
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188

## Validation Metrics

- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.5640509725016768
- Macro Precision: 0.23015873015873015
- Micro Precision: 0.6904761904761905
- Weighted Precision: 0.4767573696145125
- Macro Recall: 0.3333333333333333
- Micro Recall: 0.6904761904761905
- Weighted Recall: 0.6904761904761905


## 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/Rem59/autotrain-Test_2-789524315
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Rem59/autotrain-Test_2-789524315", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Rem59/autotrain-Test_2-789524315", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["Rem59/autotrain-data-Test_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.0134443204822188}
transformers pytorch camembert text-classification autotrain unk dataset:Rem59/autotrain-data-Test_2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Rem59/autotrain-data-Test_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188

## Validation Metrics

- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.5640509725016768
- Macro Precision: 0.23015873015873015
- Micro Precision: 0.6904761904761905
- Weighted Precision: 0.4767573696145125
- Macro Recall: 0.3333333333333333
- Micro Recall: 0.6904761904761905
- Weighted Recall: 0.6904761904761905


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188 ## Validation Metrics

- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.5640509725016768
- Macro Precision: 0.23015873015873015
- Micro Precision: 0.6904761904761905
- Weighted Precision: 0.4767573696145125
- Macro Recall: 0.3333333333333333
- Micro Recall: 0.6904761904761905
- Weighted Recall: 0.6904761904761905 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Rem59/autotrain-data-Test_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188 ## Validation Metrics

- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.5640509725016768
- Macro Precision: 0.23015873015873015
- Micro Precision: 0.6904761904761905
- Weighted Precision: 0.4767573696145125
- Macro Recall: 0.3333333333333333
- Micro Recall: 0.6904761904761905
- Weighted Recall: 0.6904761904761905 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #camembert #text-classification #autotrain #unk #dataset-Rem59/autotrain-data-Test_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 789524315
- CO2 Emissions (in grams): 2.0134443204822188## Validation Metrics

- Loss: 0.8042349815368652
- Accuracy: 0.6904761904761905
- Macro F1: 0.27230046948356806
- Micro F1: 0.6904761904761905
- Weighted F1: 0.5640509725016768
- Macro Precision: 0.23015873015873015
- Micro Precision: 0.6904761904761905
- Weighted Precision: 0.4767573696145125
- Macro Recall: 0.3333333333333333
- Micro Recall: 0.6904761904761905
- Weighted Recall: 0.6904761904761905## Usage

You can use cURL to access this model:



Or Python API: