
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142

## Validation Metrics

- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863


## 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/pedro-m4u/autotrain-new_1000_respostas-3526695346
```

Or Python API:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("pedro-m4u/autotrain-new_1000_respostas-3526695346", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("pedro-m4u/autotrain-new_1000_respostas-3526695346", use_auth_token=True)

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

outputs = model(**inputs)
```
{"language": ["pt"], "tags": ["autotrain", "text-classification"], "datasets": ["pedro-m4u/autotrain-data-new_1000_respostas"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": {"emissions": 1.7141641973570885}}
transformers pytorch bert text-classification autotrain pt dataset:pedro-m4u/autotrain-data-new_1000_respostas co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain #pt #dataset-pedro-m4u/autotrain-data-new_1000_respostas #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142

## Validation Metrics

- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863


## Usage

You can use cURL to access this model:



Or Python API:


# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142 ## Validation Metrics

- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #pt #dataset-pedro-m4u/autotrain-data-new_1000_respostas #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142 ## Validation Metrics

- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863 ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #bert #text-classification #autotrain #pt #dataset-pedro-m4u/autotrain-data-new_1000_respostas #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification
- Model ID: 3526695346
- CO2 Emissions (in grams): 1.7142## Validation Metrics

- Loss: 0.483
- Accuracy: 0.863
- Macro F1: 0.821
- Micro F1: 0.863
- Weighted F1: 0.858
- Macro Precision: 0.876
- Micro Precision: 0.863
- Weighted Precision: 0.866
- Macro Recall: 0.813
- Micro Recall: 0.863
- Weighted Recall: 0.863## Usage

You can use cURL to access this model:



Or Python API: