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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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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: 0.7800806555562139 |
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- name: Pearson Correlation - assin2 Dataset |
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type: Pearson Correlation |
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value: 0.841456941132706 |
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- name: Pearson Correlation - stsb_multi_mt pt Dataset |
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type: Pearson Correlation |
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value: 0.8506042636740455 |
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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-mkd-nli-sts-v0 (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-mkd-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-mlm (legal variant of [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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This model was subjected to Multilingual Knowledge Distillation technique (mkd). For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese |
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The dataset used was: TED 2020 – Parallel Sentences Corpus. TED 2020 contains around 4000 TED and TED-X transcripts from July 2020. These transcripts were translated by volunteers into more than 100 languages, adding up to a total of 10 544 174 sentences. |
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The model was presented to NLI data. 16 batch size, 2e-5 lr |
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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) 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-mkd-nli-sts-v0') |
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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-mkd-nli-sts-v0') |
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model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-mkd-nli-sts-v0') |
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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 and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, |
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title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, |
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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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@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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@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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``` |
