
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377

## Validation Metrics

- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580173
- Macro Precision: 0.9142880779580832
- Micro Precision: 0.9149108589951378
- Weighted Precision: 0.9159535860210665
- Macro Recall: 0.910063875934768
- Micro Recall: 0.9149108589951378
- Weighted Recall: 0.9149108589951378


## 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/justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634731
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634731", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("justpyschitry/autotrain-Wikipeida_Article_Classifier_by_Chap-1022634731", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "en", "tags": "autotrain", "datasets": ["justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 19.2150872382377}
transformers pytorch roberta text-classification autotrain en dataset:justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-justpyschitry/autotrain-data-Wikipeida_Article_Classifier_by_Chap #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377

## Validation Metrics

- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580173
- Macro Precision: 0.9142880779580832
- Micro Precision: 0.9149108589951378
- Weighted Precision: 0.9159535860210665
- Macro Recall: 0.910063875934768
- Micro Recall: 0.9149108589951378
- Weighted Recall: 0.9149108589951378


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377 ## Validation Metrics

- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580173
- Macro Precision: 0.9142880779580832
- Micro Precision: 0.9149108589951378
- Weighted Precision: 0.9159535860210665
- Macro Recall: 0.910063875934768
- Micro Recall: 0.9149108589951378
- Weighted Recall: 0.9149108589951378 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377 ## Validation Metrics

- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580173
- Macro Precision: 0.9142880779580832
- Micro Precision: 0.9149108589951378
- Weighted Precision: 0.9159535860210665
- Macro Recall: 0.910063875934768
- Micro Recall: 0.9149108589951378
- Weighted Recall: 0.9149108589951378 ## Usage

You can use cURL to access this model:



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

- Problem type: Multi-class Classification
- Model ID: 1022634731
- CO2 Emissions (in grams): 19.2150872382377## Validation Metrics

- Loss: 0.44044896960258484
- Accuracy: 0.9149108589951378
- Macro F1: 0.9112823337353622
- Micro F1: 0.9149108589951378
- Weighted F1: 0.9148129605580173
- Macro Precision: 0.9142880779580832
- Micro Precision: 0.9149108589951378
- Weighted Precision: 0.9159535860210665
- Macro Recall: 0.910063875934768
- Micro Recall: 0.9149108589951378
- Weighted Recall: 0.9149108589951378## Usage

You can use cURL to access this model:



Or Python API: