Model save

README.md

@@ -15,19 +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:
-- eval_loss: 1.6706
-- eval_ANSWER: {'precision': 0.875, 'recall': 0.9082007343941249, 'f1': 0.8912912912912913, 'number': 817}
-- eval_HEADER: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
-- eval_QUESTION: {'precision': 0.8880931065353626, 'recall': 0.9210770659238626, 'f1': 0.9042844120328168, 'number': 1077}
-- eval_overall_precision: 0.8718
-- eval_overall_recall: 0.8952
-- eval_overall_f1: 0.8833
-- eval_overall_accuracy: 0.8026
-- eval_runtime: 50.8863
-- eval_samples_per_second: 0.983
-- eval_steps_per_second: 0.138
-- epoch: 90.8421
-- step: 1726
+- Loss: 1.8743
+- Answer: {'precision': 0.8584579976985041, 'recall': 0.9130966952264382, 'f1': 0.8849347568208777, 'number': 817}
+- Header: {'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119}
+- Question: {'precision': 0.8976014760147601, 'recall': 0.903435468895079, 'f1': 0.9005090236001851, 'number': 1077}
+- Overall Precision: 0.8684
+- Overall Recall: 0.8852
+- Overall F1: 0.8768
+- Overall Accuracy: 0.7961
 
 ## Model description
 
@@ -55,9 +50,27 @@ The following hyperparameters were used during training:
 - training_steps: 2500
 - mixed_precision_training: Native AMP
 
+### 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           |
+
+
 ### Framework versions
 
-- Transformers 4.42.4
-- Pytorch 2.3.1+cpu
-- Datasets 2.20.0
+- Transformers 4.44.0
+- Pytorch 2.4.0+cu118
+- Datasets 2.21.0
 - Tokenizers 0.19.1

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