
# Model Trained Using AutoNLP

- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805

## Validation Metrics

- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967

## 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/Emanuel/autonlp-pos-tag-bosque-21124427
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("Emanuel/autonlp-pos-tag-bosque")

tokenizer = AutoTokenizer.from_pretrained("Emanuel/autonlp-pos-tag-bosque")

inputs = tokenizer("A noiva casa de branco", return_tensors="pt")

outputs = model(**inputs)

labelids = outputs.logits.squeeze().argmax(axis=-1)
labels = [model.config.id2label[int(x)] for x in labelids]
labels = labels[1:-1]# Filter start and end of sentence symbols

```
{"language": "pt", "tags": "autonlp", "datasets": ["Emanuel/autonlp-data-pos-tag-bosque"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 6.2107269129101805}
transformers pytorch bert token-classification autonlp pt dataset:Emanuel/autonlp-data-pos-tag-bosque co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #autonlp #pt #dataset-Emanuel/autonlp-data-pos-tag-bosque #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoNLP

- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805

## Validation Metrics

- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoNLP

- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805 ## Validation Metrics

- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #token-classification #autonlp #pt #dataset-Emanuel/autonlp-data-pos-tag-bosque #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoNLP

- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805 ## Validation Metrics

- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967 ## Usage

You can use cURL to access this model:



Or Python API:
passage: TAGS
#transformers #pytorch #bert #token-classification #autonlp #pt #dataset-Emanuel/autonlp-data-pos-tag-bosque #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoNLP

- Problem type: Entity Extraction
- Model ID: 21124427
- CO2 Emissions (in grams): 6.2107269129101805## Validation Metrics

- Loss: 0.09813392907381058
- Accuracy: 0.9714309035997062
- Precision: 0.9721275936822545
- Recall: 0.9735345807918949
- F1: 0.9728305785123967## Usage

You can use cURL to access this model:



Or Python API: