Feature Extraction
sentence-transformers
Safetensors
Transformers
Portuguese
bert
sentence-similarity
text-embeddings-inference
Instructions to use pucpr-br/sbertimbau_news_2019 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use pucpr-br/sbertimbau_news_2019 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pucpr-br/sbertimbau_news_2019") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use pucpr-br/sbertimbau_news_2019 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pucpr-br/sbertimbau_news_2019")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr-br/sbertimbau_news_2019") model = AutoModel.from_pretrained("pucpr-br/sbertimbau_news_2019") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 166b739f7acf57ef438d3048bc9e99a471e22e2af9f58af77598d9b0a314cb96
- Size of remote file:
- 436 MB
- SHA256:
- af0b47960eb7914bad19a8604ad6aab5cbb13fceadd9f4a817d0e140d14c67d1
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