Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
Generated from Trainer
dataset_size:268861
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Matjac5/MNLP_M3_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M3_rag_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/MNLP_M3_rag_model") sentences = [ "how many seconds will a 450 m long train take to cross a man walking with a speed of 3 km / hr in the direction of the moving train if the speed of the train is 63 km / hr ?", "'", "[", "2" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b63bc526f098f0fd5fcba058354704bd76f886869da125e6565543d10de7aed
- Size of remote file:
- 2.38 GB
- SHA256:
- fdeae6d28d0a1704d66cdb36938add1c6ef97c23633f5a1e29a8bcf009486f9f
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