| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: albert/albert-base-v2 |
| tags: |
| - generated_from_trainer |
| - sentiment-analysis |
| metrics: |
| - accuracy |
| model-index: |
| - name: albert-imdb |
| 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. --> |
|
|
| # albert-imdb |
|
|
| This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1795 |
| - Accuracy: 0.9479 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 24 |
| - eval_batch_size: 24 |
| - 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: 2 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.2346 | 0.4997 | 833 | 0.1875 | 0.9291 | |
| | 0.1219 | 0.9994 | 1666 | 0.2007 | 0.9305 | |
| | 0.1825 | 1.4991 | 2499 | 0.2000 | 0.9464 | |
| | 0.1559 | 1.9988 | 3332 | 0.1795 | 0.9479 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
| |
