metadata

tags:
  - autotrain
  - text-classification
language:
  - pt
widget:
  - text: I love AutoTrain 🤗
datasets:
  - alexandreteles/autotrain-data-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: told-br
          name: told-br
        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

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 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)