
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.2497206864306065

## Validation Metrics

- Loss: 0.744236171245575
- Accuracy: 0.6719238613188308
- Macro F1: 0.5450301061253738
- Micro F1: 0.6719238613188308
- Weighted F1: 0.6349879540623229
- Macro Precision: 0.6691326843926052
- Micro Precision: 0.6719238613188308
- Weighted Precision: 0.6706209016443158
- Macro Recall: 0.5426627824078865
- Micro Recall: 0.6719238613188308
- Weighted Recall: 0.6719238613188308


## 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/Danitg95/autotrain-kaggle-effective-arguments-1086739296
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Danitg95/autotrain-kaggle-effective-arguments-1086739296", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["Danitg95/autotrain-data-kaggle-effective-arguments"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.2497206864306065}
transformers pytorch distilbert text-classification autotrain en dataset:Danitg95/autotrain-data-kaggle-effective-arguments co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Danitg95/autotrain-data-kaggle-effective-arguments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.2497206864306065

## Validation Metrics

- Loss: 0.744236171245575
- Accuracy: 0.6719238613188308
- Macro F1: 0.5450301061253738
- Micro F1: 0.6719238613188308
- Weighted F1: 0.6349879540623229
- Macro Precision: 0.6691326843926052
- Micro Precision: 0.6719238613188308
- Weighted Precision: 0.6706209016443158
- Macro Recall: 0.5426627824078865
- Micro Recall: 0.6719238613188308
- Weighted Recall: 0.6719238613188308


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.2497206864306065 ## Validation Metrics

- Loss: 0.744236171245575
- Accuracy: 0.6719238613188308
- Macro F1: 0.5450301061253738
- Micro F1: 0.6719238613188308
- Weighted F1: 0.6349879540623229
- Macro Precision: 0.6691326843926052
- Micro Precision: 0.6719238613188308
- Weighted Precision: 0.6706209016443158
- Macro Recall: 0.5426627824078865
- Micro Recall: 0.6719238613188308
- Weighted Recall: 0.6719238613188308 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Danitg95/autotrain-data-kaggle-effective-arguments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.2497206864306065 ## Validation Metrics

- Loss: 0.744236171245575
- Accuracy: 0.6719238613188308
- Macro F1: 0.5450301061253738
- Micro F1: 0.6719238613188308
- Weighted F1: 0.6349879540623229
- Macro Precision: 0.6691326843926052
- Micro Precision: 0.6719238613188308
- Weighted Precision: 0.6706209016443158
- Macro Recall: 0.5426627824078865
- Micro Recall: 0.6719238613188308
- Weighted Recall: 0.6719238613188308 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Danitg95/autotrain-data-kaggle-effective-arguments #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1086739296
- CO2 Emissions (in grams): 5.2497206864306065## Validation Metrics

- Loss: 0.744236171245575
- Accuracy: 0.6719238613188308
- Macro F1: 0.5450301061253738
- Micro F1: 0.6719238613188308
- Weighted F1: 0.6349879540623229
- Macro Precision: 0.6691326843926052
- Micro Precision: 0.6719238613188308
- Weighted Precision: 0.6706209016443158
- Macro Recall: 0.5426627824078865
- Micro Recall: 0.6719238613188308
- Weighted Recall: 0.6719238613188308## Usage

You can use cURL to access this model:



Or Python API: