Legal BERTimbau Base TSDAE Sts

🚀 rufimelo/Legal-BERTimbau-base-TSDAE-sts

This is a sentence-transformers model that maps sentences and paragraphs to a 1024-dimensional dense vector space. It can be used for tasks such as clustering or semantic search. The rufimelo/Legal-BERTimbau-base-TSDAE-sts model is based on Legal-BERTimbau-large, which is derived from the BERTimbau large model. It is adapted to the Portuguese legal domain and trained for Semantic Textual Similarity (STS) on Portuguese datasets.

📦 Model Information

🚀 Quick Start

Installation

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

pip install -U sentence-transformers

Basic Usage

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

model = SentenceTransformer('rufimelo/Legal-BERTimbau-base-TSDAE-sts')
embeddings = model.encode(sentences)
print(embeddings)

💻 Usage Examples

Advanced 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-BERTimbau-base-TSDAE-sts')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-base-TSDAE-sts')

# 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

🔧 Training

rufimelo/Legal-BERTimbau-base-TSDAE-sts is based on rufimelo/Legal-BERTimbau-base-TSDAE, which is derived from BERTimbau large.

rufimelo/Legal-BERTimbau-base-TSDAE was trained with TSDAE on 50000 cleaned documents (https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v1) with a learning rate of 1e-5. It was further fine-tuned for Semantic Textual Similarity on the assin, assin2, and stsb_multi_mt pt datasets with a learning rate of 1e-5.

📚 Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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:

@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}
}