Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/neuralmind/bert-large-portuguese-cased/README.md

README.md

@@ -0,0 +1,106 @@
+---
+language: pt
+license: mit
+tags:
+  - bert
+  - pytorch
+datasets:
+  - brWaC
+---
+
+# BERTimbau Large (aka "bert-large-portuguese-cased")
+
+![Bert holding a berimbau](https://imgur.com/JZ7Hynh.jpg)
+
+## Introduction
+
+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.
+
+For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/).
+
+## Available models
+
+| Model                                    | Arch.      | #Layers | #Params |
+| ---------------------------------------- | ---------- | ------- | ------- |
+| `neuralmind/bert-base-portuguese-cased`  | BERT-Base  | 12      | 110M    |
+| `neuralmind/bert-large-portuguese-cased` | 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('neuralmind/bert-large-portuguese-cased')
+tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-large-portuguese-cased', do_lower_case=False)
+```
+
+### Masked language modeling prediction example
+
+```python
+from transformers import pipeline
+
+pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+
+pipe('Tinha uma [MASK] no meio do caminho.')
+# [{'score': 0.5054386258125305,
+#   'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]',
+#   'token': 5028,
+#   'token_str': 'pedra'},
+#  {'score': 0.05616172030568123,
+#   'sequence': '[CLS] Tinha uma curva no meio do caminho. [SEP]',
+#   'token': 9562,
+#   'token_str': 'curva'},
+#  {'score': 0.02348282001912594,
+#   'sequence': '[CLS] Tinha uma parada no meio do caminho. [SEP]',
+#   'token': 6655,
+#   'token_str': 'parada'},
+#  {'score': 0.01795753836631775,
+#   'sequence': '[CLS] Tinha uma mulher no meio do caminho. [SEP]',
+#   'token': 2606,
+#   'token_str': 'mulher'},
+#  {'score': 0.015246033668518066,
+#   'sequence': '[CLS] Tinha uma luz no meio do caminho. [SEP]',
+#   'token': 3377,
+#   'token_str': 'luz'}]
+
+```
+
+### For BERT embeddings
+
+```python
+
+import torch
+
+model = AutoModel.from_pretrained('neuralmind/bert-large-portuguese-cased')
+input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt')
+
+with torch.no_grad():
+    outs = model(input_ids)
+    encoded = outs[0][0, 1:-1]  # Ignore [CLS] and [SEP] special tokens
+
+# encoded.shape: (8, 1024)
+# tensor([[ 1.1872,  0.5606, -0.2264,  ...,  0.0117, -0.1618, -0.2286],
+#         [ 1.3562,  0.1026,  0.1732,  ..., -0.3855, -0.0832, -0.1052],
+#         [ 0.2988,  0.2528,  0.4431,  ...,  0.2684, -0.5584,  0.6524],
+#         ...,
+#         [ 0.3405, -0.0140, -0.0748,  ...,  0.6649, -0.8983,  0.5802],
+#         [ 0.1011,  0.8782,  0.1545,  ..., -0.1768, -0.8880, -0.1095],
+#         [ 0.7912,  0.9637, -0.3859,  ...,  0.2050, -0.1350,  0.0432]])
+```
+
+## Citation
+
+If you use our work, please cite:
+
+```bibtex
+@inproceedings{souza2020bertimbau,
+  author    = {F{\'a}bio Souza and
+               Rodrigo Nogueira and
+               Roberto Lotufo},
+  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
+  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
+  year      = {2020}
+}
+```