
# 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:
```
@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: 0.21315555274486542
- 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"}' 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)
```
{"language": "ar", "widget": [{"text": "\u0642\u0641\u0627 \u0646\u0628\u0643 \u0645\u0646 \u0630\u0650\u0643\u0631\u0649 \u062d\u0628\u064a\u0628 \u0648\u0645\u0646\u0632\u0644\u0650  \u0628\u0633\u0650\u0642\u0637\u0650 \u0627\u0644\u0644\u0650\u0651\u0648\u0649 \u0628\u064a\u0646\u064e \u0627\u0644\u062f\u064e\u0651\u062e\u0648\u0644 \u0641\u062d\u064e\u0648\u0652\u0645\u0644\u0650"}, {"text": "\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\u0627"}], "co2_eq_emissions": 404.66986451902227}
transformers pytorch bert text-classification ar co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #ar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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:

## Validation Metrics

- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587

## Usage

You can use cURL to access this model:



Or Python API:


# 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: ## Validation Metrics

- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #ar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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: ## Validation Metrics

- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #ar #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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:## Validation Metrics

- Loss: 0.21315555274486542
- Accuracy: 0.9493554089595999
- Macro F1: 0.7537353091512587## Usage

You can use cURL to access this model:



Or Python API: