
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3525195293
- CO2 Emissions (in grams): 1.1728

## Validation Metrics

- Loss: 1.116
- Accuracy: 0.542
- Macro F1: 0.512
- Micro F1: 0.542
- Weighted F1: 0.540
- Macro Precision: 0.512
- Micro Precision: 0.542
- Weighted Precision: 0.541
- Macro Recall: 0.514
- Micro Recall: 0.542
- Weighted Recall: 0.542


## 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/DioLiu/autotrain-koles_score2-3525195293
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("DioLiu/autotrain-koles_score2-3525195293", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("DioLiu/autotrain-koles_score2-3525195293", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["DioLiu/autotrain-data-koles_score2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.1728247359077393}}
transformers pytorch bert text-classification autotrain en dataset:DioLiu/autotrain-data-koles_score2 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-DioLiu/autotrain-data-koles_score2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3525195293
- CO2 Emissions (in grams): 1.1728

## Validation Metrics

- Loss: 1.116
- Accuracy: 0.542
- Macro F1: 0.512
- Micro F1: 0.542
- Weighted F1: 0.540
- Macro Precision: 0.512
- Micro Precision: 0.542
- Weighted Precision: 0.541
- Macro Recall: 0.514
- Micro Recall: 0.542
- Weighted Recall: 0.542


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3525195293
- CO2 Emissions (in grams): 1.1728 ## Validation Metrics

- Loss: 1.116
- Accuracy: 0.542
- Macro F1: 0.512
- Micro F1: 0.542
- Weighted F1: 0.540
- Macro Precision: 0.512
- Micro Precision: 0.542
- Weighted Precision: 0.541
- Macro Recall: 0.514
- Micro Recall: 0.542
- Weighted Recall: 0.542 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-DioLiu/autotrain-data-koles_score2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3525195293
- CO2 Emissions (in grams): 1.1728 ## Validation Metrics

- Loss: 1.116
- Accuracy: 0.542
- Macro F1: 0.512
- Micro F1: 0.542
- Weighted F1: 0.540
- Macro Precision: 0.512
- Micro Precision: 0.542
- Weighted Precision: 0.541
- Macro Recall: 0.514
- Micro Recall: 0.542
- Weighted Recall: 0.542 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-DioLiu/autotrain-data-koles_score2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3525195293
- CO2 Emissions (in grams): 1.1728## Validation Metrics

- Loss: 1.116
- Accuracy: 0.542
- Macro F1: 0.512
- Micro F1: 0.542
- Weighted F1: 0.540
- Macro Precision: 0.512
- Micro Precision: 0.542
- Weighted Precision: 0.541
- Macro Recall: 0.514
- Micro Recall: 0.542
- Weighted Recall: 0.542## Usage

You can use cURL to access this model:



Or Python API: