---
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