---
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_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_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: 2.1410
- Accuracy: 0.1591
- Precision: 0.0253
- Recall: 0.1591
- F1: 0.0437

## 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: 850

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.0683 | 50   | 2.0313          | 0.2237   | 0.0856    | 0.2237 | 0.1038 |
| No log        | 0.1366 | 100  | 2.2165          | 0.1337   | 0.0179    | 0.1337 | 0.0316 |
| No log        | 0.2049 | 150  | 2.1366          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| No log        | 0.2732 | 200  | 2.1438          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| No log        | 0.3415 | 250  | 2.1389          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |
| No log        | 0.4098 | 300  | 2.1450          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| No log        | 0.4781 | 350  | 2.1456          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| No log        | 0.5464 | 400  | 2.1396          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| No log        | 0.6148 | 450  | 2.1349          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| 2.1632        | 0.6831 | 500  | 2.1392          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |
| 2.1632        | 0.7514 | 550  | 2.1391          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| 2.1632        | 0.8197 | 600  | 2.1391          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |
| 2.1632        | 0.8880 | 650  | 2.1360          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| 2.1632        | 0.9563 | 700  | 2.1366          | 0.1722   | 0.0296    | 0.1722 | 0.0506 |
| 2.1632        | 1.0246 | 750  | 2.1388          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |
| 2.1632        | 1.0929 | 800  | 2.1410          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |
| 2.1632        | 1.1612 | 850  | 2.1410          | 0.1591   | 0.0253    | 0.1591 | 0.0437 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0