
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455

## Validation Metrics

- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758


## 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/milyiyo/autotrain-iptc-classification-v4-42015107919
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("milyiyo/autotrain-iptc-classification-v4-42015107919", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("milyiyo/autotrain-iptc-classification-v4-42015107919", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["es"], "tags": ["autotrain", "text-classification"], "datasets": ["milyiyo/autotrain-data-iptc-classification-v4"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 0.845545764970478}}
transformers pytorch bert text-classification autotrain es dataset:milyiyo/autotrain-data-iptc-classification-v4 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #es #dataset-milyiyo/autotrain-data-iptc-classification-v4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455

## Validation Metrics

- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455 ## Validation Metrics

- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #es #dataset-milyiyo/autotrain-data-iptc-classification-v4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455 ## Validation Metrics

- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #es #dataset-milyiyo/autotrain-data-iptc-classification-v4 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 42015107919
- CO2 Emissions (in grams): 0.8455## Validation Metrics

- Loss: 1.231
- Accuracy: 0.758
- Macro F1: 0.531
- Micro F1: 0.758
- Weighted F1: 0.708
- Macro Precision: 0.532
- Micro Precision: 0.758
- Weighted Precision: 0.685
- Macro Recall: 0.553
- Micro Recall: 0.758
- Weighted Recall: 0.758## Usage

You can use cURL to access this model:



Or Python API: