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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
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
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: 0.5515
- Accuracy: 0.8248
- Precision: 0.8311
- Recall: 0.8248
- F1: 0.8240
## 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: 64
- 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: 850
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.2732 | 50 | 1.4704 | 0.4704 | 0.4864 | 0.4704 | 0.4383 |
| No log | 0.5464 | 100 | 1.1159 | 0.5742 | 0.5936 | 0.5742 | 0.5676 |
| No log | 0.8197 | 150 | 0.9276 | 0.6441 | 0.6564 | 0.6441 | 0.6391 |
| No log | 1.0929 | 200 | 0.7966 | 0.7064 | 0.7146 | 0.7064 | 0.7057 |
| No log | 1.3661 | 250 | 0.7308 | 0.7317 | 0.7408 | 0.7317 | 0.7291 |
| No log | 1.6393 | 300 | 0.6640 | 0.7571 | 0.7624 | 0.7571 | 0.7560 |
| No log | 1.9126 | 350 | 0.5874 | 0.7940 | 0.7975 | 0.7940 | 0.7931 |
| No log | 2.1858 | 400 | 0.6288 | 0.7863 | 0.7958 | 0.7863 | 0.7840 |
| No log | 2.4590 | 450 | 0.5621 | 0.8055 | 0.8128 | 0.8055 | 0.8048 |
| 0.8255 | 2.7322 | 500 | 0.5799 | 0.8094 | 0.8181 | 0.8094 | 0.8087 |
| 0.8255 | 3.0055 | 550 | 0.5560 | 0.7994 | 0.8041 | 0.7994 | 0.7978 |
| 0.8255 | 3.2787 | 600 | 0.5402 | 0.8301 | 0.8341 | 0.8301 | 0.8305 |
| 0.8255 | 3.5519 | 650 | 0.5534 | 0.8201 | 0.8287 | 0.8201 | 0.8197 |
| 0.8255 | 3.8251 | 700 | 0.5439 | 0.8248 | 0.8333 | 0.8248 | 0.8249 |
| 0.8255 | 4.0984 | 750 | 0.5402 | 0.8248 | 0.8304 | 0.8248 | 0.8244 |
| 0.8255 | 4.3716 | 800 | 0.5363 | 0.8271 | 0.8309 | 0.8271 | 0.8262 |
| 0.8255 | 4.6448 | 850 | 0.5515 | 0.8248 | 0.8311 | 0.8248 | 0.8240 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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