Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:828
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use adriansanz/sqv-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adriansanz/sqv-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adriansanz/sqv-v3") sentences = [ "Comunicació prèvia per l'execució de cales, pous i sondejos, en terreny privat, previs a l'actuació definitiva.", "Quin és el requisit per a l'execució de les obres en terreny privat?", "Quin és el propòsit del tràmit de rectificació de dades personals?", "Quin és el requisit per a la crema en zones de conservació?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from adriansanz/sqv-v3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/adriansanz/sqv-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://adriansanz/sqv-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/adriansanz/sqv-v3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 73a563baf68d51e5cf2e28fc8a7d5887633303c074a7029c1fdcd02a0c8ef954
- Size of remote file:
- 17.1 MB
- SHA256:
- e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
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