--- language: - pt thumbnail: Portuguese BERT for the Legal Domain tags: - sentence-transformers - transformers - bert - pytorch - sentence-similarity license: mit pipeline_tag: sentence-similarity datasets: - stjiris/portuguese-legal-sentences-v0 - assin - assin2 - stsb_multi_mt - stjiris/IRIS_sts widget: - source_sentence: "O advogado apresentou as provas ao juíz." sentences: - "O juíz leu as provas." - "O juíz leu o recurso." - "O juíz atirou uma pedra." model-index: - name: BERTimbau results: - task: name: STS type: STS metrics: - name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.7763420633772975 - name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.8067374216274927 - name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.8388993109077857 - name: Pearson Correlation - IRIS STS Dataset type: Pearson Correlation value: 0.7931353381814285 --- ![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png) ![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png) Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) # stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1 (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-tsdae derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large. It was trained using the TSDAE technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 21.2k training steps (best performance for our semantic search system implementation) The model was presented to NLI data. 16 batch size, 2e-5 lr It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) and [IRIS STS](https://huggingface.co/datasets/stjiris/IRIS_sts) datasets. 'lr': 1e-5 ## 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-tsdae-nli-sts-v1') 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-tsdae-nli-sts-v1') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1') # 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 ### Contributions [@rufimelo99](https://github.com/rufimelo99) 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} } ```