---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_cleanonlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-cotat
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_cleanonlytitleandtext
      type: DandinPower/review_cleanonlytitleandtext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.623
---

<!-- 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-cotat

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co./microsoft/deberta-v3-base) on the DandinPower/review_cleanonlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4985
- Accuracy: 0.623
- Macro F1: 0.6247

## 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: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.0223        | 0.14  | 500   | 0.9610          | 0.592    | 0.5971   |
| 1.0108        | 0.29  | 1000  | 0.9378          | 0.6044   | 0.6083   |
| 0.9323        | 0.43  | 1500  | 0.9605          | 0.589    | 0.5652   |
| 0.9651        | 0.57  | 2000  | 0.9845          | 0.5797   | 0.5687   |
| 0.928         | 0.71  | 2500  | 0.9521          | 0.5907   | 0.5656   |
| 0.9205        | 0.86  | 3000  | 0.9073          | 0.603    | 0.5740   |
| 0.9243        | 1.0   | 3500  | 0.8876          | 0.616    | 0.6113   |
| 0.8545        | 1.14  | 4000  | 0.8631          | 0.6267   | 0.6290   |
| 0.8267        | 1.29  | 4500  | 0.8908          | 0.624    | 0.6185   |
| 0.8175        | 1.43  | 5000  | 0.8771          | 0.6173   | 0.6222   |
| 0.8613        | 1.57  | 5500  | 0.9564          | 0.6209   | 0.6081   |
| 0.8138        | 1.71  | 6000  | 0.9246          | 0.6089   | 0.6063   |
| 0.7314        | 1.86  | 6500  | 0.9030          | 0.6329   | 0.6313   |
| 0.8287        | 2.0   | 7000  | 0.8753          | 0.6211   | 0.6235   |
| 0.6963        | 2.14  | 7500  | 0.9700          | 0.6247   | 0.6257   |
| 0.7034        | 2.29  | 8000  | 0.9592          | 0.6234   | 0.6220   |
| 0.679         | 2.43  | 8500  | 0.8994          | 0.6233   | 0.6272   |
| 0.7207        | 2.57  | 9000  | 1.0013          | 0.6236   | 0.6183   |
| 0.6992        | 2.71  | 9500  | 0.9385          | 0.6169   | 0.6219   |
| 0.7032        | 2.86  | 10000 | 0.9247          | 0.6366   | 0.6364   |
| 0.6949        | 3.0   | 10500 | 0.9615          | 0.6239   | 0.6281   |
| 0.5581        | 3.14  | 11000 | 1.0439          | 0.6217   | 0.6267   |
| 0.55          | 3.29  | 11500 | 1.1205          | 0.6259   | 0.6232   |
| 0.5496        | 3.43  | 12000 | 1.1122          | 0.6226   | 0.6267   |
| 0.5462        | 3.57  | 12500 | 1.0692          | 0.6251   | 0.6263   |
| 0.5121        | 3.71  | 13000 | 1.1563          | 0.6197   | 0.6214   |
| 0.531         | 3.86  | 13500 | 1.1123          | 0.6261   | 0.6256   |
| 0.5256        | 4.0   | 14000 | 1.1194          | 0.6247   | 0.6264   |
| 0.3908        | 4.14  | 14500 | 1.3631          | 0.6204   | 0.6210   |
| 0.4439        | 4.29  | 15000 | 1.4810          | 0.6204   | 0.6211   |
| 0.4252        | 4.43  | 15500 | 1.4454          | 0.6211   | 0.6217   |
| 0.3721        | 4.57  | 16000 | 1.5315          | 0.6204   | 0.6231   |
| 0.369         | 4.71  | 16500 | 1.4797          | 0.6184   | 0.6190   |
| 0.3907        | 4.86  | 17000 | 1.4857          | 0.6219   | 0.6234   |
| 0.4022        | 5.0   | 17500 | 1.4985          | 0.623    | 0.6247   |


### Framework versions

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