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---
language:
- en
license: mit
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
base_model: microsoft/deberta-v2-xlarge
datasets:
- DandinPower/review_onlytitleandtext
metrics:
- accuracy
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
---
<!-- 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-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
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