
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746

## Validation Metrics

- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555096418732782
- Micro Precision: 0.75
- Weighted Precision: 0.7555096418732782
- Macro Recall: 0.75
- Micro Recall: 0.75
- Weighted Recall: 0.75


## 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/js3078/autotrain-BerTweet-749522913
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("js3078/autotrain-BerTweet-749522913", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("js3078/autotrain-BerTweet-749522913", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["js3078/autotrain-data-BerTweet"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.093939667345746}
transformers pytorch roberta text-classification autotrain unk dataset:js3078/autotrain-data-BerTweet co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-js3078/autotrain-data-BerTweet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746

## Validation Metrics

- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555096418732782
- Micro Precision: 0.75
- Weighted Precision: 0.7555096418732782
- Macro Recall: 0.75
- Micro Recall: 0.75
- Weighted Recall: 0.75


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746 ## Validation Metrics

- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555096418732782
- Micro Precision: 0.75
- Weighted Precision: 0.7555096418732782
- Macro Recall: 0.75
- Micro Recall: 0.75
- Weighted Recall: 0.75 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-js3078/autotrain-data-BerTweet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746 ## Validation Metrics

- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555096418732782
- Micro Precision: 0.75
- Weighted Precision: 0.7555096418732782
- Macro Recall: 0.75
- Micro Recall: 0.75
- Weighted Recall: 0.75 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-js3078/autotrain-data-BerTweet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746## Validation Metrics

- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555096418732782
- Micro Precision: 0.75
- Weighted Precision: 0.7555096418732782
- Macro Recall: 0.75
- Micro Recall: 0.75
- Weighted Recall: 0.75## Usage

You can use cURL to access this model:



Or Python API: