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---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- classification
license: "mit"
datasets:
- SetFit/qqp
models:
- microsoft/deberta-v3-base
metrics:
- accuracy
- loss
widget:
- text: How is the life of a math student? Could you describe your own experiences?
context: Which level of preparation is enough for the exam jlpt5?
example_title: "Classification"
---
A fine-tuned model based on the **DeBERTaV3** model of Microsoft and fine-tuned on **Glue QQP**, which detects the linguistical similarities between two questions and whether they are similar questions or duplicates.
## Model Hyperparameters
```python
epoch=4
per_device_train_batch_size=32
per_device_eval_batch_size=16
lr=2e-5
weight_decay=1e-2
gradient_checkpointing=True
gradient_accumulation_steps=8
```
## Model Performance
```JSON
{"Training Loss": 0.132400,
"Validation Loss": 0.217410,
"Validation Accuracy": 0.917969
}
```
## Model Dependencies
```JSON
{"Main Model": "microsoft/deberta-v3-base",
"Dataset": "SetFit/qqp"
}
```
## Information Citation
```bibtex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
``` |