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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: math_question_grade_detection_Bert_databalanced_v2
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_Bert_databalanced_v2
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.
It achieves the following results on the evaluation set:
- Loss: 0.5945
- Accuracy: 0.8127
- Precision: 0.8116
- Recall: 0.8127
- F1: 0.8110
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.2817 | 50 | 2.1406 | 0.1698 | 0.1183 | 0.1698 | 0.1327 |
| No log | 0.5634 | 100 | 1.8833 | 0.3540 | 0.3387 | 0.3540 | 0.2911 |
| No log | 0.8451 | 150 | 1.5465 | 0.4365 | 0.4580 | 0.4365 | 0.4060 |
| No log | 1.1268 | 200 | 1.2969 | 0.4937 | 0.4950 | 0.4937 | 0.4471 |
| No log | 1.4085 | 250 | 1.0146 | 0.6143 | 0.6253 | 0.6143 | 0.5906 |
| No log | 1.6901 | 300 | 0.8713 | 0.6778 | 0.6771 | 0.6778 | 0.6476 |
| No log | 1.9718 | 350 | 0.7740 | 0.7016 | 0.7000 | 0.7016 | 0.6896 |
| No log | 2.2535 | 400 | 0.7760 | 0.6968 | 0.7068 | 0.6968 | 0.6872 |
| No log | 2.5352 | 450 | 0.6579 | 0.7619 | 0.7726 | 0.7619 | 0.7590 |
| 1.2792 | 2.8169 | 500 | 0.6872 | 0.7429 | 0.7571 | 0.7429 | 0.7418 |
| 1.2792 | 3.0986 | 550 | 0.6073 | 0.7698 | 0.7783 | 0.7698 | 0.7700 |
| 1.2792 | 3.3803 | 600 | 0.6297 | 0.7714 | 0.7840 | 0.7714 | 0.7718 |
| 1.2792 | 3.6620 | 650 | 0.6160 | 0.7762 | 0.7764 | 0.7762 | 0.7731 |
| 1.2792 | 3.9437 | 700 | 0.5895 | 0.8111 | 0.8147 | 0.8111 | 0.8110 |
| 1.2792 | 4.2254 | 750 | 0.5717 | 0.8111 | 0.8087 | 0.8111 | 0.8089 |
| 1.2792 | 4.5070 | 800 | 0.5767 | 0.8095 | 0.8126 | 0.8095 | 0.8083 |
| 1.2792 | 4.7887 | 850 | 0.5898 | 0.8016 | 0.8029 | 0.8016 | 0.7995 |
| 1.2792 | 5.0704 | 900 | 0.5908 | 0.8127 | 0.8143 | 0.8127 | 0.8115 |
| 1.2792 | 5.3521 | 950 | 0.5972 | 0.8111 | 0.8136 | 0.8111 | 0.8102 |
| 0.304 | 5.6338 | 1000 | 0.5945 | 0.8127 | 0.8116 | 0.8127 | 0.8110 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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