Datasetpublic

pyterrier/dbpedia-entity.retromae.flex

dbpedia-entity.retromae.flex Description RetroMAE index for DBPedia-Entity Usage # Load the artifact import pyterrier_alpha as pta artifact = pta.Artifact.from_hf('pyterrier/dbpedia-entity.retromae.flex') artifact.np_retriever() Benchmarks dbpedia-entity/dev name nDCG@10 R@1000 np (flat) 0.4687 0.7372 dbpedia-entity/test name nDCG@10 R@1000 np (flat) 0.3729 0.678 Reproduction import… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier/dbpedia-entity.retromae.flex.

sourceHugging Faceupdated 2y agoView on Hugging Face

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dbpedia-entity.retromae.flex

Description

RetroMAE index for DBPedia-Entity

Usage

python

# Load the artifact
import pyterrier_alpha as pta
artifact = pta.Artifact.from_hf('pyterrier/dbpedia-entity.retromae.flex')
artifact.np_retriever()

Benchmarks

dbpedia-entity/dev

name	nDCG@10	R@1000
np (flat)	0.4687	0.7372

dbpedia-entity/test

name	nDCG@10	R@1000
np (flat)	0.3729	0.678

Reproduction

python

import pyterrier as pt
from tqdm import tqdm
import ir_datasets
from pyterrier_dr import FlexIndex, RetroMAE
pipeline = RetroMAE.msmarco_distill() >> FlexIndex("dbpedia-entity.retromae.flex")
dataset = ir_datasets.load('beir/dbpedia-entity')
docs = ({'docno': d.doc_id, 'text': d.default_text()} for d in tqdm(dataset.docs))
pipeline.index(docs)

Metadata

{
  "type": "dense_index",
  "format": "flex",
  "vec_size": 768,
  "doc_count": 4635922
}