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library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: math_question_grade_detection_v12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# math_question_grade_detection_v12
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.
It achieves the following results on the evaluation set:
- Loss: 0.8913
- Accuracy: 0.7397
- Precision: 0.7255
- Recall: 0.7397
- F1: 0.7178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.0855 | 50 | 3.0343 | 0.1710 | 0.1287 | 0.1710 | 0.0917 |
| No log | 0.1709 | 100 | 2.4792 | 0.3151 | 0.2979 | 0.3151 | 0.2439 |
| No log | 0.2564 | 150 | 2.1397 | 0.4428 | 0.4101 | 0.4428 | 0.3752 |
| No log | 0.3419 | 200 | 1.9824 | 0.4419 | 0.3872 | 0.4419 | 0.3638 |
| No log | 0.4274 | 250 | 1.7765 | 0.5072 | 0.4772 | 0.5072 | 0.4666 |
| No log | 0.5128 | 300 | 1.6510 | 0.5524 | 0.5347 | 0.5524 | 0.5147 |
| No log | 0.5983 | 350 | 1.5160 | 0.5793 | 0.5501 | 0.5793 | 0.5319 |
| No log | 0.6838 | 400 | 1.4481 | 0.5898 | 0.5608 | 0.5898 | 0.5437 |
| No log | 0.7692 | 450 | 1.3791 | 0.6148 | 0.5758 | 0.6148 | 0.5678 |
| 1.9748 | 0.8547 | 500 | 1.3154 | 0.6196 | 0.6123 | 0.6196 | 0.5779 |
| 1.9748 | 0.9402 | 550 | 1.2399 | 0.6484 | 0.6168 | 0.6484 | 0.6119 |
| 1.9748 | 1.0256 | 600 | 1.1968 | 0.6340 | 0.6181 | 0.6340 | 0.5889 |
| 1.9748 | 1.1111 | 650 | 1.2477 | 0.6215 | 0.6014 | 0.6215 | 0.5825 |
| 1.9748 | 1.1966 | 700 | 1.2098 | 0.6340 | 0.6285 | 0.6340 | 0.5884 |
| 1.9748 | 1.2821 | 750 | 1.1316 | 0.6619 | 0.6442 | 0.6619 | 0.6385 |
| 1.9748 | 1.3675 | 800 | 1.0783 | 0.6744 | 0.6644 | 0.6744 | 0.6462 |
| 1.9748 | 1.4530 | 850 | 1.0512 | 0.6907 | 0.6728 | 0.6907 | 0.6583 |
| 1.9748 | 1.5385 | 900 | 1.0388 | 0.6945 | 0.6909 | 0.6945 | 0.6697 |
| 1.9748 | 1.6239 | 950 | 0.9954 | 0.6974 | 0.6748 | 0.6974 | 0.6707 |
| 1.0265 | 1.7094 | 1000 | 0.9812 | 0.7128 | 0.6888 | 0.7128 | 0.6874 |
| 1.0265 | 1.7949 | 1050 | 0.9717 | 0.7099 | 0.6907 | 0.7099 | 0.6852 |
| 1.0265 | 1.8803 | 1100 | 0.9437 | 0.7099 | 0.6823 | 0.7099 | 0.6866 |
| 1.0265 | 1.9658 | 1150 | 0.9724 | 0.7061 | 0.7096 | 0.7061 | 0.6800 |
| 1.0265 | 2.0513 | 1200 | 0.9168 | 0.7224 | 0.7099 | 0.7224 | 0.6976 |
| 1.0265 | 2.1368 | 1250 | 0.9097 | 0.7243 | 0.7109 | 0.7243 | 0.6996 |
| 1.0265 | 2.2222 | 1300 | 0.9072 | 0.7329 | 0.7336 | 0.7329 | 0.7083 |
| 1.0265 | 2.3077 | 1350 | 0.9028 | 0.7262 | 0.7114 | 0.7262 | 0.7033 |
| 1.0265 | 2.3932 | 1400 | 0.8951 | 0.7301 | 0.7145 | 0.7301 | 0.7068 |
| 1.0265 | 2.4786 | 1450 | 0.8949 | 0.7378 | 0.7339 | 0.7378 | 0.7154 |
| 0.687 | 2.5641 | 1500 | 0.8913 | 0.7397 | 0.7255 | 0.7397 | 0.7178 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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