Sentence Similarity
sentence-transformers
Safetensors
t5
feature-extraction
Generated from Trainer
dataset_size:11442
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
loss:ContrastiveLoss
custom_code
Eval Results (legacy)
Instructions to use 1shoomun/pq_cache_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 1shoomun/pq_cache_9 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("1shoomun/pq_cache_9", trust_remote_code=True) sentences = [ "What are the underperforming funds in my portfolio?", "Switch my stock portfolio with mutual funds", "List me cheapest funds", "Which of my funds aren't doing well?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26b2579f7220cc6b5bc66b2f16d4a96263f6d90b0b170ec247a67651a289b97a
- Size of remote file:
- 5.97 kB
- SHA256:
- 63a02545aab1c3b3021d8478ed62e0b67339f57afee9289edd678727d7639c02
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