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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


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-large-uncased')
tokenizer = AutoTokenizer.from_pretrained('pablocosta/bertabaporu-large-uncased')
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