
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2799882586
- CO2 Emissions (in grams): 1.5316

## Validation Metrics

- Loss: 0.251
- Accuracy: 0.950
- Macro F1: 0.949
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.962
- Micro Precision: 0.950
- Weighted Precision: 0.953
- Macro Recall: 0.938
- Micro Recall: 0.950
- Weighted Recall: 0.950


## 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/lucieackley/autotrain-multi_msg_4-2799882586
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lucieackley/autotrain-multi_msg_4-2799882586", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lucieackley/autotrain-multi_msg_4-2799882586", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["lucieackley/autotrain-data-multi_msg_4"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.531642698505257}}
transformers pytorch deberta-v2 text-classification autotrain en dataset:lucieackley/autotrain-data-multi_msg_4 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucieackley/autotrain-data-multi_msg_4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2799882586
- CO2 Emissions (in grams): 1.5316

## Validation Metrics

- Loss: 0.251
- Accuracy: 0.950
- Macro F1: 0.949
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.962
- Micro Precision: 0.950
- Weighted Precision: 0.953
- Macro Recall: 0.938
- Micro Recall: 0.950
- Weighted Recall: 0.950


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2799882586
- CO2 Emissions (in grams): 1.5316 ## Validation Metrics

- Loss: 0.251
- Accuracy: 0.950
- Macro F1: 0.949
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.962
- Micro Precision: 0.950
- Weighted Precision: 0.953
- Macro Recall: 0.938
- Micro Recall: 0.950
- Weighted Recall: 0.950 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucieackley/autotrain-data-multi_msg_4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2799882586
- CO2 Emissions (in grams): 1.5316 ## Validation Metrics

- Loss: 0.251
- Accuracy: 0.950
- Macro F1: 0.949
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.962
- Micro Precision: 0.950
- Weighted Precision: 0.953
- Macro Recall: 0.938
- Micro Recall: 0.950
- Weighted Recall: 0.950 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #deberta-v2 #text-classification #autotrain #en #dataset-lucieackley/autotrain-data-multi_msg_4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2799882586
- CO2 Emissions (in grams): 1.5316## Validation Metrics

- Loss: 0.251
- Accuracy: 0.950
- Macro F1: 0.949
- Micro F1: 0.950
- Weighted F1: 0.950
- Macro Precision: 0.962
- Micro Precision: 0.950
- Weighted Precision: 0.953
- Macro Recall: 0.938
- Micro Recall: 0.950
- Weighted Recall: 0.950## Usage

You can use cURL to access this model:



Or Python API: