metadata

annotations_creators: []
language_creators:
  - crowdsourced
  - expert-generated
language:
  - code
license: apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets: []
task_categories:
  - text-generation
task_ids:
  - language-modeling
paperswithcode_id: taco-topics-in-algorithmic-code-generation
pretty_name: TACO
tags:
  - code
dataset_info:
  config_name: ALL
  features:
    - name: question
      dtype: string
    - name: solutions
      dtype: string
    - name: starter_code
      dtype: string
    - name: input_output
      dtype: string
    - name: difficulty
      dtype: string
    - name: raw_tags
      dtype: string
    - name: name
      dtype: string
    - name: source
      dtype: string
    - name: tags
      dtype: string
    - name: skill_types
      dtype: string
    - name: url
      dtype: string
    - name: Expected Auxiliary Space
      dtype: string
    - name: time_limit
      dtype: string
    - name: date
      dtype: string
    - name: picture_num
      dtype: string
    - name: memory_limit
      dtype: string
    - name: Expected Time Complexity
      dtype: string
  splits:
    - name: train
      num_bytes: 4239311973
      num_examples: 25443
    - name: test
      num_bytes: 481480755
      num_examples: 1000
  download_size: 2419844942
  dataset_size: 4720792728
configs:
  - config_name: ALL
    data_files:
      - split: train
        path: ALL/train-*
      - split: test
        path: ALL/test-*

TACO Dataset

TACO is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications.

Key Update:

We remove and modified some test cases in test set. Please update to use the newest version.

Dataset Description

Repository: https://github.com/FlagOpen/TACO/
Paper: TACO: Topics in Algorithmic COde generation dataset
Leaderboard: Code Generation on CodeContests
Point of Contact: Bo-Wen Zhang

Languages

The dataset contains questions in English and code solutions in Python.

Dataset Structure

from datasets import load_dataset
load_dataset("BAAI/TACO")

DatasetDict({
    train: Dataset({
        features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'],
        num_rows: 25443
    })
    test: Dataset({
        features: ['question', 'solutions', 'starter_code', 'input_output', 'difficulty', 'raw_tags', 'name', 'source', 'tags', 'skill_types', 'url', 'Expected Auxiliary Space', 'time_limit', 'date', 'picture_num', 'memory_limit', 'Expected Time Complexity'],
        num_rows: 1000
    })
})

How to use it

You can load and iterate through the dataset with the following two lines of code for the train split:

from datasets import load_dataset
import json

ds = load_dataset("BAAI/TACO", split="train")
sample = next(iter(ds))
# non-empty solutions and input_output features can be parsed from text format this way:
sample["solutions"] = json.loads(sample["solutions"])
sample["input_output"] = json.loads(sample["input_output"])
sample["raw_tags"] = eval(sample["raw_tags"])
sample["tags"] = eval(sample["tags"])
sample["skill_types"] = eval(sample["skill_types"])
print(sample)

#OUTPUT:
{
  "question": "You have a deck of $n$ cards, and you'd like to reorder it to a new one.\n\nEach card has a value between $1$ and $n$ equal to $p_i$. ...",
  "solutions": [
    "import heapq\nfrom math import sqrt\nimport operator\nimport sys\ninf_var = 0\nif inf_var == 1:\n\tinf = open('input.txt', 'r')\nelse:\n\tinf = sys.stdin\n ...",
    "t = int(input())\nfor _ in range(t):\n\tn = int(input())\n\tp = list(map(int, input().split()))\n\tans = []\n\tp1 = [-1] * (n + 1)\n\tfor i in range(n):\n\t\tp1[p[i]] = i\n\ti = n\n\twhile i:\n\t\twhile i > 0 and p1[i] == -1:\n\t\t\ti -= 1\n\t\telse:\n\t\t\tif i:\n\t\t\t\tk = 0\n\t\t\t\tfor j in range(p1[i], n):\n\t\t\t\t\tans.append(p[j])\n\t\t\t\t\tp1[p[j]] = -1\n\t\t\t\t\tk += 1\n\t\t\t\tn -= k\n\t\t\t\ti -= 1\n\t\t\telse:\n\t\t\t\tbreak\n\tprint(*ans)\n",
    "import sys\n\ndef get_ints():\n\treturn map(int, sys.stdin.readline().strip().split())\n\ndef get_list():\n\treturn list(map(int, sys.stdin.readline().strip().split()))\n\ndef get_list_string():\n\treturn list(map(str, sys.stdin.readline().strip().split()))\n\ndef get_string():\n\treturn sys.stdin.readline().strip()\n\ndef get_int():\n\treturn int(sys.stdin.readline().strip())\n\ndef get_print_int(x):\n\tsys.stdout.write(str(x) + '\\n')\n\ndef get_print(x):\n\tsys.stdout.write(x + '\\n')\n\ndef get_print_int_same(x):\n\tsys.stdout.write(str(x) + ' ')\n\ndef get_print_same(x):\n\tsys.stdout.write(x + ' ')\nfrom sys import maxsize\n\ndef solve():\n\tfor _ in range(get_int()):\n\t\tn = get_int()\n\t\tarr = get_list()\n\t\ti = n - 1\n\t\tj = n - 1\n\t\ttemp = sorted(arr)\n\t\tvis = [False] * n\n\t\tans = []\n\t\twhile j >= 0:\n\t\t\tt = j\n\t\t\ttt = []\n\t\t\twhile t >= 0 and arr[t] != temp[i]:\n\t\t\t\tvis[arr[t] - 1] = True\n\t\t\t\ttt.append(arr[t])\n\t\t\t\tt -= 1\n\t\t\tvis[arr[t] - 1] = True\n\t\t\ttt.append(arr[t])\n\t\t\ttt = tt[::-1]\n\t\t\tfor k in tt:\n\t\t\t\tans.append(k)\n\t\t\tj = t - 1\n\t\t\twhile i >= 0 and vis[i]:\n\t\t\t\ti -= 1\n\t\tget_print(' '.join(map(str, ans)))\nsolve()\n",
    ...
  ],
  "starter_code": "",
  "input_output": {
    "inputs": [
      "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n",
      "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n",
      "4\n4\n2 1 3 4\n5\n1 5 2 4 3\n6\n2 4 5 3 6 1\n1\n1\n",
      "4\n4\n1 2 3 4\n5\n1 5 2 4 3\n6\n4 2 5 3 6 1\n1\n1\n"
    ],
    "outputs": [
      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
      "4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n",
      "\n4 3 2 1\n5 2 4 3 1\n6 1 5 3 4 2\n1\n"
    ]
  },
  "difficulty": "EASY",
  "raw_tags": [
    "data structures",
    "greedy",
    "math"
  ],
  "name": null,
  "source": "codeforces",
  "tags": [
    "Data structures",
    "Mathematics",
    "Greedy algorithms"
  ],
  "skill_types": [
    "Data structures",
    "Greedy algorithms"
  ],
  "url": "https://codeforces.com/problemset/problem/1492/B",
  "Expected Auxiliary Space": null,
  "time_limit": "1 second",
  "date": "2021-02-23",
  "picture_num": "0",
  "memory_limit": "512 megabytes",
  "Expected Time Complexity": null
}

Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level (difficulty), topics of task (raw tags), algorithms (tags) as well as required programming skill types (skill_types) of the problem and its source.

If a sample has non empty input_output feature, you can read it as a dictionary with keys inputs and outputs and fn_name if it exists, and similarily you can parse the solutions into a list of solutions as shown in the code above.

You can also filter the dataset for the difficulty level: EASY, MEDIUM, MEDIUM_HARD, HARD and VERY_HARD, or filter the programming skill types: Amortized analysis, Bit manipulation, Complete search, Data structures, Dynamic programming, Greedy algorithms, Range queries, Sorting. Just pass the list of difficulties or skills as a list. E.g. if you want the most challenging problems, you need to select the VERY_HARD level:

ds = load_dataset("BAAI/TACO", split="train", difficulties=["VERY_HARD"])
print(next(iter(ds))["question"])

#OUTPUT:
"""Let S(n) denote the number that represents the digits of n in sorted order. For example, S(1) = 1, S(5) = 5, S(50394) = 3459, S(353535) = 333555.
Given a number X, compute <image> modulo 109 + 7.

Input
The first line of input will contain the integer X (1 ≤ X ≤ 10700).

Output
Print a single integer, the answer to the question.

Examples

Input
21

Output
195

Input
345342

Output
390548434

Note

The first few values of S are 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 12. The sum of these values is 195.

Or if you want the problems invovled with Range queries and Sorting, you need to select the skills Range queries and Sorting:

ds = load_dataset("BAAI/TACO", split="train", skills=["Range queries", "Sorting"])

Data Fields

Field	Type	Description
question	string	problem description
solutions	string	some python solutions
input_output	string	Json string with "inputs" and "outputs" of the test cases, might also include "fn_name" the name of the function
difficulty	string	difficulty level of the problem
picture_num	string	the number of pictures in the problem
source	string	the source of the problem
url	string	url of the source of the problem
date	string	the date of the problem
starter_code	string	starter code to include in prompts
time_limit	string	the time consumption limit to solve the problem
memory_limit	string	the memory consumption limit to solve the problem
Expected Auxiliary Space	string	the extra auxiliary space expected to solve the problem
Expected Time Complexity	string	the time complexity expected to solve the problem
raw_tags	string	the topics of the programming task
tags	string	the manually annoatated algorithms needed to solve the problem
skill_types	string	the mapped programming skill types to solve the problem

Data Splits

The dataset contains a train with 25443 samples and test splits with 1000 samples.

Dataset Statistics

26443 coding problems
1.55M verified solutions
for tests split, the average number of test cases is 202.3
all files have ground-truth solutions in the test split

Dataset Creation

To create the TACO dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Aizu AtCoder, CodeChef, Codeforces, CodeWars, GeeksforGeeks, HackerEarth, HackerRank, Katti and LeetCode. For more details please refer to the original paper.

License

The TACO dataset that is authored by BAAI, Shandong Normal University and Peking University is released under an Apache 2.0 License. However, the data also includes content licensed under other permissive licenses such as MIT License, or web-crawled data which is used under the terms of the CC BY 4.0 license (Creative Commons Attribution 4.0 International license).

We gratefully acknowledge the contributions of the following:

some AtCoder, Codeforces, CodeWars, Kattis, LeetCode material curated from APPS dataset (https://github.com/hendrycks/apps)
some Aizu, AtCoder, CodeChef, Codeforces material curated from CodeContest dataset (https://github.com/google-deepmind/code_contests)
Codeforces materials are sourced from http://codeforces.com.
CodeChef materials are sourced from https://www.codechef.com.
GeekforGeeks materials are sourced from https://www.geeksforgeeks.org
HackerEarth materials are curated from: Description2Code Dataset, licensed under the MIT open source license, copyright not specified.
HackerRank materials are sourced from https://www.hackerrank.com. We don't know what the legal rights or data licenses of HackerRank. Please contact us if there is data license.

Citation Information

If you find our data, or code helpful, please cite the original paper:

@article{li2023taco,
  title={TACO: Topics in Algorithmic COde generation dataset},
  author={Rongao Li and Jie Fu and Bo-Wen Zhang and Tao Huang and Zhihong Sun and Chen Lyu and Guang Liu and Zhi Jin and Ge Li},
  journal={arXiv preprint arXiv:2312.14852},
  year={2023}
}