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
| { | |
| "__version__": { | |
| "sentence_transformers": "5.1.0", | |
| "transformers": "4.56.1", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |