metadata

tags:
  - autotrain
  - text-classification
language:
  - pt
widget:
  - text: I love AutoTrain 🤗
datasets:
  - alexandreteles/autotrain-data-told_br_binary_sm_bertimbau
co2_eq_emissions:
  emissions: 1.778776476039011

Model Trained Using AutoTrain

Problem type: Binary Classification
Model ID: 2489776826
Base model: bert-base-portuguese-cased
Parameters: 109M
Model size: 416MB
CO2 Emissions (in grams): 1.7788

Validation Metrics

Loss: 0.412
Accuracy: 0.815
Precision: 0.793
Recall: 0.794
AUC: 0.895
F1: 0.793

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_bertimbau-2489776826

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("alexandreteles/autotrain-told_br_binary_sm_bertimbau-2489776826", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("alexandreteles/autotrain-told_br_binary_sm_bertimbau-2489776826", use_auth_token=True)

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

outputs = model(**inputs)