
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229

## Validation Metrics

- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.750390015600624

## 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-glue1-1046836019
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-glue1-1046836019", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-glue1-1046836019", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-glue1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.869994913020229}
transformers pytorch bert text-classification autotrain unk dataset:deepesh0x/autotrain-data-glue1 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-glue1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229

## Validation Metrics

- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.750390015600624

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229 ## Validation Metrics

- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.750390015600624 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-glue1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229 ## Validation Metrics

- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.750390015600624 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-deepesh0x/autotrain-data-glue1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1046836019
- CO2 Emissions (in grams): 3.869994913020229## Validation Metrics

- Loss: 0.626447856426239
- Accuracy: 0.6606574761399788
- Precision: 0.6925845932325414
- Recall: 0.8187234042553192
- AUC: 0.656404823892031
- F1: 0.750390015600624## Usage

You can use cURL to access this model:



Or Python API: