README.md · AI-Ahmed/deberta-v3-base-funetuned-cls-qqa at main

metadata

language:
  - en
license: cc-by-4.0
tags:
  - classification
datasets:
  - SetFit/qqp
metrics:
  - accuracy
  - loss
thumbnail: https://github.com/AI-Ahmed
models:
  - microsoft/deberta-v3-base
pipeline_tag: text-classification
widget:
  - text: >-
      How is the life of a math student? Could you describe your own
      experiences? Which level of preparation is enough for the exam jlpt5?
    example_title: Difference Detection.
  - text: What can one do after MBBS? What do i do after my MBBS?
    example_title: Duplicates Detection.
model-index:
  - name: deberta-v3-base-funetuned-cls-qqa
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: qqp
          type: qqp
          config: sst2
          split: validation
        metrics:
          - type: accuracy
            value: 0.917969
            name: Accuracy
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzA2OWM4ZjJkYzZjNmM3YmNkODNhODYzOTMxY2RjZTZmODg4ODA4ZjJmNjFhNjkwZjFmZjk3YjBiNzhjNDAzOCIsInZlcnNpb24iOjF9.TqdmhhV_3fTWYHtM7SJj35ICZgZ6Ux7qYXwPHu8j0MkDmSeZfTniFCqB8LO8pqM1bK5iHQV1-vggZUdMu4spCA
          - type: loss
            value: 0.21741
            name: loss
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGQzZGZjNzZjNzFjOWViNjkyNGIxMGE5ZjA5ODAxOTNiZGQ5OTY4NWM1YThlZGEyZGRjOGE2MjkwYTRjN2Q2MyIsInZlcnNpb24iOjF9.ZxmqxdbOhAA8Ywz8_Q3aFaFG2kmTogFdWjlHgEa2JnGQWhL39VVtcn6A8gtekE_e3z5jsaNS4EnSzYVSWJZjAQ

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 duplicates questions or different.

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

wandb - deberta_qqa_classification

Model Testing

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "AI-Ahmed/deberta-v3-base-funetuned-cls-qqa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenized_input = tokenizer("How is the life of a math student? Could you describe your own experiences? Which level of preparation is enough for the exam jlpt5?", return_tensors="pt")

with torch.no_grad():
  logits = model(**tokenized_input).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

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}
}