| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-bert/bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: multiple_choice_model |
| 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. --> |
|
|
| # multiple_choice_model |
|
|
| 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: 2.0560 |
| - Accuracy: 0.751 |
|
|
| ## 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: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.9281 | 1.0 | 563 | 0.7698 | 0.723 | |
| | 0.4148 | 2.0 | 1126 | 0.8325 | 0.726 | |
| | 0.2001 | 3.0 | 1689 | 1.4160 | 0.712 | |
| | 0.1073 | 4.0 | 2252 | 1.4036 | 0.726 | |
| | 0.0671 | 5.0 | 2815 | 1.8364 | 0.735 | |
| | 0.0386 | 6.0 | 3378 | 1.8075 | 0.739 | |
| | 0.019 | 7.0 | 3941 | 1.9959 | 0.748 | |
| | 0.0054 | 8.0 | 4504 | 2.0279 | 0.753 | |
| | 0.0016 | 9.0 | 5067 | 2.0335 | 0.752 | |
| | 0.0021 | 10.0 | 5630 | 2.0560 | 0.751 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.54.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
| |
