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Model

  • Problem type: Multi-class Classification
  • CO2 Emissions (in grams): 1.8046766441629636

Dataset

We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the Qafiyah column were kept:

@Article{Yousef2019LearningMetersArabicEnglish-arxiv,
  author =       {Yousef, Waleed A. and Ibrahime, Omar M. and Madbouly, Taha M. and Mahmoud,
                  Moustafa A.},
  title =        {Learning Meters of Arabic and English Poems With Recurrent Neural Networks: a Step
                  Forward for Language Understanding and Synthesis},
  journal =      {arXiv preprint arXiv:1905.05700},
  year =         2019,
  url =          {https://github.com/hci-lab/LearningMetersPoems}
}

Validation Metrics

  • Loss: 0.398613303899765
  • Accuracy: 0.912351981006084
  • Macro F1: 0.717311758991278
  • Micro F1: 0.912351981006084
  • Weighted F1: 0.9110094798809955

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Yah216/Poem_Rawiy_detection

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Yah216/Poem_Qafiyah_Detection", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Yah216/Poem_Qafiyah_Detection", use_auth_token=True)

inputs = tokenizer("text, return_tensors="pt")

outputs = model(**inputs)
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