
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955

## Validation Metrics

- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535


## 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/aujer/autotrain-not_interested_2-1213045881
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("aujer/autotrain-not_interested_2-1213045881", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["aujer/autotrain-data-not_interested_2"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.695519133475222}}
transformers pytorch bert text-classification autotrain en dataset:aujer/autotrain-data-not_interested_2 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955

## Validation Metrics

- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955 ## Validation Metrics

- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955 ## Validation Metrics

- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-aujer/autotrain-data-not_interested_2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1213045881
- CO2 Emissions (in grams): 1.6955## Validation Metrics

- Loss: 1.607
- Accuracy: 0.535
- Macro F1: 0.306
- Micro F1: 0.535
- Weighted F1: 0.440
- Macro Precision: 0.346
- Micro Precision: 0.535
- Weighted Precision: 0.435
- Macro Recall: 0.345
- Micro Recall: 0.535
- Weighted Recall: 0.535## Usage

You can use cURL to access this model:



Or Python API: