---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: polar4
  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. -->

# polar4

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: 0.5187
- Accuracy: 0.7426
- F1: 0.7199
- Precision: 0.7477
- Recall: 0.7426

## 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: 0.0002
- train_batch_size: 200
- eval_batch_size: 200
- 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: 0.2
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6359        | 7.6923  | 100  | 0.6229          | 0.6357   | 0.4941 | 0.4041    | 0.6357 |
| 0.5953        | 15.3846 | 200  | 0.5654          | 0.7039   | 0.6698 | 0.7030    | 0.7039 |
| 0.5605        | 23.0769 | 300  | 0.5510          | 0.6930   | 0.6297 | 0.7259    | 0.6930 |
| 0.5578        | 30.7692 | 400  | 0.5273          | 0.7426   | 0.7384 | 0.7376    | 0.7426 |
| 0.5635        | 38.4615 | 500  | 0.5198          | 0.7364   | 0.7257 | 0.7301    | 0.7364 |
| 0.5443        | 46.1538 | 600  | 0.5214          | 0.7271   | 0.6978 | 0.7338    | 0.7271 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1