Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1432
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use ElijahDevPH/bge-fast-epoch-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ElijahDevPH/bge-fast-epoch-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ElijahDevPH/bge-fast-epoch-2") sentences = [ "Represent this job requirement for retrieving matching candidate resume sections: Forklift or heavy equipment awareness", "Queue Management", "Employee Support", "Tool and Material Controller: Managed inventory tracking, recording, and stock levels for tools and materials supporting production. Coordinated with suppliers and purchasing" ] 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" | |
| } | |
| ] |