README.md

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

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1529
- Precision: 0.7757
- Recall: 0.7782
- F1: 0.7769
- Accuracy: 0.8075

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6211        | 1.0   | 769  | 0.7439          | 0.8403    | 0.6654 | 0.6824 | 0.7854   |
| 0.5518        | 2.0   | 1538 | 0.4591          | 0.7945    | 0.7469 | 0.7629 | 0.8114   |
| 0.4051        | 3.0   | 2307 | 0.7194          | 0.7718    | 0.7674 | 0.7695 | 0.8036   |
| 0.2264        | 4.0   | 3076 | 0.9925          | 0.7918    | 0.7546 | 0.7682 | 0.8127   |
| 0.166         | 5.0   | 3845 | 1.1529          | 0.7757    | 0.7782 | 0.7769 | 0.8075   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1