Feature Extraction
sentence-transformers
Safetensors
Model2Vec
English
Portuguese
mteb
embeddings
static-embeddings
Eval Results (legacy)
Instructions to use cnmoro/static-nomic-384-pten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cnmoro/static-nomic-384-pten with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/static-nomic-384-pten") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Model2Vec
How to use cnmoro/static-nomic-384-pten with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("cnmoro/static-nomic-384-pten") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download config.json from cnmoro/static-nomic-384-pten: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/cnmoro/static-nomic-384-pten/resolve/main/config.json
- Command line
-
hf download hf://cnmoro/static-nomic-384-pten/config.json
-
curl -L -o config.json https://huggingface.co/cnmoro/static-nomic-384-pten/resolve/main/config.json
97 Bytes
| { | |
| "normalize": true, | |
| "embedding_dtype": "float32", | |
| "vocabulary_quantization": 32000 | |
| } |