Update bert-base-portuguese-cased and bert-large-portuguese-cased model cards (#6527)

Co-authored-by: Fabio Souza <fabiosouza@neuralmind.ai>
2020-08-16 23:49:49 -03:00 · 2020-08-16 23:49:49 -03:00 · 6d38ab1cc3
parent 84c265ffcc
commit 6d38ab1cc3
2 changed files with 203 additions and 2 deletions
--- a/model_cards/neuralmind/bert-base-portuguese-cased/README.md
+++ b/model_cards/neuralmind/bert-base-portuguese-cased/README.md
@ -1,5 +1,105 @@
 ---
 language: pt
+license: mit
+tags:
+  - bert
+  - pytorch
+datasets:
+  - brWaC
 ---

-## bert-base-portuguese-cased
+# BERTimbau Base (aka "bert-base-portuguese-cased")
+
+![Bert holding a berimbau](https://imgur.com/JZ7Hynh.jpg)
+
+## Introduction
+
+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.
+
+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-base-portuguese-cased')
+tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-base-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': 0.14287759363651276,
+#  'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]',
+#  'token': 5028,
+#  'token_str': 'pedra'},
+# {'score': 0.06213393807411194,
+#  'sequence': '[CLS] Tinha uma árvore no meio do caminho. [SEP]',
+#  'token': 7411,
+#  'token_str': 'árvore'},
+# {'score': 0.05515013635158539,
+#  'sequence': '[CLS] Tinha uma estrada no meio do caminho. [SEP]',
+#  'token': 5675,
+#  'token_str': 'estrada'},
+# {'score': 0.0299188531935215,
+#  'sequence': '[CLS] Tinha uma casa no meio do caminho. [SEP]',
+#  'token': 1105,
+#  'token_str': 'casa'},
+# {'score': 0.025660505518317223,
+#  'sequence': '[CLS] Tinha uma cruz no meio do caminho. [SEP]',
+#  'token': 3466,
+#  'token_str': 'cruz'}]
+
+```
+
+### For BERT embeddings
+
+```python
+import torch
+
+model = AutoModel.from_pretrained('neuralmind/bert-base-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, 768)
+# tensor([[-0.0398, -0.3057,  0.2431,  ..., -0.5420,  0.1857, -0.5775],
+#         [-0.2926, -0.1957,  0.7020,  ..., -0.2843,  0.0530, -0.4304],
+#         [ 0.2463, -0.1467,  0.5496,  ...,  0.3781, -0.2325, -0.5469],
+#         ...,
+#         [ 0.0662,  0.7817,  0.3486,  ..., -0.4131, -0.2852, -0.2819],
+#         [ 0.0662,  0.2845,  0.1871,  ..., -0.2542, -0.2933, -0.0661],
+#         [ 0.2761, -0.1657,  0.3288,  ..., -0.2102,  0.0029, -0.2009]])
+```
+
+## 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}
+}
+```
--- a/model_cards/neuralmind/bert-large-portuguese-cased/README.md
+++ b/model_cards/neuralmind/bert-large-portuguese-cased/README.md
@ -1,5 +1,106 @@
 ---
 language: pt
+license: mit
+tags:
+  - bert
+  - pytorch
+datasets:
+  - brWaC
 ---

-## bert-large-portuguese-cased
+# 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}
+}
+```