Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:22604
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use rnbokade/custom-bge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rnbokade/custom-bge with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rnbokade/custom-bge") sentences = [ "23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab", "mat-3783s5 : 3783 Seq 5 - Material Order", "21-1313-2.0 : Layout Drawings", "26-0500-1.0a : Breakers (2P 20A)" ] 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" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |