
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023

## Validation Metrics

- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.923920135717082

## 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-GlueModels-1010733562
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-GlueModels-1010733562", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-GlueModels-1010733562", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["deepesh0x/autotrain-data-GlueModels"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 60.24263131580023}
transformers pytorch bert text-classification autotrain en dataset:deepesh0x/autotrain-data-GlueModels co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-deepesh0x/autotrain-data-GlueModels #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023

## Validation Metrics

- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.923920135717082

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023 ## Validation Metrics

- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.923920135717082 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023 ## Validation Metrics

- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.923920135717082 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 1010733562
- CO2 Emissions (in grams): 60.24263131580023## Validation Metrics

- Loss: 0.1812974065542221
- Accuracy: 0.9252564102564103
- Precision: 0.9409888357256778
- Recall: 0.9074596257369905
- AUC: 0.9809618001947271
- F1: 0.923920135717082## Usage

You can use cURL to access this model:



Or Python API: