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Github: datasets/csl.py

ir_datasets: CSL

Index
  1. csl
  2. csl/trec-2023

"csl"

The CSL dataset, used for the TREC NueCLIR technical document task.

docs
396K docs

Language: zh

Document type:
CslDoc: (namedtuple)
  1. doc_id: str
  2. title: str
  3. abstract: str
  4. keywords: List[str]
  5. category: str
  6. category_eng: str
  7. discipline: str
  8. discipline_eng: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("csl")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, abstract, keywords, category, category_eng, discipline, discipline_eng>

You can find more details about the Python API here.

CLI 
ir_datasets export csl docs
[doc_id]    [title]    [abstract]    [keywords]    [category]    [category_eng]    [discipline]    [discipline_eng]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

XPM-IR 
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.csl')
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

Metadata 
{
  "docs": {
    "count": 395927,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": "csl-"
      }
    }
  }
}

"csl/trec-2023"

The TREC NeuCLIR 2023 technical documen task.

queries
41 queries

Language: multiple/other/unknown

Query type:
ExctractedCCNoReportQuery: (namedtuple)
  1. query_id: str
  2. title: str
  3. description: str
  4. narrative: str
  5. ht_title: str
  6. ht_description: str
  7. ht_narrative: str
  8. mt_title: str
  9. mt_description: str
  10. mt_narrative: str
  11. translation_lang: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("csl/trec-2023")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, description, narrative, ht_title, ht_description, ht_narrative, mt_title, mt_description, mt_narrative, translation_lang>

You can find more details about the Python API here.

CLI 
ir_datasets export csl/trec-2023 queries
[query_id]    [title]    [description]    [narrative]    [ht_title]    [ht_description]    [ht_narrative]    [mt_title]    [mt_description]    [mt_narrative]    [translation_lang]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

XPM-IR 
from datamaestro import prepare_dataset
topics = prepare_dataset('irds.csl.trec-2023.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
396K docs

Inherits docs from csl

Language: zh

Document type:
CslDoc: (namedtuple)
  1. doc_id: str
  2. title: str
  3. abstract: str
  4. keywords: List[str]
  5. category: str
  6. category_eng: str
  7. discipline: str
  8. discipline_eng: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("csl/trec-2023")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, abstract, keywords, category, category_eng, discipline, discipline_eng>

You can find more details about the Python API here.

CLI 
ir_datasets export csl/trec-2023 docs
[doc_id]    [title]    [abstract]    [keywords]    [category]    [category_eng]    [discipline]    [discipline_eng]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

XPM-IR 
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.csl.trec-2023')
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
11K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Not-valuable. Information in the document might be included in a report footnote, or omitted entirely.11K94.3%
1Somewhat-valuable. The most valuable information in the document would be found in the remainder of such a report.419 3.7%
3Very-valuable. Information in the document would be found in the lead paragraph of a report that is later written on the topic.228 2.0%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("csl/trec-2023")
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 csl/trec-2023 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

XPM-IR 
from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.csl.trec-2023.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.

Metadata 
{
  "docs": {
    "count": 395927,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": "csl-"
      }
    }
  },
  "queries": {
    "count": 41
  },
  "qrels": {
    "count": 11291,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 10644,
          "1": 419,
          "3": 228
        }
      }
    }
  }
}