Instructions to use rdenadai/BR_BERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdenadai/BR_BERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rdenadai/BR_BERTo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rdenadai/BR_BERTo") model = AutoModelForMaskedLM.from_pretrained("rdenadai/BR_BERTo") - Notebooks
- Google Colab
- Kaggle
metadata
language: pt
tags:
- portuguese
- brazil
- pt_BR
widget:
- text: gostei muito dessa <mask>
BR_BERTo
Portuguese (Brazil) model for text inference.
Params
Trained on a corpus of 6_993_330 sentences.
- Vocab size: 150_000
- RobertaForMaskedLM size : 512
- Num train epochs: 3
- Time to train: ~10days (on GCP with a Nvidia T4)
I follow the great tutorial from HuggingFace team:
How to train a new language model from scratch using Transformers and Tokenizers
More infor here: