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library_name: transformers
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: albert-base-v2_fold_9
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-base-v2_fold_9
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1269
- Accuracy: 0.9622
- F1: 0.9586
- Precision: 0.9610
- Recall: 0.9562
## 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: 40
- eval_batch_size: 40
- 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1167 | 1.0 | 15481 | 0.1164 | 0.9543 | 0.9502 | 0.9490 | 0.9515 |
| 0.0913 | 2.0 | 30962 | 0.1177 | 0.9586 | 0.9553 | 0.9453 | 0.9655 |
| 0.0435 | 3.0 | 46443 | 0.1269 | 0.9622 | 0.9586 | 0.9610 | 0.9562 |
### Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2
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