
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951

## Validation Metrics

- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.8177339901477833

## 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/lidiia/autonlp-trans_class_arg-32957902
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lidiia/autonlp-trans_class_arg-32957902", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lidiia/autonlp-trans_class_arg-32957902", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["lidiia/autonlp-data-trans_class_arg"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.9756221672668951}
transformers pytorch bert text-classification autonlp unk dataset:lidiia/autonlp-data-trans_class_arg co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-lidiia/autonlp-data-trans_class_arg #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951

## Validation Metrics

- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.8177339901477833

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951 ## Validation Metrics

- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.8177339901477833 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951 ## Validation Metrics

- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.8177339901477833 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 32957902
- CO2 Emissions (in grams): 0.9756221672668951## Validation Metrics

- Loss: 0.2765039801597595
- Accuracy: 0.8939828080229226
- Precision: 0.7757009345794392
- Recall: 0.8645833333333334
- AUC: 0.9552659749670619
- F1: 0.8177339901477833## Usage

You can use cURL to access this model:



Or Python API: