
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274

## Validation Metrics

- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999


## 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/Garfieldgx/autotrain-severe-js100-sentiment-45485113858
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Garfieldgx/autotrain-severe-js100-sentiment-45485113858", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Garfieldgx/autotrain-severe-js100-sentiment-45485113858", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["Garfieldgx/autotrain-data-severe-js100-sentiment"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.9273951637568196}}
transformers pytorch camembert text-classification autotrain unk dataset:Garfieldgx/autotrain-data-severe-js100-sentiment co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Garfieldgx/autotrain-data-severe-js100-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274

## Validation Metrics

- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274 ## Validation Metrics

- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Garfieldgx/autotrain-data-severe-js100-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274 ## Validation Metrics

- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Garfieldgx/autotrain-data-severe-js100-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 45485113858
- CO2 Emissions (in grams): 0.9274## Validation Metrics

- Loss: 0.007
- Accuracy: 0.999
- Macro F1: 0.995
- Micro F1: 0.999
- Weighted F1: 0.999
- Macro Precision: 0.991
- Micro Precision: 0.999
- Weighted Precision: 0.999
- Macro Recall: 0.999
- Micro Recall: 0.999
- Weighted Recall: 0.999## Usage

You can use cURL to access this model:



Or Python API: