Sentence Similarity
Transformers
PyTorch
sentence-transformers
milco
feature-extraction
sparse
sparse-encoder
sparse-retrieval
multilingual
learned-sparse
cross-lingual
custom_code
Instructions to use omai-research/milco-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omai-research/milco-300m with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("omai-research/milco-300m", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use omai-research/milco-300m with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("omai-research/milco-300m", trust_remote_code=True) queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1 opened about 2 months ago
by
SFconvertbot