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)
