
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48943118458
- CO2 Emissions (in grams): 1.9844

## Validation Metrics

- Loss: 0.062
- Accuracy: 0.991
- Macro F1: 0.991
- Micro F1: 0.991
- Weighted F1: 0.991
- Macro Precision: 0.991
- Micro Precision: 0.991
- Weighted Precision: 0.991
- Macro Recall: 0.992
- Micro Recall: 0.991
- Weighted Recall: 0.991


## 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/AyoubChLin/autotrain-roberta-large-bbc_news-48943118458
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/autotrain-roberta-large-bbc_news-48943118458", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/autotrain-roberta-large-bbc_news-48943118458", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["unk"], "tags": ["autotrain", "text-classification"], "datasets": ["AyoubChLin/autotrain-data-roberta-large-bbc_news"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.9843929651071104}}
transformers pytorch safetensors roberta text-classification autotrain unk dataset:AyoubChLin/autotrain-data-roberta-large-bbc_news co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #unk #dataset-AyoubChLin/autotrain-data-roberta-large-bbc_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48943118458
- CO2 Emissions (in grams): 1.9844

## Validation Metrics

- Loss: 0.062
- Accuracy: 0.991
- Macro F1: 0.991
- Micro F1: 0.991
- Weighted F1: 0.991
- Macro Precision: 0.991
- Micro Precision: 0.991
- Weighted Precision: 0.991
- Macro Recall: 0.992
- Micro Recall: 0.991
- Weighted Recall: 0.991


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48943118458
- CO2 Emissions (in grams): 1.9844 ## Validation Metrics

- Loss: 0.062
- Accuracy: 0.991
- Macro F1: 0.991
- Micro F1: 0.991
- Weighted F1: 0.991
- Macro Precision: 0.991
- Micro Precision: 0.991
- Weighted Precision: 0.991
- Macro Recall: 0.992
- Micro Recall: 0.991
- Weighted Recall: 0.991 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #unk #dataset-AyoubChLin/autotrain-data-roberta-large-bbc_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48943118458
- CO2 Emissions (in grams): 1.9844 ## Validation Metrics

- Loss: 0.062
- Accuracy: 0.991
- Macro F1: 0.991
- Micro F1: 0.991
- Weighted F1: 0.991
- Macro Precision: 0.991
- Micro Precision: 0.991
- Weighted Precision: 0.991
- Macro Recall: 0.992
- Micro Recall: 0.991
- Weighted Recall: 0.991 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain #unk #dataset-AyoubChLin/autotrain-data-roberta-large-bbc_news #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 48943118458
- CO2 Emissions (in grams): 1.9844## Validation Metrics

- Loss: 0.062
- Accuracy: 0.991
- Macro F1: 0.991
- Micro F1: 0.991
- Weighted F1: 0.991
- Macro Precision: 0.991
- Micro Precision: 0.991
- Weighted Precision: 0.991
- Macro Recall: 0.992
- Micro Recall: 0.991
- Weighted Recall: 0.991## Usage

You can use cURL to access this model:



Or Python API: