---
language:
- en
license: mit
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
base_model: microsoft/deberta-v2-xxlarge
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v2-xxlarge-otat-small-lr
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - type: accuracy
      value: 0.668
      name: Accuracy
---

<!-- 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-xxlarge-otat-small-lr

This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co./microsoft/deberta-v2-xxlarge) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7982
- Accuracy: 0.668
- Macro F1: 0.6665

## 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: 1.8e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.6073        | 0.23  | 100  | 1.5910          | 0.2409   | 0.1625   |
| 1.5142        | 0.46  | 200  | 1.2862          | 0.439    | 0.3770   |
| 1.0421        | 0.69  | 300  | 0.8956          | 0.617    | 0.6084   |
| 0.8818        | 0.91  | 400  | 0.8344          | 0.6487   | 0.6462   |
| 0.8309        | 1.14  | 500  | 0.8180          | 0.6586   | 0.6575   |
| 0.8029        | 1.37  | 600  | 0.8090          | 0.6603   | 0.6589   |
| 0.7949        | 1.6   | 700  | 0.8124          | 0.6613   | 0.6538   |
| 0.7847        | 1.83  | 800  | 0.7775          | 0.6696   | 0.6698   |
| 0.7717        | 2.06  | 900  | 0.7727          | 0.6703   | 0.6699   |
| 0.7445        | 2.29  | 1000 | 0.7767          | 0.669    | 0.6646   |
| 0.7367        | 2.51  | 1100 | 0.7774          | 0.6693   | 0.6676   |
| 0.7419        | 2.74  | 1200 | 0.7580          | 0.674    | 0.6743   |
| 0.7394        | 2.97  | 1300 | 0.7660          | 0.6714   | 0.6722   |
| 0.7253        | 3.2   | 1400 | 0.7695          | 0.6717   | 0.6740   |
| 0.7155        | 3.43  | 1500 | 0.7623          | 0.6676   | 0.6699   |
| 0.7089        | 3.66  | 1600 | 0.7762          | 0.6687   | 0.6630   |
| 0.7041        | 3.89  | 1700 | 0.7670          | 0.6716   | 0.6719   |
| 0.6982        | 4.11  | 1800 | 0.7735          | 0.6699   | 0.6659   |
| 0.6778        | 4.34  | 1900 | 0.7676          | 0.6701   | 0.6676   |
| 0.6919        | 4.57  | 2000 | 0.7772          | 0.6717   | 0.6692   |
| 0.6919        | 4.8   | 2100 | 0.7751          | 0.6687   | 0.6662   |
| 0.6721        | 5.03  | 2200 | 0.7955          | 0.6666   | 0.6613   |
| 0.6576        | 5.26  | 2300 | 0.7765          | 0.6714   | 0.6720   |
| 0.6675        | 5.49  | 2400 | 0.7900          | 0.6703   | 0.6711   |
| 0.6641        | 5.71  | 2500 | 0.7780          | 0.6689   | 0.6676   |
| 0.6669        | 5.94  | 2600 | 0.7751          | 0.6687   | 0.6675   |
| 0.6368        | 6.17  | 2700 | 0.7995          | 0.6691   | 0.6690   |
| 0.647         | 6.4   | 2800 | 0.7962          | 0.668    | 0.6635   |
| 0.6285        | 6.63  | 2900 | 0.7861          | 0.6699   | 0.6702   |
| 0.6656        | 6.86  | 3000 | 0.7939          | 0.6706   | 0.6695   |
| 0.6397        | 7.09  | 3100 | 0.7876          | 0.668    | 0.6672   |
| 0.6252        | 7.31  | 3200 | 0.8001          | 0.669    | 0.6671   |
| 0.6378        | 7.54  | 3300 | 0.8006          | 0.6687   | 0.6675   |
| 0.6243        | 7.77  | 3400 | 0.7982          | 0.668    | 0.6665   |


### Framework versions

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