
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555

## Validation Metrics

- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.9520917678812415

## 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/doctorlan/autonlp-JD-bert-653619233
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["doctorlan/autonlp-data-JD-bert"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.919372931976555}
transformers pytorch bert text-classification autonlp unk dataset:doctorlan/autonlp-data-JD-bert co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-doctorlan/autonlp-data-JD-bert #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555

## Validation Metrics

- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.9520917678812415

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555 ## Validation Metrics

- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.9520917678812415 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555 ## Validation Metrics

- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.9520917678812415 ## Usage

You can use cURL to access this model:



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

- Problem type: Binary Classification
- Model ID: 653619233
- CO2 Emissions (in grams): 5.919372931976555## Validation Metrics

- Loss: 0.15083155035972595
- Accuracy: 0.952650883627876
- Precision: 0.9631399317406143
- Recall: 0.9412941961307538
- AUC: 0.9828776962419389
- F1: 0.9520917678812415## Usage

You can use cURL to access this model:



Or Python API: