
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844

## Validation Metrics

- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194

## 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/masifayub/autotrain-PAN-976832386
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("masifayub/autotrain-PAN-976832386", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("masifayub/autotrain-PAN-976832386", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["masifayub/autotrain-data-PAN"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 7.17945527948844}
transformers pytorch roberta text-classification autotrain en dataset:masifayub/autotrain-data-PAN co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-masifayub/autotrain-data-PAN #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844

## Validation Metrics

- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844 ## Validation Metrics

- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844 ## Validation Metrics

- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 976832386
- CO2 Emissions (in grams): 7.17945527948844## Validation Metrics

- Loss: 0.4892439842224121
- Accuracy: 0.7591666666666667
- Precision: 0.8088978766430738
- Recall: 0.8888888888888888
- AUC: 0.7550212962962962
- F1: 0.8470089994706194## Usage

You can use cURL to access this model:



Or Python API: