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library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
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
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