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---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lge_tests_prelim
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. -->
# lge_tests_prelim
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4324
- Accuracy: 0.64
## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0 | 0 | 2.6277 | 0.0 |
| 2.487 | 0.0320 | 100 | 2.4830 | 0.0 |
| 2.3341 | 0.0641 | 200 | 2.3221 | 0.0 |
| 2.2138 | 0.0961 | 300 | 2.2363 | 0.0 |
| 2.0317 | 0.1281 | 400 | 2.0567 | 0.0 |
| 1.8181 | 0.1602 | 500 | 1.7764 | 0.005 |
| 1.5427 | 0.1922 | 600 | 1.5209 | 0.015 |
| 1.3683 | 0.2242 | 700 | 1.3421 | 0.015 |
| 1.1867 | 0.2562 | 800 | 1.1883 | 0.045 |
| 1.1158 | 0.2883 | 900 | 1.1456 | 0.035 |
| 1.1237 | 0.3203 | 1000 | 1.0221 | 0.04 |
| 0.9856 | 0.3523 | 1100 | 0.9365 | 0.14 |
| 0.8885 | 0.3844 | 1200 | 0.8694 | 0.16 |
| 0.8273 | 0.4164 | 1300 | 0.8421 | 0.115 |
| 0.8084 | 0.4484 | 1400 | 0.8112 | 0.14 |
| 0.7671 | 0.4805 | 1500 | 0.7577 | 0.145 |
| 0.6999 | 0.5125 | 1600 | 0.6785 | 0.33 |
| 0.6531 | 0.5445 | 1700 | 0.6651 | 0.325 |
| 0.6251 | 0.5766 | 1800 | 0.6239 | 0.365 |
| 0.5899 | 0.6086 | 1900 | 0.5955 | 0.375 |
| 0.5622 | 0.6406 | 2000 | 0.5660 | 0.42 |
| 0.5719 | 0.6726 | 2100 | 0.5642 | 0.365 |
| 0.5585 | 0.7047 | 2200 | 0.5228 | 0.495 |
| 0.514 | 0.7367 | 2300 | 0.4972 | 0.575 |
| 0.5052 | 0.7687 | 2400 | 0.4992 | 0.49 |
| 0.4651 | 0.8008 | 2500 | 0.4654 | 0.585 |
| 0.4473 | 0.8328 | 2600 | 0.4556 | 0.65 |
| 0.4548 | 0.8648 | 2700 | 0.4506 | 0.575 |
| 0.4576 | 0.8969 | 2800 | 0.4450 | 0.57 |
| 0.4343 | 0.9289 | 2900 | 0.4344 | 0.67 |
| 0.4262 | 0.9609 | 3000 | 0.4328 | 0.67 |
| 0.4298 | 0.9930 | 3100 | 0.4324 | 0.64 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1
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