nilc-nlp/assin2
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How to use melll-uff/pt-br_simcse with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("melll-uff/pt-br_simcse")
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]How to use melll-uff/pt-br_simcse with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("melll-uff/pt-br_simcse")
model = AutoModel.from_pretrained("melll-uff/pt-br_simcse")This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.