🚀 rufimelo/Legal-BERTimbau-sts-large-ma-v3
This is a sentence-transformers model that maps sentences and paragraphs to a 1024-dimensional dense vector space, useful for tasks like clustering or semantic search.
🚀 Quick Start
This model is a sentence-transformers model. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for tasks such as clustering or semantic search. rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large, which is derived from BERTimbau large. It is adapted to the Portuguese legal domain and trained for STS on Portuguese datasets.
✨ Features
- Maps sentences and paragraphs to a 1024-dimensional dense vector space.
- Can be used for clustering or semantic search.
- Adapted to the Portuguese legal domain.
- Trained for STS on Portuguese datasets.
📦 Installation
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
💻 Usage Examples
Basic Usage
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
model = SentenceTransformer('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
embeddings = model.encode(sentences)
print(embeddings)
Advanced Usage
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
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 = ['This is an example sentence', 'Each sentence is converted']
tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
📚 Documentation
Evaluation Results STS
Model |
Assin |
Assin2 |
stsb_multi_mt pt |
avg |
Legal-BERTimbau-sts-base |
0.71457 |
0.73545 |
0.72383 |
0.72462 |
Legal-BERTimbau-sts-base-ma |
0.74874 |
0.79532 |
0.82254 |
0.78886 |
Legal-BERTimbau-sts-base-ma-v2 |
0.75481 |
0.80262 |
0.82178 |
0.79307 |
Legal-BERTimbau-base-TSDAE-sts |
0.78814 |
0.81380 |
0.75777 |
0.78657 |
Legal-BERTimbau-sts-large |
0.76629 |
0.82357 |
0.79120 |
0.79369 |
Legal-BERTimbau-sts-large-v2 |
0.76299 |
0.81121 |
0.81726 |
0.79715 |
Legal-BERTimbau-sts-large-ma |
0.76195 |
0.81622 |
0.82608 |
0.80142 |
Legal-BERTimbau-sts-large-ma-v2 |
0.7836 |
0.8462 |
0.8261 |
0.81863 |
Legal-BERTimbau-sts-large-ma-v3 |
0.7749 |
0.8470 |
0.8364 |
0.81943 |
Legal-BERTimbau-large-v2-sts |
0.71665 |
0.80106 |
0.73724 |
0.75165 |
Legal-BERTimbau-large-TSDAE-sts |
0.72376 |
0.79261 |
0.73635 |
0.75090 |
Legal-BERTimbau-large-TSDAE-sts-v2 |
0.81326 |
0.83130 |
0.786314 |
0.81029 |
Legal-BERTimbau-large-TSDAE-sts-v3 |
0.80703 |
0.82270 |
0.77638 |
0.80204 |
BERTimbau base Fine - tuned for STS |
0.78455 |
0.80626 |
0.82841 |
0.80640 |
BERTimbau large Fine - tuned for STS |
0.78193 |
0.81758 |
0.83784 |
0.81245 |
paraphrase - multilingual - mpnet - base - v2 |
0.71457 |
0.79831 |
0.83999 |
0.78429 |
paraphrase - multilingual - mpnet - base - v2 Fine - tuned with assin(s) |
0.77641 |
0.79831 |
0.84575 |
0.80682 |
Training
rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from BERTimbau large.
Firstly, due to the lack of Portuguese datasets, it was trained using multilingual knowledge distillation. 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.
It was trained for Semantic Textual Similarity, being submitted to a fine - tuning stage with the assin, assin2 and stsb_multi_mt pt datasets. (batch 8, 5 epochs 'lr': 1e - 5)
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
📄 License
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}
}