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--- |
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language: |
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- pt |
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thumbnail: Portuguese BERT for the Legal Domain |
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tags: |
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- sentence-transformers |
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- transformers |
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- bert |
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- pytorch |
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- sentence-similarity |
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license: mit |
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pipeline_tag: sentence-similarity |
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datasets: |
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- stjiris/portuguese-legal-sentences-v0 |
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- assin |
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- assin2 |
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- stsb_multi_mt |
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- stjiris/IRIS_sts |
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widget: |
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- source_sentence: "O advogado apresentou as provas ao juíz." |
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sentences: |
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- "O juíz leu as provas." |
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- "O juíz leu o recurso." |
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- "O juíz atirou uma pedra." |
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model-index: |
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- name: BERTimbau |
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results: |
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- task: |
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name: STS |
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type: STS |
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metrics: |
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- name: Pearson Correlation - assin Dataset |
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type: Pearson Correlation |
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value: 0.7843350530283666 |
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- name: Pearson Correlation - assin2 Dataset |
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type: Pearson Correlation |
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value: 0.8161009943619048 |
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- name: Pearson Correlation - stsb_multi_mt pt Dataset |
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type: Pearson Correlation |
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value: 0.8432039975708361 |
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- name: Pearson Correlation - IRIS STS Dataset |
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type: Pearson Correlation |
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value: 0.7842761087524468 |
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--- |
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[](https://www.inesc-id.pt/projects/PR07005/) |
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[](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) |
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Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). |
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Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) |
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# stjiris/bert-large-portuguese-cased-legal-tsdae-sts-v1 (Legal BERTimbau) |
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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. |
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stjiris/bert-large-portuguese-cased-legal-tsdae derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large. |
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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) |
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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 |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["Isto é um exemplo", "Isto é um outro exemplo"] |
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model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-tsdae-nli-sts-v1') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-sts-v1') |
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model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-sts-v1') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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## Citing & Authors |
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### Contributions |
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[@rufimelo99](https://github.com/rufimelo99) |
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If you use this work, please cite: |
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```bibtex |
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@InProceedings{MeloSemantic, |
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author="Melo, Rui |
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and Santos, Pedro A. |
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and Dias, Jo{\~a}o", |
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editor="Moniz, Nuno |
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and Vale, Zita |
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and Cascalho, Jos{\'e} |
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and Silva, Catarina |
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and Sebasti{\~a}o, Raquel", |
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title="A Semantic Search System for the Supremo Tribunal de Justi{\c{c}}a", |
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booktitle="Progress in Artificial Intelligence", |
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year="2023", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="142--154", |
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abstract="Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justi{\c{c}}a (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a {\$}{\$}335{\backslash}{\%}{\$}{\$}335{\%}increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.", |
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isbn="978-3-031-49011-8" |
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} |
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|
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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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|
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@inproceedings{fonseca2016assin, |
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title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, |
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author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, |
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booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, |
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pages={13--15}, |
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year={2016} |
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} |
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|
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@inproceedings{real2020assin, |
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title={The assin 2 shared task: a quick overview}, |
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author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, |
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booktitle={International Conference on Computational Processing of the Portuguese Language}, |
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pages={406--412}, |
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year={2020}, |
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organization={Springer} |
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} |
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@InProceedings{huggingface:dataset:stsb_multi_mt, |
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title = {Machine translated multilingual STS benchmark dataset.}, |
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author={Philip May}, |
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year={2021}, |
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url={https://github.com/PhilipMay/stsb-multi-mt} |
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} |
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``` |
