transformers safetensors deberta-v2 text-classification nycu-112-2-datamining-hw2 generated_from_trainer en dataset:DandinPower/review_onlytitleandtext base_model:microsoft/deberta-v2-xlarge base_model:finetune:microsoft/deberta-v2-xlarge license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/deberta-v2-xlarge
datasets:
- DandinPower/review_onlytitleandtext
language:
- en
license: mit
metrics:
- accuracy
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
model-index:
- name: deberta-v2-xlarge-otat-recommened-hp
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: DandinPower/review_onlytitleandtext
      type: DandinPower/review_onlytitleandtext
    metrics:
    - type: accuracy
      value: 0.6777142857142857
      name: Accuracy
---

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# deberta-v2-xlarge-otat-recommened-hp

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: 0.7741
- Accuracy: 0.6777
- Macro F1: 0.6756

## 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: 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: 1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.7904        | 1.14  | 500  | 0.8056          | 0.6661   | 0.6641   |
| 0.7232        | 2.29  | 1000 | 0.7701          | 0.6783   | 0.6757   |
| 0.6944        | 3.43  | 1500 | 0.7669          | 0.681    | 0.6802   |
| 0.6795        | 4.57  | 2000 | 0.7741          | 0.6777   | 0.6756   |


### Framework versions

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