Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:944
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use azizdh00/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use azizdh00/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("azizdh00/MNLP_M3_document_encoder") sentences = [ "A hash function $h$ is collision-resistant if\\dots", "6471::[9216:9728]", "13251::[1536:2048]", "5817::[2688:3200]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e791735c3c090fa7062ad5a357d7f39c36e4e151211a668419723d60f8ef3e36
- Size of remote file:
- 2.27 GB
- SHA256:
- 7b3da5db74e6a83d7f0c1d4644b8fc674422eb243c49c6d3058b3ef70fe21b3e
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