ir_datasets : CodeSearchNet

import ir_datasets
dataset = ir_datasets.load("codesearchnet")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, repo, path, func_name, code, language>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet docs



[doc_id]    [repo]    [path]    [func_name]    [code]    [language]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.codesearchnet')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

\cite{Husain2019CodeSearchNet}

Bibtex:

@article{Husain2019CodeSearchNet, title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search}, author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt}, journal={ArXiv}, year={2019} }

{
  "docs": {
    "count": 2070536,
    "fields": {
      "doc_id": {
        "max_len": 339,
        "common_prefix": "https://github.com/"
      }
    }
  }
}

`"codesearchnet/challenge"`

Official challenge set, with keyword queries and deep relevance assessments.

99 queries

Language: multiple/other/unknown

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/challenge")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/challenge queries



[query_id]    [text]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.codesearchnet.challenge.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

2.1M docs

Inherits docs from codesearchnet

Language: multiple/other/unknown

Document type:

CodeSearchNetDoc: (namedtuple)

doc_id: str
repo: str
path: str
func_name: str
code: str
language: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/challenge")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, repo, path, func_name, code, language>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/challenge docs



[doc_id]    [repo]    [path]    [func_name]    [code]    [language]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.codesearchnet.challenge')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

4.0K qrels

Query relevance judgment type:

CodeSearchNetChallengeQrel: (namedtuple)

query_id: str
doc_id: str
relevance: str
note: str

Relevance levels

Rel.	Definition	Count	%
0	Irrelevant	`1.3K`	32.8%
1	Weak Match	`982`	24.5%
2	String Match	`863`	21.5%
3	Exact Match	`847`	21.1%

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/challenge")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, note>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/challenge qrels --format tsv



[query_id]    [doc_id]    [relevance]    [note]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.codesearchnet.challenge.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Husain2019CodeSearchNet}

Bibtex:

{
  "docs": {
    "count": 2070536,
    "fields": {
      "doc_id": {
        "max_len": 339,
        "common_prefix": "https://github.com/"
      }
    }
  },
  "queries": {
    "count": 99
  },
  "qrels": {
    "count": 4006,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 1314,
          "1": 982,
          "2": 863,
          "3": 847
        }
      }
    }
  }
}

`"codesearchnet/test"`

Official test set, using queries inferred from docstrings.

101K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/test")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/test queries



[query_id]    [text]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.codesearchnet.test.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

2.1M docs

Inherits docs from codesearchnet

Language: multiple/other/unknown

Document type:

CodeSearchNetDoc: (namedtuple)

doc_id: str
repo: str
path: str
func_name: str
code: str
language: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/test")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, repo, path, func_name, code, language>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/test docs



[doc_id]    [repo]    [path]    [func_name]    [code]    [language]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.codesearchnet.test')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

101K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
1	Matches docstring	`101K`	100.0%

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/test")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/test qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.codesearchnet.test.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Husain2019CodeSearchNet}

Bibtex:

{
  "docs": {
    "count": 2070536,
    "fields": {
      "doc_id": {
        "max_len": 339,
        "common_prefix": "https://github.com/"
      }
    }
  },
  "queries": {
    "count": 100529
  },
  "qrels": {
    "count": 100529,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "1": 100529
        }
      }
    }
  }
}

`"codesearchnet/train"`

Official train set, using queries inferred from docstrings.

1.9M queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/train")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/train queries



[query_id]    [text]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.codesearchnet.train.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

2.1M docs

Inherits docs from codesearchnet

Language: multiple/other/unknown

Document type:

CodeSearchNetDoc: (namedtuple)

doc_id: str
repo: str
path: str
func_name: str
code: str
language: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/train")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, repo, path, func_name, code, language>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/train docs



[doc_id]    [repo]    [path]    [func_name]    [code]    [language]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.codesearchnet.train')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

1.9M qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
1	Matches docstring	`1.9M`	100.0%

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/train")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/train qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.codesearchnet.train.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Husain2019CodeSearchNet}

Bibtex:

{
  "docs": {
    "count": 2070536,
    "fields": {
      "doc_id": {
        "max_len": 339,
        "common_prefix": "https://github.com/"
      }
    }
  },
  "queries": {
    "count": 1880853
  },
  "qrels": {
    "count": 1880853,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "1": 1880853
        }
      }
    }
  }
}

`"codesearchnet/valid"`

Official validation set, using queries inferred from docstrings.

89K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/valid")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/valid queries



[query_id]    [text]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.codesearchnet.valid.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

2.1M docs

Inherits docs from codesearchnet

Language: multiple/other/unknown

Document type:

CodeSearchNetDoc: (namedtuple)

doc_id: str
repo: str
path: str
func_name: str
code: str
language: str

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/valid")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, repo, path, func_name, code, language>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/valid docs



[doc_id]    [repo]    [path]    [func_name]    [code]    [language]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.codesearchnet.valid')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

89K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
1	Matches docstring	`89K`	100.0%

Examples:

import ir_datasets
dataset = ir_datasets.load("codesearchnet/valid")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export codesearchnet/valid qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

No example available for PyTerrier

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.codesearchnet.valid.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Husain2019CodeSearchNet}

Bibtex: