
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3535895495
- CO2 Emissions (in grams): 13.2039

## Validation Metrics

- Loss: 0.761
- Accuracy: 0.763
- Macro F1: 0.124
- Micro F1: 0.763
- Weighted F1: 0.661
- Macro Precision: 0.109
- Micro Precision: 0.763
- Weighted Precision: 0.583
- Macro Recall: 0.143
- Micro Recall: 0.763
- Weighted Recall: 0.763


## 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/nicoco404/autotrain-aita-post-classifier-3535895495
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nicoco404/autotrain-aita-post-classifier-3535895495", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("nicoco404/autotrain-aita-post-classifier-3535895495", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["nicoco404/autotrain-data-aita-post-classifier"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 13.203921634602377}}
transformers pytorch deberta text-classification autotrain en dataset:nicoco404/autotrain-data-aita-post-classifier co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-nicoco404/autotrain-data-aita-post-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3535895495
- CO2 Emissions (in grams): 13.2039

## Validation Metrics

- Loss: 0.761
- Accuracy: 0.763
- Macro F1: 0.124
- Micro F1: 0.763
- Weighted F1: 0.661
- Macro Precision: 0.109
- Micro Precision: 0.763
- Weighted Precision: 0.583
- Macro Recall: 0.143
- Micro Recall: 0.763
- Weighted Recall: 0.763


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3535895495
- CO2 Emissions (in grams): 13.2039 ## Validation Metrics

- Loss: 0.761
- Accuracy: 0.763
- Macro F1: 0.124
- Micro F1: 0.763
- Weighted F1: 0.661
- Macro Precision: 0.109
- Micro Precision: 0.763
- Weighted Precision: 0.583
- Macro Recall: 0.143
- Micro Recall: 0.763
- Weighted Recall: 0.763 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-nicoco404/autotrain-data-aita-post-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3535895495
- CO2 Emissions (in grams): 13.2039 ## Validation Metrics

- Loss: 0.761
- Accuracy: 0.763
- Macro F1: 0.124
- Micro F1: 0.763
- Weighted F1: 0.661
- Macro Precision: 0.109
- Micro Precision: 0.763
- Weighted Precision: 0.583
- Macro Recall: 0.143
- Micro Recall: 0.763
- Weighted Recall: 0.763 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta #text-classification #autotrain #en #dataset-nicoco404/autotrain-data-aita-post-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3535895495
- CO2 Emissions (in grams): 13.2039## Validation Metrics

- Loss: 0.761
- Accuracy: 0.763
- Macro F1: 0.124
- Micro F1: 0.763
- Weighted F1: 0.661
- Macro Precision: 0.109
- Micro Precision: 0.763
- Weighted Precision: 0.583
- Macro Recall: 0.143
- Micro Recall: 0.763
- Weighted Recall: 0.763## Usage

You can use cURL to access this model:



Or Python API: