
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445

## Validation Metrics

- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325

## 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 AutoNLP"}' https://api-inference.huggingface.co/models/zwang199/autonlp-traffic_nlp_binary-537215209
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["zwang199/autonlp-data-traffic_nlp_binary"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.171798205242445}
transformers pytorch roberta text-classification autonlp en dataset:zwang199/autonlp-data-traffic_nlp_binary co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-zwang199/autonlp-data-traffic_nlp_binary #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445

## Validation Metrics

- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445 ## Validation Metrics

- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-zwang199/autonlp-data-traffic_nlp_binary #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445 ## Validation Metrics

- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-zwang199/autonlp-data-traffic_nlp_binary #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445## Validation Metrics

- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325## Usage

You can use cURL to access this model:



Or Python API: