metadata
language:
- en
thumbnail: https://github.com/AI-Ahmed
tags:
- classification
license: cc-by-4.0
datasets:
- SetFit/qqp
models:
- microsoft/deberta-v3-base
metrics:
- accuracy
- loss
pipeline_tag: text-classification
widget:
- text: >-
How is the life of a math student? Could you describe your own
experiences?
pair: Which level of preparation is enough for the exam jlpt5?
example_title: Similarity Detection.
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
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
{"Training Loss": 0.132400,
"Validation Loss": 0.217410,
"Validation Accuracy": 0.917969
}
Model Dependencies
{"Main Model": "microsoft/deberta-v3-base",
"Dataset": "SetFit/qqp"
}
Training Monitoring & Performance
Information Citation
@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}
}