
# 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)
```
{"language": "ar", "datasets": ["Yah216/Poem_Rawiy_detection"], "co2_eq_emissions": 1.8046766441629636, "widget": ["\u0633\u064e\u0644\u0648 \u0642\u064e\u0644\u0628\u064a \u063a\u064e\u062f\u0627\u0629\u064e \u0633\u064e\u0644\u0627 \u0648\u064e\u062b\u0627\u0628\u0627 \u0644\u064e\u0639\u064e\u0644\u064e\u0651 \u0639\u064e\u0644\u0649 \u0627\u0644\u062c\u064e\u0645\u0627\u0644\u0650 \u0644\u064e\u0647\u064f \u0639\u0650\u062a\u0627\u0628"]}
transformers pytorch bert text-classification ar dataset:Yah216/Poem_Rawiy_detection co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Yah216/Poem_Rawiy_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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:


## 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:



Or Python API:


# 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: ## 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:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Yah216/Poem_Rawiy_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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: ## 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:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #ar #dataset-Yah216/Poem_Rawiy_detection #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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:## 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:



Or Python API: