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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-base-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 |
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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 |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5515 |
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- Accuracy: 0.8248 |
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- Precision: 0.8311 |
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- Recall: 0.8248 |
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- F1: 0.8240 |
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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: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- training_steps: 850 |
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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 | 0.2732 | 50 | 1.4704 | 0.4704 | 0.4864 | 0.4704 | 0.4383 | |
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| No log | 0.5464 | 100 | 1.1159 | 0.5742 | 0.5936 | 0.5742 | 0.5676 | |
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| No log | 0.8197 | 150 | 0.9276 | 0.6441 | 0.6564 | 0.6441 | 0.6391 | |
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| No log | 1.0929 | 200 | 0.7966 | 0.7064 | 0.7146 | 0.7064 | 0.7057 | |
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| No log | 1.3661 | 250 | 0.7308 | 0.7317 | 0.7408 | 0.7317 | 0.7291 | |
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| No log | 1.6393 | 300 | 0.6640 | 0.7571 | 0.7624 | 0.7571 | 0.7560 | |
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| No log | 1.9126 | 350 | 0.5874 | 0.7940 | 0.7975 | 0.7940 | 0.7931 | |
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| No log | 2.1858 | 400 | 0.6288 | 0.7863 | 0.7958 | 0.7863 | 0.7840 | |
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| No log | 2.4590 | 450 | 0.5621 | 0.8055 | 0.8128 | 0.8055 | 0.8048 | |
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| 0.8255 | 2.7322 | 500 | 0.5799 | 0.8094 | 0.8181 | 0.8094 | 0.8087 | |
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| 0.8255 | 3.0055 | 550 | 0.5560 | 0.7994 | 0.8041 | 0.7994 | 0.7978 | |
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| 0.8255 | 3.2787 | 600 | 0.5402 | 0.8301 | 0.8341 | 0.8301 | 0.8305 | |
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| 0.8255 | 3.5519 | 650 | 0.5534 | 0.8201 | 0.8287 | 0.8201 | 0.8197 | |
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| 0.8255 | 3.8251 | 700 | 0.5439 | 0.8248 | 0.8333 | 0.8248 | 0.8249 | |
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| 0.8255 | 4.0984 | 750 | 0.5402 | 0.8248 | 0.8304 | 0.8248 | 0.8244 | |
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| 0.8255 | 4.3716 | 800 | 0.5363 | 0.8271 | 0.8309 | 0.8271 | 0.8262 | |
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| 0.8255 | 4.6448 | 850 | 0.5515 | 0.8248 | 0.8311 | 0.8248 | 0.8240 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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