
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306

## Validation Metrics

- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569

## 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/deepesh0x/autotrain-finetunedmodel1-1034535555
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-finetunedmodel1-1034535555", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-finetunedmodel1-1034535555", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-finetunedmodel1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 29.194903746653306}
transformers pytorch distilbert text-classification autotrain unk dataset:deepesh0x/autotrain-data-finetunedmodel1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-finetunedmodel1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306

## Validation Metrics

- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306 ## Validation Metrics

- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-finetunedmodel1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306 ## Validation Metrics

- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-finetunedmodel1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1034535555
- CO2 Emissions (in grams): 29.194903746653306## Validation Metrics

- Loss: 0.16423887014389038
- Accuracy: 0.9402375649591685
- Precision: 0.94876254180602
- Recall: 0.9438381687516636
- AUC: 0.9843968335444757
- F1: 0.9462939488958569## Usage

You can use cURL to access this model:



Or Python API: