Text Classification
Transformers
Safetensors
Portuguese
bert
toxicity
portuguese
text-embeddings-inference
Instructions to use Polygl0t/portuguese-bertimbau-toxicity-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Polygl0t/portuguese-bertimbau-toxicity-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Polygl0t/portuguese-bertimbau-toxicity-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Polygl0t/portuguese-bertimbau-toxicity-classifier") model = AutoModelForSequenceClassification.from_pretrained("Polygl0t/portuguese-bertimbau-toxicity-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download emissions.csv from Polygl0t/portuguese-bertimbau-toxicity-classifier: direct link, hf CLI and curl.
- Browser
- Download file 843 Bytes
-
https://huggingface.co/Polygl0t/portuguese-bertimbau-toxicity-classifier/resolve/main/emissions.csv
- Command line
-
hf download hf://Polygl0t/portuguese-bertimbau-toxicity-classifier/emissions.csv
-
curl -L -o emissions.csv https://huggingface.co/Polygl0t/portuguese-bertimbau-toxicity-classifier/resolve/main/emissions.csv
843 Bytes
| timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue | |
| 2025-04-14T15:57:12,codecarbon,27d6fef3-212a-4619-8011-3a8215395c34,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,20067.527782217,1.6780737380863917,8.362134869315741e-05,112.5,303.7843516981492,365.62500000000006,0.6271095488002801,1.740715106182082,2.0371464385637443,4.404971093546113,Germany,DEU,north rhine-westphalia,,,Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34,3.11.3,2.7.1,128,AMD EPYC 7713 64-Core Processor,1,1 x NVIDIA A100-SXM4-80GB,7.1042,50.7338,975,machine,N,1.0 | |