metadata

dataset_info:
  features:
    - name: _id
      dtype: string
    - name: title
      dtype: string
    - name: text
      dtype: string
    - name: openai
      sequence: float32
    - name: splade
      sequence: float32
  splits:
    - name: train
      num_bytes: 12862697823
      num_examples: 100000
  download_size: 901410913
  dataset_size: 12862697823
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: apache-2.0
task_categories:
  - feature-extraction
language:
  - en
pretty_name: 'DBPedia SPLADE + OpenAI: 100,000 Vectors'
size_categories:
  - 100K<n<1M

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