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---
language:
- en
dataset_info:
config_name: continuation
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 11897245
num_examples: 14436
- name: test
num_bytes: 1383452
num_examples: 1906
download_size: 7051329
dataset_size: 13280697
configs:
- config_name: continuation
data_files:
- split: train
path: continuation/train-*
- split: test
path: continuation/test-*
---
# Dataset Card for aeslc
<!-- Provide a quick summary of the dataset. -->
This is a preprocessed version of aeslc dataset for benchmarks in LM-Polygraph.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** https://huggingface.co/LM-Polygraph
- **License:** https://github.com/IINemo/lm-polygraph/blob/main/LICENSE.md
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/IINemo/lm-polygraph
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
This dataset should be used for performing benchmarks on LM-polygraph.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset should not be used for further dataset preprocessing.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
This dataset contains the "continuation" subset, which corresponds to main dataset, used in LM-Polygraph. It may also contain other subsets, which correspond to instruct methods, used in LM-Polygraph.
Each subset contains two splits: train and test. Each split contains two string columns: "input", which corresponds to processed input for LM-Polygraph, and "output", which corresponds to processed output for LM-Polygraph.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
This dataset is created in order to separate dataset creation code from benchmarking code.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
Data is collected from https://huggingface.co/datasets/aeslc and processed by using build_dataset.py script in repository.
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
People who created https://huggingface.co/datasets/aeslc
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This dataset contains the same biases, risks, and limitations as its source dataset https://huggingface.co/datasets/aeslc
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset.
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