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Github: datasets/vaswani.py

ir_datasets: Vaswani

Index
  1. vaswani

"vaswani"

A small corpus of roughly 11,000 scientific abstracts.

queries
93 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("vaswani")
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 vaswani queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:vaswani')
index_ref = pt.IndexRef.of('./indices/vaswani') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

XPM-IR 
from datamaestro import prepare_dataset
topics = prepare_dataset('irds.vaswani.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
11K docs

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("vaswani")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI 
ir_datasets export vaswani docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:vaswani')
# Index vaswani
indexer = pt.IterDictIndexer('./indices/vaswani')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

XPM-IR 
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.vaswani')
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
2.1K 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%
1Relevant2.1K100.0%

Examples:

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

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:vaswani')
index_ref = pt.IndexRef.of('./indices/vaswani') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

XPM-IR 
from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.vaswani.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": 11429,
    "fields": {
      "doc_id": {
        "max_len": 5,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 93
  },
  "qrels": {
    "count": 2083,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "1": 2083
        }
      }
    }
  }
}