
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182

## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214

## 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/lewtun/autotrain-my-eval-project-615-5694363
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["lewtun/autotrain-data-my-eval-project-615"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 172.04481351504182, "model-index": [{"name": "bhadresh-savani/distilbert-base-uncased-emotion", "results": [{"task": {"type": "text-classification", "name": "Multi-class Classification"}, "dataset": {"name": "Emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "loss", "value": 0.17404702305793762, "name": "Loss"}, {"type": "accuracy", "value": 0.927, "name": "Accuracy"}, {"type": "macro_f1", "value": 0.8825061528287809, "name": "Macro F1"}, {"type": "micro_f1", "value": 0.927, "name": "Recall"}, {"type": "weighted_f1", "value": 0.926876082854655, "name": "Weighted F1"}, {"type": "macro_precision", "value": 0.8880230732280744, "name": "Macro Precision"}, {"type": "micro_precision", "value": 0.927, "name": "Micro Precision"}, {"type": "weighted_precision", "value": 0.9272902840835793, "name": "Weighted Precision"}, {"type": "macro_recall", "value": 0.8790126653780703, "name": "Macro Recall"}, {"type": "micro_recall", "value": 0.927, "name": "Micro Recall"}, {"type": "weighted_recall", "value": 0.927, "name": "Weighted Recall"}]}]}]}
transformers pytorch bert text-classification autotrain en dataset:lewtun/autotrain-data-my-eval-project-615 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-lewtun/autotrain-data-my-eval-project-615 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182

## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182 ## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-lewtun/autotrain-data-my-eval-project-615 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182 ## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-lewtun/autotrain-data-my-eval-project-615 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182## Validation Metrics

- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214## Usage

You can use cURL to access this model:



Or Python API: