
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645

## Validation Metrics

- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409


## 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/zenkri/autotrain-Arabic_Poetry_by_Subject-920730230
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("zenkri/autotrain-Arabic_Poetry_by_Subject-920730230", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
```
{"language": "ar", "tags": "autotrain", "datasets": ["zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.07445219847409645}
transformers pytorch bert text-classification autotrain ar dataset:zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645

## Validation Metrics

- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645 ## Validation Metrics

- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645 ## Validation Metrics

- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-zenkri/autotrain-data-Arabic_Poetry_by_Subject-1d8ba412 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 920730230
- CO2 Emissions (in grams): 0.07445219847409645## Validation Metrics

- Loss: 0.5806193351745605
- Accuracy: 0.8785200718993409
- Macro F1: 0.8208042310550474
- Micro F1: 0.8785200718993409
- Weighted F1: 0.8783590365809876
- Macro Precision: 0.8486540338838363
- Micro Precision: 0.8785200718993409
- Weighted Precision: 0.8815185727115001
- Macro Recall: 0.8121110408113442
- Micro Recall: 0.8785200718993409
- Weighted Recall: 0.8785200718993409## Usage

You can use cURL to access this model:



Or Python API: