|
--- |
|
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 |
|
|
