---
language:
- en
license: mit
base_model: microsoft/deberta-v3-large
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v3-large-otat-recommened-hp
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6685714285714286
---

<!-- 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-large-otat-recommened-hp

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8169
- Accuracy: 0.6686
- Macro F1: 0.6662

## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 50
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.7726        | 1.14  | 500  | 0.8107          | 0.6613   | 0.6602   |
| 0.6983        | 2.29  | 1000 | 0.7739          | 0.669    | 0.6662   |
| 0.6504        | 3.43  | 1500 | 0.7891          | 0.6726   | 0.6725   |
| 0.6067        | 4.57  | 2000 | 0.8169          | 0.6686   | 0.6662   |


### Framework versions

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