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Migrate model card from transformers-repo

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Read%20announcement%20at%20https%3A//discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755%0AOriginal%20file%20history%3A%20https%3A//github.com/huggingface/transformers/commits/master/model_cards/neuralmind/bert-base-portuguese-cased/README.md

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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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+
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+ # BERTimbau Base (aka "bert-base-portuguese-cased")
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+
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+ ![Bert holding a berimbau](https://imgur.com/JZ7Hynh.jpg)
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+
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+ ## Introduction
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+
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+ BERTimbau Base 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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+
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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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+
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+ ## Available models
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+
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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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+
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+ ## Usage
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+
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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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+
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+ model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-base-portuguese-cased')
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+ tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased', do_lower_case=False)
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+ ```
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+
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+ ### Masked language modeling prediction example
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
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+
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+ pipe('Tinha uma [MASK] no meio do caminho.')
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+ # [{'score': 0.14287759363651276,
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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': 0.06213393807411194,
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+ # 'sequence': '[CLS] Tinha uma árvore no meio do caminho. [SEP]',
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+ # 'token': 7411,
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+ # 'token_str': 'árvore'},
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+ # {'score': 0.05515013635158539,
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+ # 'sequence': '[CLS] Tinha uma estrada no meio do caminho. [SEP]',
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+ # 'token': 5675,
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+ # 'token_str': 'estrada'},
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+ # {'score': 0.0299188531935215,
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+ # 'sequence': '[CLS] Tinha uma casa no meio do caminho. [SEP]',
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+ # 'token': 1105,
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+ # 'token_str': 'casa'},
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+ # {'score': 0.025660505518317223,
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+ # 'sequence': '[CLS] Tinha uma cruz no meio do caminho. [SEP]',
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+ # 'token': 3466,
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+ # 'token_str': 'cruz'}]
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+
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+ ```
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+
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+ ### For BERT embeddings
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+
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+ ```python
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+ import torch
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+
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+ model = AutoModel.from_pretrained('neuralmind/bert-base-portuguese-cased')
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+ input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt')
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+
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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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+
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+ # encoded.shape: (8, 768)
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+ # tensor([[-0.0398, -0.3057, 0.2431, ..., -0.5420, 0.1857, -0.5775],
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+ # [-0.2926, -0.1957, 0.7020, ..., -0.2843, 0.0530, -0.4304],
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+ # [ 0.2463, -0.1467, 0.5496, ..., 0.3781, -0.2325, -0.5469],
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+ # ...,
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+ # [ 0.0662, 0.7817, 0.3486, ..., -0.4131, -0.2852, -0.2819],
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+ # [ 0.0662, 0.2845, 0.1871, ..., -0.2542, -0.2933, -0.0661],
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+ # [ 0.2761, -0.1657, 0.3288, ..., -0.2102, 0.0029, -0.2009]])
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+ ```
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+
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+ ## Citation
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+
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+ If you use our work, please cite:
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+
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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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+ ```