Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:360
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use FAWAS97/bge-base-financial-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FAWAS97/bge-base-financial-matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FAWAS97/bge-base-financial-matryoshka") sentences = [ "Details for hostname <hostname> please", "Tell me about the entity/device <hostname>", "I need the MAC address for <ip>", "Tell me MAC address for IP <ip>" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": true | |
| } |