Legal BERTimbau sts large EU AI Act Compliance Profile

language:

• pt thumbnail: "Portugues BERT for the Legal Domain" pipeline_tag: sentence-similarity tags:
• sentence-transformers
• sentence-similarity
• transformers datasets:
• assin
• assin2
• rufimelo/PortugueseLegalSentences-v0 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" model-index:
• name: BERTimbau results:
  • task: name: STS type: STS metrics:
    • name: Pearson Correlation - assin Dataset type: Pearson Correlation value: 0.76629
    • name: Pearson Correlation - assin2 Dataset type: Pearson Correlation value: 0.82357
    • name: Pearson Correlation - stsb_multi_mt pt Dataset type: Pearson Correlation value: 0.79120

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:

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 accoun

Your obligations as a Legal BERTimbau sts large deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library