rufimelo/Legal-BERTimbau-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-BERTimbau-sts-large is based on Legal-BERTimbau-large which derives from BERTimbau large. 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-BERTimbau-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-BERTimbau-sts-large')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-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 | 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 is based on Legal-BERTimbau-large which 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
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}
}
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Datasets used to train rufimelo/Legal-BERTimbau-sts-large
Evaluation results
- Pearson Correlation - assin Datasetself-reported0.766
- Pearson Correlation - assin2 Datasetself-reported0.824
- Pearson Correlation - stsb_multi_mt pt Datasetself-reported0.791