---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-base-DIALOCONAN-WIKI-CLS
  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-DIALOCONAN-WIKI-CLS

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: 0.3828
- Precision: 0.7060
- Recall: 0.7086
- F1: 0.7072
- Accuracy: 0.9422

## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4295        | 1.0   | 2500  | 0.5694          | 0.6816    | 0.6816 | 0.6793 | 0.9040   |
| 0.3525        | 2.0   | 5000  | 0.4852          | 0.6923    | 0.6938 | 0.6928 | 0.9225   |
| 0.2604        | 3.0   | 7500  | 0.4372          | 0.6993    | 0.7005 | 0.6995 | 0.9314   |
| 0.1979        | 4.0   | 10000 | 0.4076          | 0.7056    | 0.7077 | 0.7065 | 0.9410   |
| 0.1295        | 5.0   | 12500 | 0.3828          | 0.7060    | 0.7086 | 0.7072 | 0.9422   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1