Datasets:
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---
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
``` |