---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_mergeallfeaturetotext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-maftt
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_mergeallfeaturetotext
      type: DandinPower/review_mergeallfeaturetotext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6288571428571429
---

<!-- 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-maftt

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_mergeallfeaturetotext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4616
- Accuracy: 0.6289
- Macro F1: 0.6302

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.0302        | 0.14  | 500   | 1.0771          | 0.5511   | 0.5499   |
| 1.0412        | 0.29  | 1000  | 0.9406          | 0.5966   | 0.6030   |
| 0.9494        | 0.43  | 1500  | 0.9546          | 0.5949   | 0.5602   |
| 0.898         | 0.57  | 2000  | 1.0436          | 0.5957   | 0.5872   |
| 0.9171        | 0.71  | 2500  | 0.9004          | 0.622    | 0.6074   |
| 0.8856        | 0.86  | 3000  | 0.8741          | 0.6137   | 0.5990   |
| 0.9359        | 1.0   | 3500  | 0.8821          | 0.6267   | 0.6245   |
| 0.8626        | 1.14  | 4000  | 0.8859          | 0.6213   | 0.6200   |
| 0.7953        | 1.29  | 4500  | 0.8606          | 0.6337   | 0.6271   |
| 0.8206        | 1.43  | 5000  | 0.8543          | 0.6169   | 0.6202   |
| 0.8184        | 1.57  | 5500  | 0.9360          | 0.6266   | 0.6165   |
| 0.8044        | 1.71  | 6000  | 0.8606          | 0.6234   | 0.6227   |
| 0.7094        | 1.86  | 6500  | 0.8842          | 0.6434   | 0.6387   |
| 0.8264        | 2.0   | 7000  | 0.9063          | 0.612    | 0.6128   |
| 0.6951        | 2.14  | 7500  | 0.8782          | 0.6386   | 0.6415   |
| 0.704         | 2.29  | 8000  | 0.9510          | 0.6326   | 0.6308   |
| 0.6806        | 2.43  | 8500  | 0.8709          | 0.6413   | 0.6455   |
| 0.6983        | 2.57  | 9000  | 0.8977          | 0.6426   | 0.6436   |
| 0.6852        | 2.71  | 9500  | 0.9686          | 0.5984   | 0.6010   |
| 0.6761        | 2.86  | 10000 | 0.8961          | 0.6386   | 0.6406   |
| 0.6804        | 3.0   | 10500 | 0.9378          | 0.6307   | 0.6332   |
| 0.5329        | 3.14  | 11000 | 1.1209          | 0.6341   | 0.6382   |
| 0.5461        | 3.29  | 11500 | 1.0323          | 0.6393   | 0.6377   |
| 0.5725        | 3.43  | 12000 | 1.0678          | 0.6334   | 0.6366   |
| 0.5499        | 3.57  | 12500 | 1.0547          | 0.6374   | 0.6394   |
| 0.5218        | 3.71  | 13000 | 1.0524          | 0.6453   | 0.6460   |
| 0.5022        | 3.86  | 13500 | 1.1100          | 0.6363   | 0.6358   |
| 0.534         | 4.0   | 14000 | 1.0378          | 0.6357   | 0.6386   |
| 0.3823        | 4.14  | 14500 | 1.3985          | 0.6357   | 0.6357   |
| 0.4518        | 4.29  | 15000 | 1.3265          | 0.6314   | 0.6318   |
| 0.4147        | 4.43  | 15500 | 1.3946          | 0.631    | 0.6324   |
| 0.3936        | 4.57  | 16000 | 1.4649          | 0.6279   | 0.6308   |
| 0.4339        | 4.71  | 16500 | 1.5322          | 0.6286   | 0.6314   |
| 0.4448        | 4.86  | 17000 | 1.4890          | 0.629    | 0.6302   |
| 0.4006        | 5.0   | 17500 | 1.4616          | 0.6289   | 0.6302   |


### Framework versions

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