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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_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
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