Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:309
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use thelocalhost/fpml-semantic-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thelocalhost/fpml-semantic-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thelocalhost/fpml-semantic-model") sentences = [ "Find the element that handles identifies the underlying asset when it is an exchange-traded fund.", "[exchangeTradedFund]: Identifies the underlying asset when it is an exchange-traded fund.", "[mutualFund]: Identifies the class of unit issued by a fund.", "[lcIssuanceFeePayment]: No description available" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6b07eba5e553e107d6234079054cc4843484fd91b232470de28350e6a473066
- Size of remote file:
- 90.9 MB
- SHA256:
- f42ed0bb7817c6021108011d72a87240873c341406db5d3a6b726e243293e47f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.