Update README.md

README.md

@@ -1,3 +1,112 @@
 ---
-license: mit
+language: 
+  - pt
+thumbnail: "Portuguese BERT for the Legal Domain"
+tags:
+- sentence-transformers
+- transformers
 ---
+
+# stjiris/bert-large-portuguese-cased-legal-mlm (Legal BERTimbau)
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+stjiris/bert-large-portuguese-cased-legal-mlm derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
+
+It was trained using the MLM technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 5300 training steps (best performance for our semantic search system implementation)
+
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+```
+pip install -U sentence-transformers
+```
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
+
+model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+## Usage (HuggingFace Transformers)
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm')
+model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
+)
+```
+## Citing & Authors
+
+If you use this work, please cite:
+
+```bibtex
+@inproceedings{MeloSemantic,
+	author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
+	title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
+}
+
+@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}
+}
+
+@inproceedings{fonseca2016assin,
+  title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
+  author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
+  booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
+  pages={13--15},
+  year={2016}
+}
+
+@inproceedings{real2020assin,
+  title={The assin 2 shared task: a quick overview},
+  author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
+  booktitle={International Conference on Computational Processing of the Portuguese Language},
+  pages={406--412},
+  year={2020},
+  organization={Springer}
+}
+@InProceedings{huggingface:dataset:stsb_multi_mt,
+title = {Machine translated multilingual STS benchmark dataset.},
+author={Philip May},
+year={2021},
+url={https://github.com/PhilipMay/stsb-multi-mt}
+}
+
+```