565217392, 'recall': 0.6050420168067226, 'f1': 0.6153846153846154, 'number': 119}  | {'precision': 0.8810172570390554, 'recall': 0.9006499535747446, 'f1': 0.8907254361799817, 'number': 1077} | 0.8606            | 0.8743         | 0.8674     | 0.7855           |
-| 0.0015        | 73.6842  | 1400 | 1.8620          | {'precision': 0.8372352285395763, 'recall': 0.9192166462668299, 'f1': 0.8763127187864644, 'number': 817} | {'precision': 0.5785123966942148, 'recall': 0.5882352941176471, 'f1': 0.5833333333333334, 'number': 119}  | {'precision': 0.9104908565928778, 'recall': 0.8783658310120706, 'f1': 0.8941398865784499, 'number': 1077} | 0.8590            | 0.8778         | 0.8683     | 0.7889           |
-| 0.0016        | 84.2105  | 1600 | 1.7642          | {'precision': 0.8637992831541219, 'recall': 0.8849449204406364, 'f1': 0.8742442563482467, 'number': 817} | {'precision': 0.6261682242990654, 'recall': 0.5630252100840336, 'f1': 0.5929203539823009, 'number': 119}  | {'precision': 0.8979779411764706, 'recall': 0.9071494893221913, 'f1': 0.902540415704388, 'number': 1077}  | 0.8696            | 0.8778         | 0.8737     | 0.7973           |
-| 0.0006        | 94.7368  | 1800 | 1.7071          | {'precision': 0.8837209302325582, 'recall': 0.8837209302325582, 'f1': 0.8837209302325582, 'number': 817} | {'precision': 0.7021276595744681, 'recall': 0.5546218487394958, 'f1': 0.619718309859155, 'number': 119}   | {'precision': 0.893048128342246, 'recall': 0.9303621169916435, 'f1': 0.9113233287858117, 'number': 1077}  | 0.8805            | 0.8892         | 0.8848     | 0.8010           |
-| 0.0004        | 105.2632 | 2000 | 1.7328          | {'precision': 0.8723150357995226, 'recall': 0.8947368421052632, 'f1': 0.8833836858006042, 'number': 817} | {'precision': 0.6153846153846154, 'recall': 0.6050420168067226, 'f1': 0.6101694915254237, 'number': 119}  | {'precision': 0.902014652014652, 'recall': 0.914577530176416, 'f1': 0.9082526509912402, 'number': 1077}   | 0.8735            | 0.8882         | 0.8808     | 0.8011           |
-| 0.0003        | 115.7895 | 2200 | 1.6935          | {'precision': 0.8775757575757576, 'recall': 0.8861689106487148, 'f1': 0.881851400730816, 'number': 817}  | {'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119}  | {'precision': 0.9034038638454461, 'recall': 0.9117920148560817, 'f1': 0.9075785582255084, 'number': 1077} | 0.8803            | 0.8808         | 0.8806     | 0.8065           |
-| 0.0003        | 126.3158 | 2400 | 1.7336          | {'precision': 0.8695136417556346, 'recall': 0.8971848225214198, 'f1': 0.883132530120482, 'number': 817}  | {'precision': 0.6548672566371682, 'recall': 0.6218487394957983, 'f1': 0.6379310344827586, 'number': 119}  | {'precision': 0.8994515539305301, 'recall': 0.9136490250696379, 'f1': 0.9064947029018885, 'number': 1077} | 0.8737            | 0.8897         | 0.8816     | 0.7979           |
+| Training Loss | Epoch    | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.418         | 10.5263  | 200  | 1.0401          | {'precision': 0.8506571087216248, 'recall': 0.8714810281517748, 'f1': 0.8609431680773882, 'number': 817} | {'precision': 0.38288288288288286, 'recall': 0.7142857142857143, 'f1': 0.49853372434017595, 'number': 119} | {'precision': 0.8753768844221106, 'recall': 0.8087279480037141, 'f1': 0.8407335907335907, 'number': 1077} | 0.8121            | 0.8286         | 0.8203     | 0.7896           |
+| 0.0498        | 21.0526  | 400  | 1.2017          | {'precision': 0.8493150684931506, 'recall': 0.9106487148102815, 'f1': 0.8789131718842291, 'number': 817} | {'precision': 0.47928994082840237, 'recall': 0.680672268907563, 'f1': 0.5625, 'number': 119}               | {'precision': 0.9011627906976745, 'recall': 0.8635097493036211, 'f1': 0.8819345661450925, 'number': 1077} | 0.8450            | 0.8718         | 0.8582     | 0.8116           |
+| 0.0143        | 31.5789  | 600  | 1.3940          | {'precision': 0.8566433566433567, 'recall': 0.8996328029375765, 'f1': 0.8776119402985075, 'number': 817} | {'precision': 0.6344086021505376, 'recall': 0.4957983193277311, 'f1': 0.5566037735849056, 'number': 119}   | {'precision': 0.8643344709897611, 'recall': 0.9405756731662024, 'f1': 0.9008448199199643, 'number': 1077} | 0.8512            | 0.8977         | 0.8738     | 0.8023           |
+| 0.0088        | 42.1053  | 800  | 1.4475          | {'precision': 0.8654073199527745, 'recall': 0.8971848225214198, 'f1': 0.8810096153846154, 'number': 817} | {'precision': 0.4666666666666667, 'recall': 0.5882352941176471, 'f1': 0.5204460966542751, 'number': 119}   | {'precision': 0.8764867337602927, 'recall': 0.8895078922934077, 'f1': 0.8829493087557604, 'number': 1077} | 0.8426            | 0.8748         | 0.8584     | 0.8008           |
+| 0.0034        | 52.6316  | 1000 | 1.5797          | {'precision': 0.8685503685503686, 'recall': 0.8653610771113831, 'f1': 0.8669527896995709, 'number': 817} | {'precision': 0.44871794871794873, 'recall': 0.5882352941176471, 'f1': 0.509090909090909, 'number': 119}   | {'precision': 0.8484320557491289, 'recall': 0.904363974001857, 'f1': 0.875505617977528, 'number': 1077}   | 0.8267            | 0.8698         | 0.8477     | 0.7960           |
+| 0.0021        | 63.1579  | 1200 | 1.5530          | {'precision': 0.83, 'recall': 0.9143206854345165, 'f1': 0.8701223063482819, 'number': 817}               | {'precision': 0.6707317073170732, 'recall': 0.46218487394957986, 'f1': 0.5472636815920398, 'number': 119}  | {'precision': 0.8688811188811189, 'recall': 0.9229340761374187, 'f1': 0.895092300765421, 'number': 1077}  | 0.8448            | 0.8922         | 0.8678     | 0.8055           |
+| 0.0016        | 73.6842  | 1400 | 1.5520          | {'precision': 0.8527397260273972, 'recall': 0.9143206854345165, 'f1': 0.8824571766095688, 'number': 817} | {'precision': 0.594059405940594, 'recall': 0.5042016806722689, 'f1': 0.5454545454545453, 'number': 119}    | {'precision': 0.9069548872180451, 'recall': 0.8960074280408542, 'f1': 0.9014479215319943, 'number': 1077} | 0.8682            | 0.8803         | 0.8742     | 0.8067           |
+| 0.0013        | 84.2105  | 1600 | 1.4729          | {'precision': 0.8419889502762431, 'recall': 0.9326805385556916, 'f1': 0.8850174216027874, 'number': 817} | {'precision': 0.5925925925925926, 'recall': 0.5378151260504201, 'f1': 0.5638766519823789, 'number': 119}   | {'precision': 0.901760889712697, 'recall': 0.903435468895079, 'f1': 0.9025974025974026, 'number': 1077}   | 0.8599            | 0.8937         | 0.8765     | 0.8149           |
+| 0.001         | 94.7368  | 1800 | 1.5041          | {'precision': 0.8497706422018348, 'recall': 0.9069767441860465, 'f1': 0.8774422735346358, 'number': 817} | {'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119}   | {'precision': 0.8790613718411552, 'recall': 0.904363974001857, 'f1': 0.891533180778032, 'number': 1077}   | 0.8503            | 0.8833         | 0.8665     | 0.8105           |
+| 0.0004        | 105.2632 | 2000 | 1.5316          | {'precision': 0.8520578420467185, 'recall': 0.9375764993880049, 'f1': 0.8927738927738927, 'number': 817} | {'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119}   | {'precision': 0.9027777777777778, 'recall': 0.9052924791086351, 'f1': 0.9040333796940194, 'number': 1077} | 0.8660            | 0.8922         | 0.8789     | 0.8137           |
+| 0.0003        | 115.7895 | 2200 | 1.5087          | {'precision': 0.8725146198830409, 'recall': 0.9130966952264382, 'f1': 0.8923444976076554, 'number': 817} | {'precision': 0.5757575757575758, 'recall': 0.4789915966386555, 'f1': 0.5229357798165138, 'number': 119}   | {'precision': 0.895067264573991, 'recall': 0.9266480965645311, 'f1': 0.9105839416058394, 'number': 1077}  | 0.8705            | 0.8947         | 0.8824     | 0.8227           |
+| 0.0002        | 126.3158 | 2400 | 1.5302          | {'precision': 0.8686046511627907, 'recall': 0.9143206854345165, 'f1': 0.8908765652951699, 'number': 817} | {'precision': 0.5523809523809524, 'recall': 0.48739495798319327, 'f1': 0.5178571428571428, 'number': 119}  | {'precision': 0.8939802336028752, 'recall': 0.9238625812441968, 'f1': 0.908675799086758, 'number': 1077}  | 0.8662            | 0.8942         | 0.8800     | 0.8190           |
 
 
 ### Framework versions

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