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---
tags:
- transformers
- pytorch
- autotrain
- text-classification
language:
- pt
widget:
- text: I love AutoTrain 🤗
datasets:
- alexandreteles/told_br_binary_sm
co2_eq_emissions:
emissions: 4.429755329718354
model-index:
- name: told_br_binary_sm
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
type: alexandreteles/told_br_binary_sm
name: told-br-small
metrics:
- type: accuracy
value: 0.8
name: Accuracy
verified: true
- type: f1
value: 0.759
name: F1
verified: true
- type: roc_auc
value: 0.891
name: AUC
verified: true
library_name: transformers
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2489276793
- Base model: bert-base-multilingual-cased
- Parameters: 109M
- Model size: 416MB
- CO2 Emissions (in grams): 4.4298
## Validation Metrics
- Loss: 0.432
- Accuracy: 0.800
- Precision: 0.823
- Recall: 0.704
- AUC: 0.891
- F1: 0.759
## Usage
This model was trained on a random subset of the [told-br](https://huggingface.co/datasets/told-br) dataset (1/3 of the original size). Our main objective is to provide a small
model that can be used to classify Brazilian Portuguese tweets in a binary way ('toxic' or 'non toxic').
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/alexandreteles/autotrain-told_br_binary_sm-2489276793
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alexandreteles/told_br_binary_sm")
tokenizer = AutoTokenizer.from_pretrained("alexandreteles/told_br_binary_sm")
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |