
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596

## Validation Metrics

- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929

## 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/Fauzan/autonlp-judulberita-32517788
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["Fauzan/autonlp-data-judulberita"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.9413042739759596}
transformers pytorch bert text-classification autonlp unk dataset:Fauzan/autonlp-data-judulberita co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-Fauzan/autonlp-data-judulberita #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596

## Validation Metrics

- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596 ## Validation Metrics

- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596 ## Validation Metrics

- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596## Validation Metrics

- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929## Usage

You can use cURL to access this model:



Or Python API: