
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389

## Validation Metrics

- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946

## 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/rexxar96/autonlp-sentiment-analysis-456211724
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rexxar96/autonlp-sentiment-analysis-456211724", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("rexxar96/autonlp-sentiment-analysis-456211724", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["rexxar96/autonlp-data-sentiment-analysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 22.28263989637389}
transformers pytorch distilbert text-classification autonlp unk dataset:rexxar96/autonlp-data-sentiment-analysis co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389

## Validation Metrics

- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389 ## Validation Metrics

- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389 ## Validation Metrics

- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #unk #dataset-rexxar96/autonlp-data-sentiment-analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389## Validation Metrics

- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946## Usage

You can use cURL to access this model:



Or Python API: