Model Trained Using AutoTrain

Dataset

We used the APCD dataset cited hereafter for pretraining the model. The dataset has been cleaned and only the main text and the meter columns 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

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": "قفا نبك من ذِكرى حبيب ومنزلِ  بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"}' https://api-inference.huggingface.co/models/Yah216/Arabic_poem_meter_3

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Yah216/Arabic_poem_meter_3", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Yah216/Arabic_poem_meter_3", use_auth_token=True)

inputs = tokenizer("قفا نبك من ذِكرى حبيب ومنزلِ  بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ", return_tensors="pt")

outputs = model(**inputs)