
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3451994032
- CO2 Emissions (in grams): 8.7693

## Validation Metrics

- Loss: 0.128
- Accuracy: 0.978
- Macro F1: 0.953
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.957
- Micro Precision: 0.978
- Weighted Precision: 0.979
- Macro Recall: 0.954
- Micro Recall: 0.978
- Weighted Recall: 0.978


## 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/acrowth/autotrain-preesmefirstpageclassificationnew-3451994032
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("acrowth/autotrain-preesmefirstpageclassificationnew-3451994032", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("acrowth/autotrain-preesmefirstpageclassificationnew-3451994032", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["fr"], "tags": ["autotrain", "text-classification"], "datasets": ["acrowth/autotrain-data-preesmefirstpageclassificationnew"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 8.769306773648797}}
transformers pytorch camembert text-classification autotrain fr dataset:acrowth/autotrain-data-preesmefirstpageclassificationnew co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #fr #dataset-acrowth/autotrain-data-preesmefirstpageclassificationnew #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3451994032
- CO2 Emissions (in grams): 8.7693

## Validation Metrics

- Loss: 0.128
- Accuracy: 0.978
- Macro F1: 0.953
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.957
- Micro Precision: 0.978
- Weighted Precision: 0.979
- Macro Recall: 0.954
- Micro Recall: 0.978
- Weighted Recall: 0.978


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3451994032
- CO2 Emissions (in grams): 8.7693 ## Validation Metrics

- Loss: 0.128
- Accuracy: 0.978
- Macro F1: 0.953
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.957
- Micro Precision: 0.978
- Weighted Precision: 0.979
- Macro Recall: 0.954
- Micro Recall: 0.978
- Weighted Recall: 0.978 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #fr #dataset-acrowth/autotrain-data-preesmefirstpageclassificationnew #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3451994032
- CO2 Emissions (in grams): 8.7693 ## Validation Metrics

- Loss: 0.128
- Accuracy: 0.978
- Macro F1: 0.953
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.957
- Micro Precision: 0.978
- Weighted Precision: 0.979
- Macro Recall: 0.954
- Micro Recall: 0.978
- Weighted Recall: 0.978 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #fr #dataset-acrowth/autotrain-data-preesmefirstpageclassificationnew #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3451994032
- CO2 Emissions (in grams): 8.7693## Validation Metrics

- Loss: 0.128
- Accuracy: 0.978
- Macro F1: 0.953
- Micro F1: 0.978
- Weighted F1: 0.978
- Macro Precision: 0.957
- Micro Precision: 0.978
- Weighted Precision: 0.979
- Macro Recall: 0.954
- Micro Recall: 0.978
- Weighted Recall: 0.978## Usage

You can use cURL to access this model:



Or Python API: