msmarco-passage.tct-hnp.flex

Description

This artifact represents the retrieval index of the MSMarco Passage collection produced by your TCT-ColBERT model (version hnp).

Usage

The following code snippete shows how to use this artifact to perform a dense retrieval experiment:

import pyterrier as pt
import pyterrier_dr
from pyterrier.measures import *

# Load the artifact
index = pt.Artifact.from_hf('ntonellotto/msmarco-passage.tct-hnp.flex')
# Load the query encoder (must be the same used to create the artifact)
model = pyterrier_dr.TctColBert.hnp()
# Load the dataset
dataset = pt.get_dataset('msmarco_passage')
# Run the PyTerrier experiment
pt.Experiment(
    [model >> index],
    dataset.get_topics('test-2019'),
    dataset.get_qrels('test-2019'),
    eval_metrics=[RR@10, Recall(rel=2)@100, Recall@100, nDCG@10, "mrt"],
    names=["Dense Retrieval"]
)

You should get something like:

name	RR@10	R(rel=2)@100	R@100	nDCG@10	mrt
Dense Retrieval	0.976744	0.607269	0.514744	0.718238	280.014161

Reproduction

Generating the data

The data has been generated using the following code snippet:

import torch
import pyterrier as pt
import pyterrier_dr

# Select the available Torch's backend
if torch.backends.mps.is_available():
    device = torch.device("mps")
elif torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")
print(f"Using device: {device}")
# Create the encoder model
tct = pyterrier_dr.TctColBert.hnp(device=device)
# Create the destination index folder
index = pyterrier_dr.FlexIndex("./msmarco-passage.tct-hnp.flex")
# Indexing
(tct >> index).index(pt.get_dataset("msmarco_passage").get_corpus_iter())

print(f"Indexed {len(index)} documents")

Uploading the data

To upload the index to Hugging Face, use the following code snippet (the dataset sheet will be created automatically):

import pyterrier as pt

# Load the artifact from disk
artifact = pt.Artifact.load("./msmarco-passage.tct-hnp.flex")
# Upload the artifact to HF
artifact.to_hf('ntonellotto/msmarco-passage.tct-hnp.flex')

Metadata

{
  "type": "dense_index",
  "format": "flex",
  "vec_size": 768,
  "doc_count": 8841823
}