

[![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)](https://www.inesc-id.pt/projects/PR07005/)

[![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)

Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).

Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)

# stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1 (Legal BERTimbau)
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-tsdae (legal variant of [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large).

It was trained using the TSDAE technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 212k training steps (best performance for our semantic search system implementation)
It was presented to Generative Pseudo Labeling training.

The model was presented to NLI data. 16 batch size,  2e-5 lr

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt)  datasets. 'lr': 1e-5

This model was subjected to Metadata Knowledge Distillation. [Repository](https://github.com/rufimelo99/metadata-knowledge-distillation)
Trial Technique to improve information retrieval through dense vectors: Metadata Knowledge Distillation

## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]

model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
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('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1')
model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1')

# 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)
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1028, '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

### Contributions
[@rufimelo99](https://github.com/rufimelo99)

If you use this work, please cite:

```bibtex
@inproceedings{MeloSemantic,
	author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
	title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
}

@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}
}

```
{"language": ["pt"], "license": "mit", "tags": ["sentence-transformers", "transformers", "bert", "pytorch", "sentence-similarity"], "datasets": ["stjiris/portuguese-legal-sentences-v0", "assin", "assin2", "stsb_multi_mt"], "thumbnail": "Portuguese BERT for the Legal Domain", "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "O advogado apresentou as provas ao ju\u00edz.", "sentences": ["O ju\u00edz leu as provas.", "O ju\u00edz leu o recurso.", "O ju\u00edz atirou uma pedra."]}]}
sentence-transformers pytorch bert feature-extraction transformers sentence-similarity pt dataset:stjiris/portuguese-legal-sentences-v0 dataset:assin dataset:assin2 dataset:stsb_multi_mt license:mit model-index endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #transformers #sentence-similarity #pt #dataset-stjiris/portuguese-legal-sentences-v0 #dataset-assin #dataset-assin2 #dataset-stsb_multi_mt #license-mit #model-index #endpoints_compatible #region-us 



![INESC-ID](URL

![A Semantic Search System for Supremo Tribunal de Justiça](URL

Work developed as part of Project IRIS.

Thesis: A Semantic Search System for Supremo Tribunal de Justiça

# stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1 (Legal BERTimbau)
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.
stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-tsdae (legal variant of BERTimbau large).

It was trained using the TSDAE technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 212k training steps (best performance for our semantic search system implementation)
It was presented to Generative Pseudo Labeling training.

The model was presented to NLI data. 16 batch size,  2e-5 lr

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin, assin2, stsb_multi_mt pt  datasets. 'lr': 1e-5

This model was subjected to Metadata Knowledge Distillation. Repository
Trial Technique to improve information retrieval through dense vectors: Metadata Knowledge Distillation

## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this:

## Usage (HuggingFace Transformers)



## Full Model Architecture

## Citing & Authors

### Contributions
@rufimelo99

If you use this work, please cite:


# stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1 (Legal BERTimbau)
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.
stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-tsdae (legal variant of BERTimbau large).

It was trained using the TSDAE technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 212k training steps (best performance for our semantic search system implementation)
It was presented to Generative Pseudo Labeling training.

The model was presented to NLI data. 16 batch size,  2e-5 lr

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin, assin2, stsb_multi_mt pt  datasets. 'lr': 1e-5

This model was subjected to Metadata Knowledge Distillation. Repository
Trial Technique to improve information retrieval through dense vectors: Metadata Knowledge Distillation ## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this: ## Usage (HuggingFace Transformers) ## Full Model Architecture ## Citing & Authors ### Contributions
@rufimelo99

If you use this work, please cite:
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #transformers #sentence-similarity #pt #dataset-stjiris/portuguese-legal-sentences-v0 #dataset-assin #dataset-assin2 #dataset-stsb_multi_mt #license-mit #model-index #endpoints_compatible #region-us 
 # stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1 (Legal BERTimbau)
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.
stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-tsdae (legal variant of BERTimbau large).

It was trained using the TSDAE technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 212k training steps (best performance for our semantic search system implementation)
It was presented to Generative Pseudo Labeling training.

The model was presented to NLI data. 16 batch size,  2e-5 lr

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin, assin2, stsb_multi_mt pt  datasets. 'lr': 1e-5

This model was subjected to Metadata Knowledge Distillation. Repository
Trial Technique to improve information retrieval through dense vectors: Metadata Knowledge Distillation ## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this: ## Usage (HuggingFace Transformers) ## Full Model Architecture ## Citing & Authors ### Contributions
@rufimelo99

If you use this work, please cite:
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #transformers #sentence-similarity #pt #dataset-stjiris/portuguese-legal-sentences-v0 #dataset-assin #dataset-assin2 #dataset-stsb_multi_mt #license-mit #model-index #endpoints_compatible #region-us 
# stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-MetaKD-v1 (Legal BERTimbau)
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.
stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 derives from stjiris/bert-large-portuguese-cased-legal-tsdae (legal variant of BERTimbau large).

It was trained using the TSDAE technique with a learning rate 1e-5 Legal Sentences from +-30000 documents 212k training steps (best performance for our semantic search system implementation)
It was presented to Generative Pseudo Labeling training.

The model was presented to NLI data. 16 batch size,  2e-5 lr

It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the assin, assin2, stsb_multi_mt pt  datasets. 'lr': 1e-5

This model was subjected to Metadata Knowledge Distillation. Repository
Trial Technique to improve information retrieval through dense vectors: Metadata Knowledge Distillation## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this:## Usage (HuggingFace Transformers)## Full Model Architecture## Citing & Authors### Contributions
@rufimelo99

If you use this work, please cite: