
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3390592983
- CO2 Emissions (in grams): 0.0090

## Validation Metrics

- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.998
- Recall: 0.997
- AUC: 1.000
- F1: 0.998

## 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/davanstrien/autotrain-dataset-mentions-3390592983
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("davanstrien/autotrain-dataset-mentions-3390592983", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("davanstrien/autotrain-dataset-mentions-3390592983", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["en"], "tags": ["autotrain", "text-classification"], "datasets": ["davanstrien/autotrain-data-dataset-mentions"], "widget": [{"text": " frases-bertimbau-v0.4 This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset."}, {"text": "Model description BERTa is a transformer-based masked language model for the Catalan language. It is based on the [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) base model and has been trained on a medium-size corpus collected from publicly available corpora and crawlers"}, {"text": "Model description More information needed"}], "co2_eq_emissions": {"emissions": 0.008999666562870793}, "base_model": "neuralmind/bert-base-portuguese-cased"}
transformers pytorch distilbert text-classification autotrain en dataset:davanstrien/autotrain-data-dataset-mentions base_model:neuralmind/bert-base-portuguese-cased co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-davanstrien/autotrain-data-dataset-mentions #base_model-neuralmind/bert-base-portuguese-cased #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3390592983
- CO2 Emissions (in grams): 0.0090

## Validation Metrics

- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.998
- Recall: 0.997
- AUC: 1.000
- F1: 0.998

## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3390592983
- CO2 Emissions (in grams): 0.0090 ## Validation Metrics

- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.998
- Recall: 0.997
- AUC: 1.000
- F1: 0.998 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-davanstrien/autotrain-data-dataset-mentions #base_model-neuralmind/bert-base-portuguese-cased #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3390592983
- CO2 Emissions (in grams): 0.0090 ## Validation Metrics

- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.998
- Recall: 0.997
- AUC: 1.000
- F1: 0.998 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-davanstrien/autotrain-data-dataset-mentions #base_model-neuralmind/bert-base-portuguese-cased #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# Model Trained Using AutoTrain

- Problem type: Binary Classification
- Model ID: 3390592983
- CO2 Emissions (in grams): 0.0090## Validation Metrics

- Loss: 0.014
- Accuracy: 0.997
- Precision: 0.998
- Recall: 0.997
- AUC: 1.000
- F1: 0.998## Usage

You can use cURL to access this model:



Or Python API: