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---
language: pt
license: mit
tags:
- bert
- pytorch
datasets:
- Twitter
---
# <a name="introduction"></a> 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](https://arxiv.org/abs/1810.04805) 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
```python
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')
```
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