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_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M2_document_encoder") sentences = [ "His anger reached a boiling point, the therapist said they should take a break and what?", "chicken coop", "cool off", "bark" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8b4f0ac0595c35995022681c12632e3cab50104a153d85705a3844722fb64e3
- Size of remote file:
- 2.38 GB
- SHA256:
- e8c61d1b0b6d71f371eb4ea6bb1d637fc1f29f1b8cb04662b0aff9ad00c77ddb
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