---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-otat
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6360357142857143
---

<!-- 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-otat

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co./microsoft/deberta-v3-base) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5029
- Accuracy: 0.6360
- Macro F1: 0.6367

## 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: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.9961        | 0.57  | 500  | 0.9958          | 0.5675   | 0.5638   |
| 0.9267        | 1.14  | 1000 | 0.9776          | 0.5814   | 0.5727   |
| 0.9086        | 1.71  | 1500 | 1.1673          | 0.5709   | 0.5355   |
| 0.744         | 2.29  | 2000 | 0.9788          | 0.6325   | 0.6267   |
| 0.7131        | 2.86  | 2500 | 0.9493          | 0.6219   | 0.6203   |
| 0.5815        | 3.43  | 3000 | 0.9966          | 0.6224   | 0.6259   |
| 0.5434        | 4.0   | 3500 | 1.1400          | 0.6336   | 0.6326   |
| 0.3162        | 4.57  | 4000 | 1.5029          | 0.6360   | 0.6367   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2