
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177

## Validation Metrics

- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086

## 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 AutoTrain"}' https://api-inference.huggingface.co/models/cjbarrie/autotrain-traintest-sentiment-split-1024534825
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("cjbarrie/autotrain-traintest-sentiment-split-1024534825", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("cjbarrie/autotrain-traintest-sentiment-split-1024534825", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["cjbarrie/autotrain-data-traintest-sentiment-split"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 3.1566482249518177}
transformers pytorch roberta text-classification autotrain en dataset:cjbarrie/autotrain-data-traintest-sentiment-split co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177

## Validation Metrics

- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177 ## Validation Metrics

- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177 ## Validation Metrics

- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-cjbarrie/autotrain-data-traintest-sentiment-split #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177## Validation Metrics

- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086## Usage

You can use cURL to access this model:



Or Python API: