BERTabaporu: a genre-specific pre-trained model of Portuguese-speaking social media

Introduction

Having the same architecture of [Bert] we trained our model from scratch following BERT pre-training procedure. And has been built from a collection of about 238 million tweets written by over 100 thousand unique Twitter users, and conveying over 2.9 billion words in total.

Available models

Model	Arch.	#Layers	#Params
`pablocosta/bertabaporu-base-uncased`	BERT-Base	12	110M
`pablocosta/bertabaporu-large-uncased`	BERT-Large	24	335M

Usage

from transformers import AutoTokenizer  # Or BertTokenizer
from transformers import AutoModelForPreTraining  # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel  # or BertModel, for BERT without pretraining heads
model = AutoModelForPreTraining.from_pretrained('pablocosta/bertabaporu-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('pablocosta/bertabaporu-base-uncased')