
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604

## Validation Metrics

- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388879325185058

## 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/sam890914/autonlp-roberta-large2-479012819
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("sam890914/autonlp-roberta-large2-479012819", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("sam890914/autonlp-roberta-large2-479012819", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["sam890914/autonlp-data-roberta-large2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 71.60954851696604}
transformers pytorch roberta text-classification autonlp unk dataset:sam890914/autonlp-data-roberta-large2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-sam890914/autonlp-data-roberta-large2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604

## Validation Metrics

- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388879325185058

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604 ## Validation Metrics

- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388879325185058 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-sam890914/autonlp-data-roberta-large2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604 ## Validation Metrics

- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388879325185058 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-sam890914/autonlp-data-roberta-large2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604## Validation Metrics

- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388879325185058## Usage

You can use cURL to access this model:



Or Python API: