Sentence Similarity
sentence-transformers
Safetensors
t5
feature-extraction
Generated from Trainer
dataset_size:3772
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
custom_code
Eval Results (legacy)
Instructions to use Bharatdeep-H/pq_cache_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bharatdeep-H/pq_cache_8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bharatdeep-H/pq_cache_8", trust_remote_code=True) sentences = [ "do I possess any funds that are not performing", "How did my portfolio perform this month?", "Show me my best performing holdings", "do I hold any funds that haven't been performing well" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |