metadata

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: 0.7774097897260964
          - name: Pearson Correlation - assin2 Dataset
            type: Pearson Correlation
            value: 0.8097518625809903
          - name: Pearson Correlation - stsb_multi_mt pt Dataset
            type: Pearson Correlation
            value: 0.8358844307795662
          - name: Pearson Correlation - IRIS STS Dataset
            type: Pearson Correlation
            value: 0.7856746037418626

Work developed as part of Project IRIS.

Thesis: A Semantic Search System for Supremo Tribunal de Justiça

stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1 (Legal BERTimbau)

This is a sentence-transformers 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-nli-sts-v1 derives from stjiris/bert-large-portuguese-cased-legal-mlm (legal variant of BERTimbau large).

It was trained using the MLM technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 15000 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, assin2, stsb_multi_mt pt and IRIS STS datasets. 'lr': 1e-5

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]

model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-nli-sts-v1')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

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-nli-sts-v1')
model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-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

If you use this work, please cite:

@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}
}