
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081

## Validation Metrics

- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299


## 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/Yarn/autotrain-Traimn-853827191
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Yarn/autotrain-Traimn-853827191", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Yarn/autotrain-Traimn-853827191", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "unk", "tags": "autotrain", "datasets": ["Yarn/autotrain-data-Traimn"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.712176860015081}
transformers pytorch bert text-classification autotrain unk dataset:Yarn/autotrain-data-Traimn co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #unk #dataset-Yarn/autotrain-data-Traimn #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081

## Validation Metrics

- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081 ## Validation Metrics

- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081 ## Validation Metrics

- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 853827191
- CO2 Emissions (in grams): 1.712176860015081## Validation Metrics

- Loss: 0.10257730633020401
- Accuracy: 0.973421926910299
- Macro F1: 0.9735224586288418
- Micro F1: 0.973421926910299
- Weighted F1: 0.9735187934099364
- Macro Precision: 0.9738505933839127
- Micro Precision: 0.973421926910299
- Weighted Precision: 0.9738995774527256
- Macro Recall: 0.9734994306470444
- Micro Recall: 0.973421926910299
- Weighted Recall: 0.973421926910299## Usage

You can use cURL to access this model:



Or Python API: