
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583

## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087


## 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/ramnika003/autotrain-sentiment_analysis_project-705021428
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("ramnika003/autotrain-sentiment_analysis_project-705021428", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["ramnika003/autotrain-data-sentiment_analysis_project"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 10.03748863138583}
transformers pytorch xlm-roberta text-classification autotrain unk dataset:ramnika003/autotrain-data-sentiment_analysis_project co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-ramnika003/autotrain-data-sentiment_analysis_project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583

## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583 ## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-ramnika003/autotrain-data-sentiment_analysis_project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583 ## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-ramnika003/autotrain-data-sentiment_analysis_project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583## Validation Metrics

- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148
- Macro Precision: 0.7658234531650739
- Micro Precision: 0.768964665184087
- Weighted Precision: 0.7684017544026074
- Macro Recall: 0.7603505092881394
- Micro Recall: 0.768964665184087
- Weighted Recall: 0.768964665184087## Usage

You can use cURL to access this model:



Or Python API: