Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Size:
1K - 10K
Tags:
text-retrieval
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Download README.md from embedding-benchmark/WikiSQL_mteb: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/datasets/embedding-benchmark/WikiSQL_mteb/resolve/main/README.md
- Command line
-
hf download hf://datasets/embedding-benchmark/WikiSQL_mteb/README.md
-
curl -L -o README.md https://huggingface.co/datasets/embedding-benchmark/WikiSQL_mteb/resolve/main/README.md
1.26 kB
metadata
task_categories:
- text-retrieval
task_ids:
- document-retrieval
config_names:
- corpus
tags:
- text-retrieval
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
- config_name: corpus
features:
- name: id
dtype: string
- name: text
dtype: string
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
configs:
- config_name: default
data_files:
- split: test
path: relevance.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
WikiSQL is a dataset comprising 80,654 hand-annotated examples of natural language questions and corresponding SQL queries across 24,241 tables from Wikipedia.
Usage
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/MBPP", "queries")
documents = datasets.load_dataset("embedding-benchmark/MBPP", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/MBPP", "default")