Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9035
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use fajayi/nomi-name-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fajayi/nomi-name-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fajayi/nomi-name-encoder") sentences = [ "Igbo name meaning 'Counsel is good'", "Izukamma", "Adematii", "Onaolapo" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from fajayi/nomi-name-encoder: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/fajayi/nomi-name-encoder/resolve/main/modules.json
- Command line
-
hf download hf://fajayi/nomi-name-encoder/modules.json
-
curl -L -o modules.json https://huggingface.co/fajayi/nomi-name-encoder/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |