ir_datasets
: TREC Fair RankingThe TREC Fair Ranking track evaluates systems according to how well they fairly rank documents.
The TREC Fair Ranking track evaluates systems according to how well they fairly rank documents.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("trec-fair/2021")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, text, marked_up_text, url, quality_score, geographic_locations, quality_score_disk>
You can find more details about the Python API here.
Official TREC Fair Ranking 2021 evaluation set.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("trec-fair/2021/eval")
for query in dataset.queries_iter():
query # namedtuple<query_id, text, keywords, scope>
You can find more details about the Python API here.
Official TREC Fair Ranking 2021 train set.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("trec-fair/2021/train")
for query in dataset.queries_iter():
query # namedtuple<query_id, text, keywords, scope, homepage>
You can find more details about the Python API here.
The TREC Fair Ranking 2022 track focuses on fairly prioritising Wikimedia articles for editing to provide a fair exposure to articles from different groups.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("trec-fair/2022")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, text, url, pred_qual, qual_cat, page_countries, page_subcont_regions, source_countries, source_subcont_regions, gender, occupations, years, num_sitelinks, relative_pageviews, first_letter, creation_date, first_letter_category, gender_category, creation_date_category, years_category, relative_pageviews_category, num_sitelinks_category>
You can find more details about the Python API here.
Official TREC Fair Ranking 2022 train set.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("trec-fair/2022/train")
for query in dataset.queries_iter():
query # namedtuple<query_id, text, url>
You can find more details about the Python API here.