---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-category
  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-category

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3595
- Macro F1: 0.7766
- Weighted F1: 0.8732
- Accuracy: 0.8728

## 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: 32
- eval_batch_size: 32
- 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: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|
| 0.4592        | 0.4840 | 500  | 0.4634          | 0.5938   | 0.7383      | 0.7330   |
| 0.2541        | 0.9681 | 1000 | 0.3037          | 0.6894   | 0.8289      | 0.8264   |
| 0.1975        | 1.4521 | 1500 | 0.3295          | 0.7422   | 0.8453      | 0.8458   |
| 0.1439        | 1.9361 | 2000 | 0.2862          | 0.7455   | 0.8511      | 0.8488   |
| 0.1674        | 2.4201 | 2500 | 0.2982          | 0.7427   | 0.8633      | 0.8629   |
| 0.1932        | 2.9042 | 3000 | 0.2622          | 0.7373   | 0.8593      | 0.8589   |
| 0.1468        | 3.3882 | 3500 | 0.3017          | 0.7825   | 0.8622      | 0.8627   |
| 0.0525        | 3.8722 | 4000 | 0.3540          | 0.7629   | 0.8622      | 0.8602   |
| 0.0977        | 4.3562 | 4500 | 0.3557          | 0.7677   | 0.8598      | 0.8572   |
| 0.0696        | 4.8403 | 5000 | 0.3595          | 0.7766   | 0.8732      | 0.8728   |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2