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<dbpedia:Parlour_music>
Parlour music
Parlour music is a type of popular music which, as the name suggests, is intended to be performed in the parlours of middle-class homes by amateur singers and pianists. Disseminated as sheet music, its heyday came in the 19th century, as a result of a steady increase in the number of households with enough surplus cash...
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<dbpedia:Third-party_administrator>
Third-party administrator
"A third-party administrator (TPA) is an organization that processes insurance claims or certain as(...TRUNCATED)
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<dbpedia:Indigenous_land_rights>
Indigenous land rights
"Indigenous land rights are the rights of indigenous peoples to land, either individually or collect(...TRUNCATED)
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<dbpedia:Agagite>
Agagite
"The term Agagite is used in the Book of Esther as a description of Haman. The term is understood t(...TRUNCATED)
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<dbpedia:Vanoise_National_Park>
Vanoise National Park
"Vanoise National Park (French: Parc national de la Vanoise), is a French national park between the (...TRUNCATED)
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<dbpedia:Sushruta_Samhita>
Sushruta Samhita
"The Suśrutasamhitā is an important Sanskrit text on medicine, considered to be one of the earlies(...TRUNCATED)
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<dbpedia:Gumla_block>
Gumla block
"Gumla block, one of the eleven administrative blocks of Gumla district, Jharkhand state, India, has(...TRUNCATED)
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<dbpedia:Leverett_Saltonstall>
Leverett Saltonstall
"Leverett A. Saltonstall (September 1, 1892 – June 17, 1979) was an American Republican politician(...TRUNCATED)
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<dbpedia:Trevignano>
Trevignano
Trevignano is a town and comune in the province of Treviso, Veneto, Italy.
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<dbpedia:Aleksandrovsky_Sad_(Moscow_Metro)>
Aleksandrovsky Sad (Moscow Metro)
"Aleksandrovsky Sad (Russian: Алекса́ндровский сад, IPA: [ɐlʲɪˈksandrəfskʲ(...TRUNCATED)
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DBPedia SPLADE + OpenAI: 100,000 SPLADE Sparse Vectors + OpenAI Embedding

This dataset has both OpenAI and SPLADE vectors for 100,000 DBPedia entries. This adds SPLADE Vectors to KShivendu/dbpedia-entities-openai-1M/

Model id used to make these vectors:

model_id = "naver/efficient-splade-VI-BT-large-doc"

For processing the query, use this:

model_id = "naver/efficient-splade-VI-BT-large-query"

If you'd like to extract the indices and weights/values from the vectors, you can do so using the following snippet:

import numpy as np
vec = np.array(ds[0]['vec']) # where ds is the dataset

def get_indices_values(vec):
  sparse_indices = vec.nonzero()
  sparse_values = vec[sparse_indices]
  return sparse_indices, sparse_values
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