README.md

---
language: 
  - pt
thumbnail: "Portugues SBERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
  sentences:
    - "O juíz leu as provas."
    - "O juíz leu o recurso."
    - "O juíz atirou uma pedra."
  example_title: "Example 1"
metrics:
  - bleu
---
# rufimelo/Legal-SBERTimbau-sts-large
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.
rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
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](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('rufimelo/Legal-SBERTimbau-sts-large')
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('rufimelo/Legal-SBERTimbau-sts-large')
model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-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| Dataset |  PearsonCorrelation       |
| ---------------------------------------- | ---------- | ---------- |
| Legal-SBERTimbau-sts-large| Assin | 0.76629 |
| Legal-SBERTimbau-sts-large| Assin2| 0.82357 |
| Legal-SBERTimbau-sts-base| Assin | 0.71457 |
| Legal-SBERTimbau-sts-base| Assin2| 0.73545|
| Legal-SBERTimbau-sts-large-v2| Assin | 0.76299 |
| Legal-SBERTimbau-sts-large-v2| Assin2| 0.81121 |
| Legal-SBERTimbau-sts-large-v2| stsb_multi_mt pt| 0.81726 |
| ---------------------------------------- | ---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| Assin | 0.71457|
| paraphrase-multilingual-mpnet-base-v2| Assin2| 0.79831 |
| 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
rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-largewhich derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin) and [assin2](https://huggingface.co/datasets/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
If you use this work, please cite BERTimbau's work:
```bibtex
@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}
}
```