---
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
---

APPS is a benchmark for code generation with 10000 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications. To create the APPS dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Codewars, AtCoder, Kattis, and Codeforces.

**Usage**
```
import datasets

# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/APPS", "queries")
documents = datasets.load_dataset("embedding-benchmark/APPS", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/APPS", "default")
```