---
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-recommened-hp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - type: accuracy
      value: 0.6741428571428572
      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-recommened-hp

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: 0.7864
- Accuracy: 0.6741
- Macro F1: 0.6719

## 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: 3e-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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.9641        | 0.46  | 200  | 0.8451          | 0.6327   | 0.6341   |
| 0.8263        | 0.91  | 400  | 0.7768          | 0.6651   | 0.6650   |
| 0.7605        | 1.37  | 600  | 0.7842          | 0.667    | 0.6667   |
| 0.7496        | 1.83  | 800  | 0.7790          | 0.6659   | 0.6650   |
| 0.7034        | 2.29  | 1000 | 0.7738          | 0.67     | 0.6639   |
| 0.7134        | 2.74  | 1200 | 0.7671          | 0.6694   | 0.6698   |
| 0.6839        | 3.2   | 1400 | 0.7754          | 0.6743   | 0.6770   |
| 0.6699        | 3.66  | 1600 | 0.7853          | 0.6711   | 0.6666   |
| 0.6502        | 4.11  | 1800 | 0.7789          | 0.671    | 0.6692   |
| 0.6431        | 4.57  | 2000 | 0.7864          | 0.6741   | 0.6719   |


### Framework versions

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