metadata
language:
- en
license: mit
This model was trained with Sparsembed. You can find details on how to use it in the Sparsembed repository.
pip install sparsembed
from sparsembed import model, retrieve
from transformers import AutoModelForMaskedLM, AutoTokenizer
device = "cuda" # cpu
batch_size = 10
# List documents to index:
documents = [
{'id': 0,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'text': 'Paris is the capital and most populous city of France.'},
{'id': 1,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'text': "Since the 17th century, Paris has been one of Europe's major centres of science, and arts."},
{'id': 2,
'title': 'Paris',
'url': 'https://en.wikipedia.org/wiki/Paris',
'text': 'The City of Paris is the centre and seat of government of the region and province of Île-de-France.'
}]
model = model.SparsEmbed(
model=AutoModelForMaskedLM.from_pretrained("raphaelsty/sparsembed-max").to(device),
tokenizer=AutoTokenizer.from_pretrained("raphaelsty/sparsembed-max"),
device=device
)
retriever = retrieve.SpladeRetriever(
key="id", # Key identifier of each document.
on=["title", "text"], # Fields to search.
model=model # Splade retriever.
)
retriever = retriever.add(
documents=documents,
batch_size=batch_size,
k_tokens=256, # Number of activated tokens.
)
retriever(
["paris", "Toulouse"], # Queries
k_tokens=20, # Maximum number of activated tokens.
k=100, # Number of documents to retrieve.
batch_size=batch_size
)