|
--- |
|
dataset_info: |
|
features: |
|
- name: doc_id |
|
dtype: string |
|
- name: file_name |
|
dtype: string |
|
- name: key |
|
dtype: string |
|
- name: value |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
splits: |
|
- name: validation |
|
num_bytes: 4651754 |
|
num_examples: 1111 |
|
download_size: 1824942 |
|
dataset_size: 4651754 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: validation |
|
path: data/validation-* |
|
task_categories: |
|
- question-answering |
|
- feature-extraction |
|
--- |
|
|
|
This dataset is adapted from the paper [Language Models Enable Simple Systems for Generating |
|
Structured Views of Heterogeneous Data Lakes](https://www.vldb.org/pvldb/vol17/p92-arora.pdf). You can learn more about the data collection process there. |
|
|
|
|
|
Please consider citing the following if you use this task in your work: |
|
``` |
|
@article{arora2024simple, |
|
title={Simple linear attention language models balance the recall-throughput tradeoff}, |
|
author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas and Zinsley, Dylan and Zou, James and Rudra, Atri and Ré, Christopher}, |
|
journal={arXiv:2402.18668}, |
|
year={2024} |
|
} |
|
``` |
|
|
