---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-base_finetuned_nostalgia
  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_finetuned_nostalgia

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3772
- Accuracy: 0.9379
- F1 Macro: 0.9288
- Accuracy Balanced: 0.9264
- F1 Micro: 0.9379
- Precision Macro: 0.9313
- Recall Macro: 0.9264
- Precision Micro: 0.9379
- Recall Micro: 0.9379

## 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: 64
- seed: 1984
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| No log        | 1.0   | 73   | 0.3903          | 0.8759   | 0.8470   | 0.8251            | 0.8759   | 0.8890          | 0.8251       | 0.8759          | 0.8759       |
| No log        | 2.0   | 146  | 0.2130          | 0.9103   | 0.8933   | 0.8783            | 0.9103   | 0.9149          | 0.8783       | 0.9103          | 0.9103       |
| No log        | 3.0   | 219  | 0.1253          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
| No log        | 4.0   | 292  | 0.2694          | 0.9310   | 0.9229   | 0.9324            | 0.9310   | 0.9154          | 0.9324       | 0.9310          | 0.9310       |
| No log        | 5.0   | 365  | 0.1924          | 0.9448   | 0.9370   | 0.9370            | 0.9448   | 0.9370          | 0.9370       | 0.9448          | 0.9448       |
| No log        | 6.0   | 438  | 0.2648          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
| 0.1908        | 7.0   | 511  | 0.3431          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
| 0.1908        | 8.0   | 584  | 0.3450          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |
| 0.1908        | 9.0   | 657  | 0.3538          | 0.9379   | 0.9279   | 0.9209            | 0.9379   | 0.9362          | 0.9209       | 0.9379          | 0.9379       |
| 0.1908        | 10.0  | 730  | 0.3772          | 0.9379   | 0.9288   | 0.9264            | 0.9379   | 0.9313          | 0.9264       | 0.9379          | 0.9379       |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1