
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897

## Validation Metrics

- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.9226646248085759

## 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/nurkayevaa/autonlp-bert-covid-407910458
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nurkayevaa/autonlp-bert-covid-407910458", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("nurkayevaa/autonlp-bert-covid-407910458", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autonlp", "datasets": ["nurkayevaa/autonlp-data-bert-covid"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 9.72797586719897}
transformers pytorch distilbert text-classification autonlp en dataset:nurkayevaa/autonlp-data-bert-covid co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-nurkayevaa/autonlp-data-bert-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897

## Validation Metrics

- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.9226646248085759

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897 ## Validation Metrics

- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.9226646248085759 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-nurkayevaa/autonlp-data-bert-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897 ## Validation Metrics

- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.9226646248085759 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-nurkayevaa/autonlp-data-bert-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 407910458
- CO2 Emissions (in grams): 9.72797586719897## Validation Metrics

- Loss: 0.20907048881053925
- Accuracy: 0.9119825708061002
- Precision: 0.8912721893491125
- Recall: 0.9563492063492064
- AUC: 0.9698454873092555
- F1: 0.9226646248085759## Usage

You can use cURL to access this model:



Or Python API: