---
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: 0.639
---

<!-- 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: 1.4437
- Accuracy: 0.639
- Macro F1: 0.6399

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.9984        | 0.14  | 500   | 0.9957          | 0.5819   | 0.5794   |
| 1.0009        | 0.29  | 1000  | 0.9064          | 0.6161   | 0.6222   |
| 0.9462        | 0.43  | 1500  | 0.9272          | 0.6047   | 0.5906   |
| 0.9037        | 0.57  | 2000  | 0.9866          | 0.5817   | 0.5750   |
| 0.8923        | 0.71  | 2500  | 0.8666          | 0.6124   | 0.5898   |
| 0.905         | 0.86  | 3000  | 0.8855          | 0.5996   | 0.5745   |
| 0.9017        | 1.0   | 3500  | 0.8521          | 0.6276   | 0.6258   |
| 0.8487        | 1.14  | 4000  | 0.8540          | 0.6309   | 0.6292   |
| 0.8042        | 1.29  | 4500  | 0.8534          | 0.6323   | 0.6294   |
| 0.8165        | 1.43  | 5000  | 0.8350          | 0.6347   | 0.6389   |
| 0.8224        | 1.57  | 5500  | 0.8687          | 0.6321   | 0.6279   |
| 0.7799        | 1.71  | 6000  | 0.8810          | 0.6316   | 0.6298   |
| 0.7354        | 1.86  | 6500  | 0.8719          | 0.639    | 0.6346   |
| 0.8026        | 2.0   | 7000  | 0.8829          | 0.6159   | 0.6154   |
| 0.6818        | 2.14  | 7500  | 0.9274          | 0.6383   | 0.6408   |
| 0.6704        | 2.29  | 8000  | 0.9327          | 0.6401   | 0.6377   |
| 0.6498        | 2.43  | 8500  | 0.8786          | 0.6367   | 0.6414   |
| 0.6956        | 2.57  | 9000  | 0.9165          | 0.6374   | 0.6320   |
| 0.6729        | 2.71  | 9500  | 0.9929          | 0.6116   | 0.6153   |
| 0.6963        | 2.86  | 10000 | 0.8843          | 0.6397   | 0.6418   |
| 0.6795        | 3.0   | 10500 | 0.9204          | 0.6471   | 0.6492   |
| 0.536         | 3.14  | 11000 | 1.0496          | 0.641    | 0.6447   |
| 0.5212        | 3.29  | 11500 | 1.0836          | 0.6466   | 0.6466   |
| 0.5278        | 3.43  | 12000 | 1.0635          | 0.6377   | 0.6420   |
| 0.5631        | 3.57  | 12500 | 1.0144          | 0.6436   | 0.6449   |
| 0.4899        | 3.71  | 13000 | 1.1613          | 0.6416   | 0.6420   |
| 0.509         | 3.86  | 13500 | 1.0841          | 0.6446   | 0.6442   |
| 0.5176        | 4.0   | 14000 | 1.0819          | 0.639    | 0.6426   |
| 0.3587        | 4.14  | 14500 | 1.3046          | 0.6401   | 0.6412   |
| 0.4342        | 4.29  | 15000 | 1.3250          | 0.6371   | 0.6394   |
| 0.3358        | 4.43  | 15500 | 1.4140          | 0.6387   | 0.6395   |
| 0.3773        | 4.57  | 16000 | 1.4286          | 0.6399   | 0.6416   |
| 0.4173        | 4.71  | 16500 | 1.4825          | 0.6393   | 0.6396   |
| 0.4072        | 4.86  | 17000 | 1.4357          | 0.6393   | 0.6405   |
| 0.3743        | 5.0   | 17500 | 1.4437          | 0.639    | 0.6399   |


### Framework versions

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