
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 202661

## Validation Metrics

- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987


## 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 AutoNLP"}' https://api-inference.huggingface.co/models/hectorcotelo/autonlp-spanish_songs-202661
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": "es", "tags": "autonlp", "datasets": ["hectorcotelo/autonlp-data-spanish_songs"], "widget": [{"text": "Y si me tomo una cerveza Vuelves a mi cabeza Y empiezo a recordarte Es que me gusta c\u00f3mo besas Con tu delicadeza Puede ser que T\u00fa y yo, somos el uno para el otro Que no dejo de pensarte Quise olvidarte y tom\u00e9 un poco Y result\u00f3 extra\u00f1arte, yeah"}]}
transformers pytorch bert text-classification autonlp es dataset:hectorcotelo/autonlp-data-spanish_songs autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 202661

## Validation Metrics

- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 202661 ## Validation Metrics

- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 202661 ## Validation Metrics

- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoNLP

- Problem type: Multi-class Classification
- Model ID: 202661## Validation Metrics

- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987## Usage

You can use cURL to access this model:



Or Python API: