metadata
language:
- pt
thumbnail: Portugues SBERT for the Legal Domain
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
rufimelo/Legal-SBERTimbau-nli-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. Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from BERTimbau Large. It is adapted to the Portuguese legal domain.
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-nli-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-nli-large')
model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-nli-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-large | Assin | 0.766293861 |
| Legal-SBERTimbau-large | Assin2 | 0.823565322 |
| ---------------------------------------- | ---------- | ---------- |
| paraphrase-multilingual-mpnet-base-v2 | Assin | 0.743740222 |
| paraphrase-multilingual-mpnet-base-v2 | Assin2 | 0.823565322 |
| 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
Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from BERTimbau Large. It was trained for Natural Language Inference (NLI). This was chosen due to the lack of Portuguese available data. In addition to that, it was submitted to a fine tuning stage with the assin and assin2 datasets.
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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}
}