Sentence Similarity
sentence-transformers
Safetensors
Hungarian
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:200000
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use karsar/bge-m3-hu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use karsar/bge-m3-hu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("karsar/bge-m3-hu") sentences = [ "Emberek várnak a lámpánál kerékpárral.", "Az emberek piros lámpánál haladnak.", "Az emberek a kerékpárjukon vannak.", "Egy fekete kutya úszik a vízben egy teniszlabdával a szájában" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d747ca97fe6e145aef974d51b6921c06a4eed57a0513eb2427235a9af134752
- Size of remote file:
- 17.1 MB
- SHA256:
- e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
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