dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: ingredients
dtype: string
- name: directions
dtype: string
- name: link
dtype: string
- name: source
dtype: string
- name: NER
sequence: string
- name: metadata
struct:
- name: NER
sequence: string
- name: title
dtype: string
- name: document
dtype: string
- name: all-MiniLM-L6-v2
sequence: float32
- name: bm42-all-minilm-l6-v2-attentions
struct:
- name: indices
sequence: int64
- name: values
sequence: float64
splits:
- name: train
num_bytes: 1176543723
num_examples: 350000
download_size: 1101274243
dataset_size: 1176543723
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Recipe Short - Dense and Sparse Embeddings Dataset
This dataset is based on the rk404/recipe_short dataset, which itself is derived from the RecipeNLG dataset. RecipeNLG is a large-scale, high-quality dataset designed for natural language generation tasks in the culinary domain. This dataset includes dense and sparse embeddings for each recipe, generated using the following models:
- Dense Embeddings: Created using the
sentence-transformers/all-MiniLM-L6-v2model withfastembedlibrary. - Sparse Embeddings: Generated using the
Qdrant/bm25-all-minilm-l6-v2-attentionsmodel withfastembedlibrary.
The embeddings were computed using GPU resources on Kaggle for efficient processing. This dataset is intended for tasks related to text similarity, search, and semantic information retrieval within recipe-related content.
Sparse Embedding Model Reference
Sparse vector embedding model focuses on capturing the most important tokens from the text. It provides attention-based scores to highlight key terms, which can be beneficial for keyword-based search and sparse retrieval tasks.
You can find more about sparse embedding here and here