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library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-base_LOGIC_Native
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. -->
# deberta-v3-base_LOGIC_Native
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.1367
- Macro Precision: 0.0105
- Macro F1: 0.0185
## 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: 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:--------:|
| No log | 1.0 | 116 | nan | 0.1367 | 0.0105 | 0.0185 |
| No log | 2.0 | 232 | nan | 0.1367 | 0.0105 | 0.0185 |
| No log | 3.0 | 348 | nan | 0.1367 | 0.0105 | 0.0185 |
| No log | 4.0 | 464 | nan | 0.1367 | 0.0105 | 0.0185 |
| 2.4862 | 5.0 | 580 | nan | 0.1367 | 0.0105 | 0.0185 |
| 2.4862 | 6.0 | 696 | nan | 0.1367 | 0.0105 | 0.0185 |
| 2.4862 | 7.0 | 812 | nan | 0.1367 | 0.0105 | 0.0185 |
| 2.4862 | 8.0 | 928 | nan | 0.1367 | 0.0105 | 0.0185 |
| 0.0 | 9.0 | 1044 | nan | 0.1367 | 0.0105 | 0.0185 |
| 0.0 | 10.0 | 1160 | nan | 0.1367 | 0.0105 | 0.0185 |
| 0.0 | 11.0 | 1276 | nan | 0.1367 | 0.0105 | 0.0185 |
| 0.0 | 12.0 | 1392 | nan | 0.1367 | 0.0105 | 0.0185 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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