
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2970786288
- CO2 Emissions (in grams): 43.9202

## Validation Metrics

- Loss: 3.426
- Accuracy: 0.207
- Macro F1: 0.197
- Micro F1: 0.207
- Weighted F1: 0.197
- Macro Precision: 0.219
- Micro Precision: 0.207
- Weighted Precision: 0.219
- Macro Recall: 0.207
- Micro Recall: 0.207
- Weighted Recall: 0.207


## 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/RowanTELSCorp/autotrain-artunit-50-500-2970786288
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("RowanTELSCorp/autotrain-artunit-50-500-2970786288", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("RowanTELSCorp/autotrain-artunit-50-500-2970786288", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["RowanTELSCorp/autotrain-data-artunit-50-500"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 43.92017027932009}}
transformers pytorch distilbert text-classification autotrain en dataset:RowanTELSCorp/autotrain-data-artunit-50-500 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-RowanTELSCorp/autotrain-data-artunit-50-500 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2970786288
- CO2 Emissions (in grams): 43.9202

## Validation Metrics

- Loss: 3.426
- Accuracy: 0.207
- Macro F1: 0.197
- Micro F1: 0.207
- Weighted F1: 0.197
- Macro Precision: 0.219
- Micro Precision: 0.207
- Weighted Precision: 0.219
- Macro Recall: 0.207
- Micro Recall: 0.207
- Weighted Recall: 0.207


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2970786288
- CO2 Emissions (in grams): 43.9202 ## Validation Metrics

- Loss: 3.426
- Accuracy: 0.207
- Macro F1: 0.197
- Micro F1: 0.207
- Weighted F1: 0.197
- Macro Precision: 0.219
- Micro Precision: 0.207
- Weighted Precision: 0.219
- Macro Recall: 0.207
- Micro Recall: 0.207
- Weighted Recall: 0.207 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-RowanTELSCorp/autotrain-data-artunit-50-500 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2970786288
- CO2 Emissions (in grams): 43.9202 ## Validation Metrics

- Loss: 3.426
- Accuracy: 0.207
- Macro F1: 0.197
- Micro F1: 0.207
- Weighted F1: 0.197
- Macro Precision: 0.219
- Micro Precision: 0.207
- Weighted Precision: 0.219
- Macro Recall: 0.207
- Micro Recall: 0.207
- Weighted Recall: 0.207 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-RowanTELSCorp/autotrain-data-artunit-50-500 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 2970786288
- CO2 Emissions (in grams): 43.9202## Validation Metrics

- Loss: 3.426
- Accuracy: 0.207
- Macro F1: 0.197
- Micro F1: 0.207
- Weighted F1: 0.197
- Macro Precision: 0.219
- Micro Precision: 0.207
- Weighted Precision: 0.219
- Macro Recall: 0.207
- Micro Recall: 0.207
- Weighted Recall: 0.207## Usage

You can use cURL to access this model:



Or Python API: