---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the KMC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0556
- Accuracy: 0.8722
- F1: 0.8849
- Precision: 0.8950
- Recall: 0.8750

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0894        | 1.0   | 1633 | 0.0817          | 0.7508   | 0.8072 | 0.8774    | 0.7474 |
| 0.059         | 2.0   | 3266 | 0.0620          | 0.8264   | 0.8520 | 0.8815    | 0.8243 |
| 0.0429        | 3.0   | 4899 | 0.0531          | 0.8563   | 0.8751 | 0.8947    | 0.8563 |
| 0.032         | 4.0   | 6532 | 0.0547          | 0.8704   | 0.8838 | 0.8976    | 0.8705 |
| 0.0253        | 5.0   | 8165 | 0.0556          | 0.8722   | 0.8849 | 0.8950    | 0.8750 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1