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---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: polar3
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. -->
# polar3
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: 0.5548
- Accuracy: 0.7023
- F1: 0.6556
- Precision: 0.7159
- Recall: 0.7023
## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6437 | 4.7619 | 100 | 0.6451 | 0.6357 | 0.4941 | 0.4041 | 0.6357 |
| 0.6315 | 9.5238 | 200 | 0.6163 | 0.6372 | 0.4976 | 0.7690 | 0.6372 |
| 0.6185 | 14.2857 | 300 | 0.5877 | 0.6558 | 0.5621 | 0.6656 | 0.6558 |
| 0.5981 | 19.0476 | 400 | 0.5718 | 0.6713 | 0.5907 | 0.6980 | 0.6713 |
| 0.5733 | 23.8095 | 500 | 0.5548 | 0.7023 | 0.6556 | 0.7159 | 0.7023 |
| 0.5597 | 28.5714 | 600 | 0.5411 | 0.7256 | 0.7070 | 0.7208 | 0.7256 |
| 0.5608 | 33.3333 | 700 | 0.5329 | 0.7287 | 0.7097 | 0.7250 | 0.7287 |
| 0.5588 | 38.0952 | 800 | 0.5269 | 0.7473 | 0.7445 | 0.7434 | 0.7473 |
| 0.5375 | 42.8571 | 900 | 0.5199 | 0.7380 | 0.7236 | 0.7334 | 0.7380 |
| 0.5352 | 47.6190 | 1000 | 0.5279 | 0.7054 | 0.6546 | 0.7296 | 0.7054 |
| 0.5461 | 52.3810 | 1100 | 0.5118 | 0.7395 | 0.7233 | 0.7365 | 0.7395 |
| 0.5356 | 57.1429 | 1200 | 0.5212 | 0.7116 | 0.6642 | 0.7364 | 0.7116 |
| 0.5313 | 61.9048 | 1300 | 0.5093 | 0.7597 | 0.7598 | 0.7599 | 0.7597 |
| 0.5327 | 66.6667 | 1400 | 0.5051 | 0.7411 | 0.7229 | 0.7402 | 0.7411 |
| 0.5403 | 71.4286 | 1500 | 0.5077 | 0.7333 | 0.7076 | 0.7382 | 0.7333 |
| 0.5456 | 76.1905 | 1600 | 0.5043 | 0.7349 | 0.7131 | 0.7357 | 0.7349 |
| 0.5342 | 80.9524 | 1700 | 0.5050 | 0.7318 | 0.7070 | 0.7348 | 0.7318 |
| 0.5307 | 85.7143 | 1800 | 0.5016 | 0.7364 | 0.7164 | 0.7359 | 0.7364 |
| 0.5192 | 90.4762 | 1900 | 0.4999 | 0.7457 | 0.7310 | 0.7430 | 0.7457 |
| 0.5404 | 95.2381 | 2000 | 0.5012 | 0.7349 | 0.7144 | 0.7343 | 0.7349 |
| 0.5241 | 100.0 | 2100 | 0.5006 | 0.7411 | 0.7223 | 0.7408 | 0.7411 |
### Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
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