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--- |
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license: mit |
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base_model: SCUT-DLVCLab/lilt-roberta-en-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: plus2_model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# plus2_model |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7698 |
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- Eader: {'precision': 0.3232876712328767, 'recall': 0.2532188841201717, 'f1': 0.28399518652226236, 'number': 466} |
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- Nswer: {'precision': 0.43698252069917204, 'recall': 0.38183279742765275, 'f1': 0.4075504075504076, 'number': 1244} |
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- Uestion: {'precision': 0.392904953145917, 'recall': 0.425670775924583, 'f1': 0.40863209189001043, 'number': 1379} |
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- Overall Precision: 0.4005 |
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- Overall Recall: 0.3820 |
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- Overall F1: 0.3911 |
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- Overall Accuracy: 0.5776 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1902 | 0.7 | 200 | 0.2840 | {'precision': 0.20205479452054795, 'recall': 0.12660944206008584, 'f1': 0.15567282321899734, 'number': 466} | {'precision': 0.2560738581146744, 'recall': 0.42363344051446944, 'f1': 0.3192004845548152, 'number': 1244} | {'precision': 0.3292753623188406, 'recall': 0.41189267585206674, 'f1': 0.36597938144329895, 'number': 1379} | 0.2832 | 0.3736 | 0.3222 | 0.6408 | |
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| 0.1315 | 1.4 | 400 | 0.3785 | {'precision': 0.2557544757033248, 'recall': 0.2145922746781116, 'f1': 0.23337222870478413, 'number': 466} | {'precision': 0.3143491124260355, 'recall': 0.34163987138263663, 'f1': 0.327426810477658, 'number': 1244} | {'precision': 0.31990924560408396, 'recall': 0.40899202320522116, 'f1': 0.35900700190961177, 'number': 1379} | 0.3106 | 0.3525 | 0.3303 | 0.5741 | |
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| 0.1117 | 2.11 | 600 | 0.5260 | {'precision': 0.33003300330033003, 'recall': 0.2145922746781116, 'f1': 0.2600780234070221, 'number': 466} | {'precision': 0.37556973564266183, 'recall': 0.3311897106109325, 'f1': 0.35198633062793677, 'number': 1244} | {'precision': 0.37993311036789296, 'recall': 0.41189267585206674, 'f1': 0.39526791927627, 'number': 1379} | 0.3731 | 0.3496 | 0.3610 | 0.5467 | |
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| 0.0824 | 2.81 | 800 | 0.4716 | {'precision': 0.29289940828402367, 'recall': 0.21244635193133046, 'f1': 0.2462686567164179, 'number': 466} | {'precision': 0.38620689655172413, 'recall': 0.36012861736334406, 'f1': 0.3727121464226289, 'number': 1244} | {'precision': 0.36801541425818884, 'recall': 0.41551849166062366, 'f1': 0.3903269754768393, 'number': 1379} | 0.3666 | 0.3626 | 0.3646 | 0.5599 | |
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| 0.0654 | 3.51 | 1000 | 0.3955 | {'precision': 0.3408360128617363, 'recall': 0.22746781115879827, 'f1': 0.2728442728442728, 'number': 466} | {'precision': 0.3290094339622642, 'recall': 0.44855305466237944, 'f1': 0.37959183673469393, 'number': 1244} | {'precision': 0.3595634692705342, 'recall': 0.45395213923132705, 'f1': 0.4012820512820513, 'number': 1379} | 0.3442 | 0.4176 | 0.3774 | 0.6413 | |
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| 0.0701 | 4.21 | 1200 | 0.5372 | {'precision': 0.3501577287066246, 'recall': 0.23819742489270387, 'f1': 0.2835249042145594, 'number': 466} | {'precision': 0.39559543230016314, 'recall': 0.38987138263665594, 'f1': 0.3927125506072874, 'number': 1244} | {'precision': 0.3765156349712827, 'recall': 0.4278462654097172, 'f1': 0.40054310930074677, 'number': 1379} | 0.3814 | 0.3839 | 0.3826 | 0.5792 | |
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| 0.048 | 4.91 | 1400 | 0.7393 | {'precision': 0.37168141592920356, 'recall': 0.18025751072961374, 'f1': 0.24277456647398846, 'number': 466} | {'precision': 0.4155972359328727, 'recall': 0.3384244372990354, 'f1': 0.3730615861763403, 'number': 1244} | {'precision': 0.39614855570839064, 'recall': 0.4176939811457578, 'f1': 0.4066360748323332, 'number': 1379} | 0.4014 | 0.3500 | 0.3739 | 0.5258 | |
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| 0.0372 | 5.61 | 1600 | 0.6617 | {'precision': 0.34726688102893893, 'recall': 0.2317596566523605, 'f1': 0.277992277992278, 'number': 466} | {'precision': 0.390325271059216, 'recall': 0.3762057877813505, 'f1': 0.38313548915268114, 'number': 1244} | {'precision': 0.39173228346456695, 'recall': 0.43292240754169686, 'f1': 0.4112986565621771, 'number': 1379} | 0.3866 | 0.3797 | 0.3831 | 0.5713 | |
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| 0.0347 | 6.32 | 1800 | 0.7866 | {'precision': 0.3680297397769517, 'recall': 0.21244635193133046, 'f1': 0.2693877551020408, 'number': 466} | {'precision': 0.42815814850530376, 'recall': 0.35691318327974275, 'f1': 0.38930293730819815, 'number': 1244} | {'precision': 0.39893617021276595, 'recall': 0.43509789702683105, 'f1': 0.4162330905306972, 'number': 1379} | 0.4068 | 0.3700 | 0.3875 | 0.5454 | |
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| 0.0278 | 7.02 | 2000 | 0.6686 | {'precision': 0.34110787172011664, 'recall': 0.2510729613733906, 'f1': 0.2892459826946848, 'number': 466} | {'precision': 0.4066115702479339, 'recall': 0.3954983922829582, 'f1': 0.40097799511002447, 'number': 1244} | {'precision': 0.39158163265306123, 'recall': 0.4452501812907904, 'f1': 0.4166949440108585, 'number': 1379} | 0.3919 | 0.3959 | 0.3939 | 0.6009 | |
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| 0.0233 | 7.72 | 2200 | 0.7673 | {'precision': 0.2876712328767123, 'recall': 0.2703862660944206, 'f1': 0.2787610619469027, 'number': 466} | {'precision': 0.4287020109689214, 'recall': 0.3770096463022508, 'f1': 0.4011976047904192, 'number': 1244} | {'precision': 0.38976109215017063, 'recall': 0.41406816533720087, 'f1': 0.4015471167369901, 'number': 1379} | 0.3891 | 0.3775 | 0.3832 | 0.5676 | |
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| 0.0205 | 8.42 | 2400 | 0.7698 | {'precision': 0.3232876712328767, 'recall': 0.2532188841201717, 'f1': 0.28399518652226236, 'number': 466} | {'precision': 0.43698252069917204, 'recall': 0.38183279742765275, 'f1': 0.4075504075504076, 'number': 1244} | {'precision': 0.392904953145917, 'recall': 0.425670775924583, 'f1': 0.40863209189001043, 'number': 1379} | 0.4005 | 0.3820 | 0.3911 | 0.5776 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0.dev20230810 |
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- Datasets 2.14.4 |
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- Tokenizers 0.14.1 |
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