Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Size:
1K - 10K
Tags:
text-retrieval
File size: 1,461 Bytes
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---
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
---
DS-1000 is a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. It employs multi-criteria evaluation metrics, including functional correctness and surface-form constraints, resulting in a high-quality dataset with only 1.8% incorrect solutions among accepted Codex-002 predictions.
**Usage**
```
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/DS1000", "queries")
documents = datasets.load_dataset("embedding-benchmark/DS1000", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/DS1000", "default")
``` |