
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 54660127837
- CO2 Emissions (in grams): 11.6362

## Validation Metrics

- Loss: 2.066
- Accuracy: 0.349
- Macro F1: 0.180
- Micro F1: 0.349
- Weighted F1: 0.294
- Macro Precision: 0.158
- Micro Precision: 0.349
- Weighted Precision: 0.255
- Macro Recall: 0.213
- Micro Recall: 0.349
- Weighted Recall: 0.349


## 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/amogh23/autotrain-sentiment-54660127837
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("amogh23/autotrain-sentiment-54660127837", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("amogh23/autotrain-sentiment-54660127837", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["hi"], "tags": ["autotrain", "text-classification"], "datasets": ["amogh23/autotrain-data-sentiment"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 11.636164991360136}}
transformers pytorch xlm-roberta text-classification autotrain hi dataset:amogh23/autotrain-data-sentiment co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #hi #dataset-amogh23/autotrain-data-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 54660127837
- CO2 Emissions (in grams): 11.6362

## Validation Metrics

- Loss: 2.066
- Accuracy: 0.349
- Macro F1: 0.180
- Micro F1: 0.349
- Weighted F1: 0.294
- Macro Precision: 0.158
- Micro Precision: 0.349
- Weighted Precision: 0.255
- Macro Recall: 0.213
- Micro Recall: 0.349
- Weighted Recall: 0.349


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 54660127837
- CO2 Emissions (in grams): 11.6362 ## Validation Metrics

- Loss: 2.066
- Accuracy: 0.349
- Macro F1: 0.180
- Micro F1: 0.349
- Weighted F1: 0.294
- Macro Precision: 0.158
- Micro Precision: 0.349
- Weighted Precision: 0.255
- Macro Recall: 0.213
- Micro Recall: 0.349
- Weighted Recall: 0.349 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #hi #dataset-amogh23/autotrain-data-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 54660127837
- CO2 Emissions (in grams): 11.6362 ## Validation Metrics

- Loss: 2.066
- Accuracy: 0.349
- Macro F1: 0.180
- Micro F1: 0.349
- Weighted F1: 0.294
- Macro Precision: 0.158
- Micro Precision: 0.349
- Weighted Precision: 0.255
- Macro Recall: 0.213
- Micro Recall: 0.349
- Weighted Recall: 0.349 ## Usage

You can use cURL to access this model:



Or Python API: