This model was trained with Neural-Cherche. You can find details on how to fine-tune it in the Neural-Cherche repository.
pip install neural-cherche
Retriever
from neural_cherche import models, retrieve
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 32
documents = [
{"id": 0, "document": "Food"},
{"id": 1, "document": "Sports"},
{"id": 2, "document": "Cinema"},
]
queries = ["Food", "Sports", "Cinema"]
model = models.SparseEmbed(
model_name_or_path="raphaelsty/neural-cherche-sparse-embed",
device=device,
)
retriever = retrieve.SparseEmbed(
key="id",
on=["document"],
model=model,
)
documents_embeddings = retriever.encode_documents(
documents=documents,
batch_size=batch_size,
)
retriever = retriever.add(
documents_embeddings=documents_embeddings,
)
queries_embeddings = retriever.encode_queries(
queries=queries,
batch_size=batch_size,
)
scores = retriever(
queries_embeddings=queries_embeddings,
batch_size=batch_size,
k=100,
)
scores
- Downloads last month
- 673
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.