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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-large-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: math_question_grade_detection_Bert_databalanced |
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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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# math_question_grade_detection_Bert_databalanced |
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This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6880 |
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- Accuracy: 0.7603 |
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- Precision: 0.7651 |
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- Recall: 0.7603 |
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- F1: 0.7588 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 0.2817 | 50 | 2.1003 | 0.2349 | 0.3799 | 0.2349 | 0.2106 | |
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| No log | 0.5634 | 100 | 1.9607 | 0.2762 | 0.3337 | 0.2762 | 0.2498 | |
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| No log | 0.8451 | 150 | 1.5031 | 0.4778 | 0.4633 | 0.4778 | 0.4591 | |
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| No log | 1.1268 | 200 | 1.2546 | 0.5460 | 0.5596 | 0.5460 | 0.5176 | |
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| No log | 1.4085 | 250 | 1.0941 | 0.5746 | 0.5804 | 0.5746 | 0.5675 | |
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| No log | 1.6901 | 300 | 0.9381 | 0.6730 | 0.6943 | 0.6730 | 0.6721 | |
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| No log | 1.9718 | 350 | 0.8974 | 0.6619 | 0.6822 | 0.6619 | 0.6570 | |
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| No log | 2.2535 | 400 | 0.8243 | 0.6889 | 0.6913 | 0.6889 | 0.6856 | |
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| No log | 2.5352 | 450 | 0.8219 | 0.6937 | 0.7131 | 0.6937 | 0.6881 | |
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| 1.2537 | 2.8169 | 500 | 0.7642 | 0.7159 | 0.7239 | 0.7159 | 0.7121 | |
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| 1.2537 | 3.0986 | 550 | 0.7580 | 0.7175 | 0.7197 | 0.7175 | 0.7068 | |
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| 1.2537 | 3.3803 | 600 | 0.7310 | 0.7397 | 0.7523 | 0.7397 | 0.7387 | |
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| 1.2537 | 3.6620 | 650 | 0.7562 | 0.7413 | 0.7466 | 0.7413 | 0.7349 | |
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| 1.2537 | 3.9437 | 700 | 0.6512 | 0.7730 | 0.7792 | 0.7730 | 0.7726 | |
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| 1.2537 | 4.2254 | 750 | 0.6941 | 0.7476 | 0.7484 | 0.7476 | 0.7447 | |
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| 1.2537 | 4.5070 | 800 | 0.6866 | 0.7571 | 0.7607 | 0.7571 | 0.7550 | |
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| 1.2537 | 4.7887 | 850 | 0.6942 | 0.7603 | 0.7644 | 0.7603 | 0.7588 | |
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| 1.2537 | 5.0704 | 900 | 0.7230 | 0.7683 | 0.7821 | 0.7683 | 0.7656 | |
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| 1.2537 | 5.3521 | 950 | 0.7123 | 0.7603 | 0.7669 | 0.7603 | 0.7588 | |
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| 0.321 | 5.6338 | 1000 | 0.6939 | 0.7667 | 0.7725 | 0.7667 | 0.7652 | |
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| 0.321 | 5.9155 | 1050 | 0.6884 | 0.7667 | 0.7723 | 0.7667 | 0.7657 | |
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| 0.321 | 6.1972 | 1100 | 0.6880 | 0.7603 | 0.7651 | 0.7603 | 0.7588 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.0 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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