Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Portuguese
bert
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use rufimelo/Legal-BERTimbau-sts-base-ma-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rufimelo/Legal-BERTimbau-sts-base-ma-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rufimelo/Legal-BERTimbau-sts-base-ma-v2") sentences = [ "O advogado apresentou as provas ao juíz.", "O juíz leu as provas.", "O juíz leu o recurso.", "O juíz atirou uma pedra." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use rufimelo/Legal-BERTimbau-sts-base-ma-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-sts-base-ma-v2") model = AutoModel.from_pretrained("rufimelo/Legal-BERTimbau-sts-base-ma-v2") - Notebooks
- Google Colab
- Kaggle
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