Arabic_poem_meter_3 /
Yah216's picture
language: ar
- text: "قفا نبك من ذِكرى حبيب ومنزلِ بسِقطِ اللِّوى بينَ الدَّخول فحَوْملِ"
- text: "سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتابا"
co2_eq_emissions: 404.66986451902227
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- CO2 Emissions (in grams): 404.66986451902227
## 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:
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 = {}
## Validation Metrics
- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587
## 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"}'
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)