Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:9741
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Matjac5/MNLP_M2_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M2_rag_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M2_rag_model") sentences = [ "What's one characteristic that separates a person from a stuffed dummy?", "accidental", "experience pain", "wrong place" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59732e0e703a430407d422c29a057a80583f8400ee4c314d1f2492331ca4fac3
- Size of remote file:
- 11.4 MB
- SHA256:
- 2c9573ae979ec2d2616f50161510156609a81f0842bbc4e8d1f161995c5cd8f4
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