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---
language: ar
datasets:
- Yah216/Poem_Rawiy_detection
co2_eq_emissions: 1.8046766441629636
widget:
- "سَلو قَلبي غَداةَ سَلا وَثابا لَعَلَّ عَلى الجَمالِ لَهُ عِتاب"
---
# 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)
```