
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143

## Validation Metrics

- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/emekaboris/autonlp-new_tx-607517182
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-new_tx-607517182", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-new_tx-607517182", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-new_tx"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.842950628218143}
transformers pytorch roberta text-classification autonlp unk dataset:emekaboris/autonlp-data-new_tx co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-emekaboris/autonlp-data-new_tx #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143

## Validation Metrics

- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143 ## Validation Metrics

- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-emekaboris/autonlp-data-new_tx #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143 ## Validation Metrics

- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-emekaboris/autonlp-data-new_tx #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143## Validation Metrics

- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992## Usage

You can use cURL to access this model:



Or Python API: