capreolus.benchmark.robust04
¶
Module Contents¶
Classes¶
Robust04 benchmark using the title folds from Huston and Croft. [1] Each of these is used as the test set. |
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Robust04 benchmark using the folds from Yang et al. [1] |
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Robust04 benchmark using the folds from Yang et al. [1] |
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Base class for Benchmark modules. The purpose of a Benchmark is to provide the data needed to run an experiment, such as queries, folds, and relevance judgments. |
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Base class for Benchmark modules. The purpose of a Benchmark is to provide the data needed to run an experiment, such as queries, folds, and relevance judgments. |
Attributes¶
- class capreolus.benchmark.robust04.Robust04(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
capreolus.benchmark.Benchmark
Robust04 benchmark using the title folds from Huston and Croft. [1] Each of these is used as the test set. Given the remaining four folds, we split them into the same train and dev sets used in recent work. [2]
[1] Samuel Huston and W. Bruce Croft. 2014. Parameters learned in the comparison of retrieval models using term dependencies. Technical Report.
[2] Sean MacAvaney, Andrew Yates, Arman Cohan, Nazli Goharian. 2019. CEDR: Contextualized Embeddings for Document Ranking. SIGIR 2019.
- class capreolus.benchmark.robust04.Robust04Yang19(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
capreolus.benchmark.Benchmark
Robust04 benchmark using the folds from Yang et al. [1]
[1] Wei Yang, Kuang Lu, Peilin Yang, and Jimmy Lin. 2019. Critically Examining the “Neural Hype”: Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models. SIGIR 2019.
- class capreolus.benchmark.robust04.Robust04Yang19Desc(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
Robust04Yang19
,capreolus.benchmark.Benchmark
Robust04 benchmark using the folds from Yang et al. [1]
[1] Wei Yang, Kuang Lu, Peilin Yang, and Jimmy Lin. 2019. Critically Examining the “Neural Hype”: Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models. SIGIR 2019.
- class capreolus.benchmark.robust04.Robust04Huston14(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
capreolus.benchmark.Benchmark
Base class for Benchmark modules. The purpose of a Benchmark is to provide the data needed to run an experiment, such as queries, folds, and relevance judgments.
- Modules should provide:
a
topics
dict mapping query ids (qids) to queriesa
qrels
dict mapping qids to docids and relevance labelsa
folds
dict mapping a fold name to training, dev (validation), and testing qidsif these can be loaded from files in standard formats, they can be specified by setting the
topic_file
,qrel_file
, andfold_file
, respectively, rather than by setting the above attributes directly
- class capreolus.benchmark.robust04.Robust04Huston14Desc(config=None, provide=None, share_dependency_objects=False, build=True)[source]¶
Bases:
Robust04Huston14
,capreolus.benchmark.Benchmark
Base class for Benchmark modules. The purpose of a Benchmark is to provide the data needed to run an experiment, such as queries, folds, and relevance judgments.
- Modules should provide:
a
topics
dict mapping query ids (qids) to queriesa
qrels
dict mapping qids to docids and relevance labelsa
folds
dict mapping a fold name to training, dev (validation), and testing qidsif these can be loaded from files in standard formats, they can be specified by setting the
topic_file
,qrel_file
, andfold_file
, respectively, rather than by setting the above attributes directly