metadata

language:
  - pt
thumbnail: Portugues SBERT for the Legal Domain
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - transformers
datasets:
  - assin
  - assin2
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.
    example_title: Example 1
metrics:
  - bleu

rufimelo/Legal-SBERTimbau-sts-large

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. rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-large which derives from BERTimbau alrge. It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.

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('rufimelo/Legal-SBERTimbau-sts-large')
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('rufimelo/Legal-SBERTimbau-sts-large')
model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large')

# 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)

Evaluation Results STS

Model	Dataset	PearsonCorrelation
Legal-SBERTimbau-sts-large	Assin	0.76629
Legal-SBERTimbau-sts-large	Assin2	0.82357
Legal-SBERTimbau-sts-base	Assin	0.71457
Legal-SBERTimbau-sts-base	Assin2	0.73545
Legal-SBERTimbau-sts-large-v2	Assin	0.76299
Legal-SBERTimbau-sts-large-v2	Assin2	0.81121
Legal-SBERTimbau-sts-large-v2	stsb_multi_mt pt	0.81726
----------------------------------------	----------	----------
paraphrase-multilingual-mpnet-base-v2	Assin	0.71457
paraphrase-multilingual-mpnet-base-v2	Assin2	0.79831
paraphrase-multilingual-mpnet-base-v2	stsb_multi_mt pt	0.83999
paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)	Assin	0.77641
paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)	Assin2	0.79831
paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)	stsb_multi_mt pt	0.84575

Training

rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-largewhich derives from BERTimbau large. It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin and assin2 datasets.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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 BERTimbau's work:

@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}
}