readme: ---
  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)
  ```