---
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/](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M)

Model id used to make these vectors: 
```python
model_id = "naver/efficient-splade-VI-BT-large-doc"
```

For processing the query, use this: 
```python
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:

```python
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
```