
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265

## Validation Metrics

- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119

## Usage
This model finds self-reported stress from txt.


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/hsaglamlar/autotrain-stress_v2-1178743973
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("hsaglamlar/autotrain-stress_v2-1178743973", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "datasets": ["hsaglamlar/autotrain-data-stress_v2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.7282806494855265}
transformers pytorch roberta text-classification en dataset:hsaglamlar/autotrain-data-stress_v2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-hsaglamlar/autotrain-data-stress_v2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265

## Validation Metrics

- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119

## Usage
This model finds self-reported stress from txt.


You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265 ## Validation Metrics

- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119 ## Usage
This model finds self-reported stress from txt.


You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-hsaglamlar/autotrain-data-stress_v2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265 ## Validation Metrics

- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119 ## Usage
This model finds self-reported stress from txt.


You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-hsaglamlar/autotrain-data-stress_v2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1178743973
- CO2 Emissions (in grams): 2.7282806494855265## Validation Metrics

- Loss: 0.431733638048172
- Accuracy: 0.7976190476190477
- Precision: 0.6918918918918919
- Recall: 0.8205128205128205
- AUC: 0.8952141608391608
- F1: 0.7507331378299119## Usage
This model finds self-reported stress from txt.


You can use cURL to access this model:



Or Python API: