---
language:
- en
license: mit
base_model: microsoft/deberta-v2-xlarge
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v2-xlarge-otat
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.20114285714285715
---

<!-- 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-v2-xlarge-otat

This model is a fine-tuned version of [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6316
- Accuracy: 0.2011
- Macro F1: 0.0670

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.1994        | 0.14  | 500   | 1.6893          | 0.4029   | 0.3240   |
| 1.6344        | 0.29  | 1000  | 1.6403          | 0.2011   | 0.0670   |
| 1.6413        | 0.43  | 1500  | 1.6270          | 0.2      | 0.0667   |
| 1.6326        | 0.57  | 2000  | 1.6375          | 0.1971   | 0.0659   |
| 1.6128        | 0.71  | 2500  | 1.6604          | 0.2011   | 0.0670   |
| 1.6213        | 0.86  | 3000  | 1.6161          | 0.2      | 0.0667   |
| 1.6199        | 1.0   | 3500  | 1.6132          | 0.2017   | 0.0671   |
| 1.6177        | 1.14  | 4000  | 1.6142          | 0.2011   | 0.0670   |
| 1.6183        | 1.29  | 4500  | 1.6213          | 0.2      | 0.0667   |
| 1.6211        | 1.43  | 5000  | 1.6136          | 0.1971   | 0.0659   |
| 1.6145        | 1.57  | 5500  | 1.6169          | 0.1971   | 0.0659   |
| 1.6187        | 1.71  | 6000  | 1.6160          | 0.2011   | 0.0670   |
| 1.6174        | 1.86  | 6500  | 1.6146          | 0.2      | 0.0667   |
| 1.6164        | 2.0   | 7000  | 1.6181          | 0.2      | 0.0667   |
| 1.6184        | 2.14  | 7500  | 1.6109          | 0.1971   | 0.0659   |
| 1.6152        | 2.29  | 8000  | 1.6189          | 0.2      | 0.0667   |
| 1.6175        | 2.43  | 8500  | 1.6146          | 0.1971   | 0.0659   |
| 1.6134        | 2.57  | 9000  | 1.6160          | 0.1971   | 0.0659   |
| 1.6144        | 2.71  | 9500  | 1.6167          | 0.2011   | 0.0670   |
| 1.6141        | 2.86  | 10000 | 1.6106          | 0.2017   | 0.0671   |
| 1.6128        | 3.0   | 10500 | 1.6139          | 0.1971   | 0.0659   |
| 1.6179        | 3.14  | 11000 | 1.6112          | 0.2      | 0.0667   |
| 1.6096        | 3.29  | 11500 | 1.6127          | 0.2      | 0.0667   |
| 1.6132        | 3.43  | 12000 | 1.6135          | 0.2011   | 0.0670   |
| 1.6053        | 3.57  | 12500 | 1.6186          | 0.2      | 0.0667   |
| 1.6049        | 3.71  | 13000 | 1.6277          | 0.2011   | 0.0670   |
| 1.6044        | 3.86  | 13500 | 1.6271          | 0.2011   | 0.0670   |
| 1.6017        | 4.0   | 14000 | 1.6275          | 0.2011   | 0.0670   |
| 1.608         | 4.14  | 14500 | 1.6192          | 0.2011   | 0.0670   |
| 1.6075        | 4.29  | 15000 | 1.6259          | 0.2011   | 0.0670   |
| 1.601         | 4.43  | 15500 | 1.6267          | 0.2011   | 0.0670   |
| 1.6086        | 4.57  | 16000 | 1.6339          | 0.2011   | 0.0670   |
| 1.5955        | 4.71  | 16500 | 1.6340          | 0.2011   | 0.0670   |
| 1.6013        | 4.86  | 17000 | 1.6322          | 0.2011   | 0.0670   |
| 1.5976        | 5.0   | 17500 | 1.6316          | 0.2011   | 0.0670   |


### Framework versions

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