← home
Github: datasets/sara.py

ir_datasets: SARA

Index
  1. sara

"sara"

A set of sensitivity-aware relevance assessments. More information is avaliable here:

queries
150 queries

Language: en

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

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("sara")
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 sara 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:sara')
index_ref = pt.IndexRef.of('./indices/sara') # 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.sara.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
1.7K docs

Language: en

Document type:
SaraDoc: (namedtuple)
  1. doc_id: str
  2. text: str
  3. sensitivity: int

Examples:

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

You can find more details about the Python API here.

CLI 
ir_datasets export sara docs
[doc_id]    [text]    [sensitivity]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:sara')
# Index sara
indexer = pt.IterDictIndexer('./indices/sara')
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.sara')
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
34K 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 relevant33K95.0%
1partially relevant999 2.9%
2highly relevant708 2.1%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("sara")
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 sara 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:sara')
index_ref = pt.IndexRef.of('./indices/sara') # 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.sara.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": 1702,
    "fields": {
      "doc_id": {
        "max_len": 6,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 150
  },
  "qrels": {
    "count": 34413,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 32706,
          "2": 708,
          "1": 999
        }
      }
    }
  }
}