
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 53197125359
- CO2 Emissions (in grams): 17.9303

## Validation Metrics

- Loss: 0.380
- Accuracy: 0.939
- Macro F1: 0.841
- Micro F1: 0.939
- Weighted F1: 0.933
- Macro Precision: 0.843
- Micro Precision: 0.939
- Weighted Precision: 0.934
- Macro Recall: 0.857
- Micro Recall: 0.939
- Weighted Recall: 0.939


## 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/duhmiko/autotrain-product_name-53197125359
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("duhmiko/autotrain-product_name-53197125359", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("duhmiko/autotrain-product_name-53197125359", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["duhmiko/autotrain-data-product_name"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 17.930285399787586}}
transformers pytorch bert text-classification autotrain en dataset:duhmiko/autotrain-data-product_name co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-duhmiko/autotrain-data-product_name #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 53197125359
- CO2 Emissions (in grams): 17.9303

## Validation Metrics

- Loss: 0.380
- Accuracy: 0.939
- Macro F1: 0.841
- Micro F1: 0.939
- Weighted F1: 0.933
- Macro Precision: 0.843
- Micro Precision: 0.939
- Weighted Precision: 0.934
- Macro Recall: 0.857
- Micro Recall: 0.939
- Weighted Recall: 0.939


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 53197125359
- CO2 Emissions (in grams): 17.9303 ## Validation Metrics

- Loss: 0.380
- Accuracy: 0.939
- Macro F1: 0.841
- Micro F1: 0.939
- Weighted F1: 0.933
- Macro Precision: 0.843
- Micro Precision: 0.939
- Weighted Precision: 0.934
- Macro Recall: 0.857
- Micro Recall: 0.939
- Weighted Recall: 0.939 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-duhmiko/autotrain-data-product_name #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 53197125359
- CO2 Emissions (in grams): 17.9303 ## Validation Metrics

- Loss: 0.380
- Accuracy: 0.939
- Macro F1: 0.841
- Micro F1: 0.939
- Weighted F1: 0.933
- Macro Precision: 0.843
- Micro Precision: 0.939
- Weighted Precision: 0.934
- Macro Recall: 0.857
- Micro Recall: 0.939
- Weighted Recall: 0.939 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-duhmiko/autotrain-data-product_name #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 53197125359
- CO2 Emissions (in grams): 17.9303## Validation Metrics

- Loss: 0.380
- Accuracy: 0.939
- Macro F1: 0.841
- Micro F1: 0.939
- Weighted F1: 0.933
- Macro Precision: 0.843
- Micro Precision: 0.939
- Weighted Precision: 0.934
- Macro Recall: 0.857
- Micro Recall: 0.939
- Weighted Recall: 0.939## Usage

You can use cURL to access this model:



Or Python API: