---
language:
- en
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_cleanonlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v3-xsmall-cotat-recommened-hp
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_cleanonlytitleandtext
      type: DandinPower/review_cleanonlytitleandtext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6262857142857143
---

<!-- 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-xsmall-cotat-recommened-hp

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the DandinPower/review_cleanonlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8783
- Accuracy: 0.6263
- Macro F1: 0.6285

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 1.61          | 0.4571 | 100  | 1.6076          | 0.22     | 0.1631   |
| 1.5063        | 0.9143 | 200  | 1.2854          | 0.4094   | 0.2942   |
| 1.2016        | 1.3714 | 300  | 1.0481          | 0.5529   | 0.5311   |
| 1.0219        | 1.8286 | 400  | 0.9338          | 0.6093   | 0.6020   |
| 0.9362        | 2.2857 | 500  | 0.8919          | 0.6261   | 0.6239   |
| 0.9097        | 2.7429 | 600  | 0.8783          | 0.6263   | 0.6285   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1