
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46622115298
- CO2 Emissions (in grams): 0.4434

## Validation Metrics

- Loss: 0.569
- Accuracy: 0.818
- Macro F1: 0.707
- Micro F1: 0.818
- Weighted F1: 0.807
- Macro Precision: 0.777
- Micro Precision: 0.818
- Weighted Precision: 0.814
- Macro Recall: 0.674
- Micro Recall: 0.818
- Weighted Recall: 0.818


## 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/sefaozalpadl/autotrain-postnashville_antitrans_telegram-46622115298
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("sefaozalpadl/autotrain-postnashville_antitrans_telegram-46622115298", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("sefaozalpadl/autotrain-postnashville_antitrans_telegram-46622115298", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["sefaozalpadl/autotrain-data-postnashville_antitrans_telegram"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.4434488215878769}}
transformers pytorch deberta-v2 text-classification autotrain en dataset:sefaozalpadl/autotrain-data-postnashville_antitrans_telegram co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-sefaozalpadl/autotrain-data-postnashville_antitrans_telegram #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46622115298
- CO2 Emissions (in grams): 0.4434

## Validation Metrics

- Loss: 0.569
- Accuracy: 0.818
- Macro F1: 0.707
- Micro F1: 0.818
- Weighted F1: 0.807
- Macro Precision: 0.777
- Micro Precision: 0.818
- Weighted Precision: 0.814
- Macro Recall: 0.674
- Micro Recall: 0.818
- Weighted Recall: 0.818


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46622115298
- CO2 Emissions (in grams): 0.4434 ## Validation Metrics

- Loss: 0.569
- Accuracy: 0.818
- Macro F1: 0.707
- Micro F1: 0.818
- Weighted F1: 0.807
- Macro Precision: 0.777
- Micro Precision: 0.818
- Weighted Precision: 0.814
- Macro Recall: 0.674
- Micro Recall: 0.818
- Weighted Recall: 0.818 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-sefaozalpadl/autotrain-data-postnashville_antitrans_telegram #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46622115298
- CO2 Emissions (in grams): 0.4434 ## Validation Metrics

- Loss: 0.569
- Accuracy: 0.818
- Macro F1: 0.707
- Micro F1: 0.818
- Weighted F1: 0.807
- Macro Precision: 0.777
- Micro Precision: 0.818
- Weighted Precision: 0.814
- Macro Recall: 0.674
- Micro Recall: 0.818
- Weighted Recall: 0.818 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-sefaozalpadl/autotrain-data-postnashville_antitrans_telegram #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 46622115298
- CO2 Emissions (in grams): 0.4434## Validation Metrics

- Loss: 0.569
- Accuracy: 0.818
- Macro F1: 0.707
- Micro F1: 0.818
- Weighted F1: 0.807
- Macro Precision: 0.777
- Micro Precision: 0.818
- Weighted Precision: 0.814
- Macro Recall: 0.674
- Micro Recall: 0.818
- Weighted Recall: 0.818## Usage

You can use cURL to access this model:



Or Python API: