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--- |
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language: pt |
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license: mit |
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tags: |
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- bert |
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- pytorch |
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datasets: |
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- brWaC |
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--- |
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# BERTimbau Large (aka "bert-large-portuguese-cased") |
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## Introduction |
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BERTimbau Large is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. |
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For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/). |
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## Available models |
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| Model | Arch. | #Layers | #Params | |
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| ---------------------------------------- | ---------- | ------- | ------- | |
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| `neuralmind/bert-base-portuguese-cased` | BERT-Base | 12 | 110M | |
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| `neuralmind/bert-large-portuguese-cased` | BERT-Large | 24 | 335M | |
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## Usage |
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```python |
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from transformers import AutoTokenizer # Or BertTokenizer |
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from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads |
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from transformers import AutoModel # or BertModel, for BERT without pretraining heads |
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model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-large-portuguese-cased') |
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tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-large-portuguese-cased', do_lower_case=False) |
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``` |
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### Masked language modeling prediction example |
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```python |
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from transformers import pipeline |
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pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer) |
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pipe('Tinha uma [MASK] no meio do caminho.') |
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# [{'score': 0.5054386258125305, |
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# 'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]', |
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# 'token': 5028, |
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# 'token_str': 'pedra'}, |
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# {'score': 0.05616172030568123, |
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# 'sequence': '[CLS] Tinha uma curva no meio do caminho. [SEP]', |
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# 'token': 9562, |
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# 'token_str': 'curva'}, |
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# {'score': 0.02348282001912594, |
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# 'sequence': '[CLS] Tinha uma parada no meio do caminho. [SEP]', |
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# 'token': 6655, |
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# 'token_str': 'parada'}, |
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# {'score': 0.01795753836631775, |
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# 'sequence': '[CLS] Tinha uma mulher no meio do caminho. [SEP]', |
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# 'token': 2606, |
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# 'token_str': 'mulher'}, |
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# {'score': 0.015246033668518066, |
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# 'sequence': '[CLS] Tinha uma luz no meio do caminho. [SEP]', |
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# 'token': 3377, |
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# 'token_str': 'luz'}] |
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``` |
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### For BERT embeddings |
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```python |
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import torch |
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model = AutoModel.from_pretrained('neuralmind/bert-large-portuguese-cased') |
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input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt') |
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with torch.no_grad(): |
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outs = model(input_ids) |
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encoded = outs[0][0, 1:-1] # Ignore [CLS] and [SEP] special tokens |
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# encoded.shape: (8, 1024) |
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# tensor([[ 1.1872, 0.5606, -0.2264, ..., 0.0117, -0.1618, -0.2286], |
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# [ 1.3562, 0.1026, 0.1732, ..., -0.3855, -0.0832, -0.1052], |
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# [ 0.2988, 0.2528, 0.4431, ..., 0.2684, -0.5584, 0.6524], |
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# ..., |
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# [ 0.3405, -0.0140, -0.0748, ..., 0.6649, -0.8983, 0.5802], |
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# [ 0.1011, 0.8782, 0.1545, ..., -0.1768, -0.8880, -0.1095], |
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# [ 0.7912, 0.9637, -0.3859, ..., 0.2050, -0.1350, 0.0432]]) |
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``` |
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## Citation |
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If you use our work, please cite: |
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```bibtex |
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@inproceedings{souza2020bertimbau, |
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author = {F{\'a}bio Souza and |
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Rodrigo Nogueira and |
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Roberto Lotufo}, |
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title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, |
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booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, |
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year = {2020} |
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} |
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``` |
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