metadata

license: mit
datasets:
  - philipphager/baidu-ultr-pretrain
  - philipphager/baidu-ultr_uva-mlm-ctr
metrics:
  - log-likelihood
  - dcg@1
  - dcg@3
  - dcg@5
  - dcg@10
  - ndcg@10
  - mrr@10
co2_eq_emissions:
  emissions: 2090
  source: >-
    Calculated using the [ML CO2 impact
    calculator](https://mlco2.github.io/impact/#compute), training for 4 x 45
    hours with a carbon efficiency of 0.029 kg/kWh. You can inspect the carbon
    efficiency of the French national grid provider here:
    https://www.rte-france.com/eco2mix/les-emissions-de-co2-par-kwh-produit-en-france
  training_type: Pre-training
  geographical_location: Grenoble, France
  hardware_used: 4 NVIDIA H100-80GB GPUs

Listwise MonoBERT trained on Baidu-ULTR using the Dual Learning Algorithm (DLA)

A flax-based MonoBERT cross encoder trained on the Baidu-ULTR dataset with a listwise DLA objective on clicks. Following Ai et al., the dual learning algorithm jointly infers item relevance (using a BERT model) and position bias (in our case, a single embedding parameter per rank), both by optimizing a listwise softmax cross-entropy loss. For more info, read our paper and find the code for this model here.

Test Results on Baidu-ULTR

Ranking performance is measured in DCG, nDCG, and MRR on expert annotations (6,985 queries). Click prediction performance is measured in log-likelihood on one test partition of user clicks (≈297k queries).

Model	Log-likelihood	DCG@1	DCG@3	DCG@5	DCG@10	nDCG@10	MRR@10
Pointwise Naive	0.227	1.641	3.462	4.752	7.251	0.357	0.609
Pointwise Two-Tower	0.218	1.629	3.471	4.822	7.456	0.367	0.607
Pointwise IPS	0.222	1.295	2.811	3.977	6.296	0.307	0.534
Listwise Naive	-	1.947	4.108	5.614	8.478	0.405	0.639
Listwise IPS	-	1.671	3.530	4.873	7.450	0.361	0.603
Listwise DLA	-	1.796	3.730	5.125	7.802	0.377	0.615

Usage

Here is an example of downloading the model and calling it for inference on a mock batch of input data. For more details on how to use the model on the Baidu-ULTR dataset, take a look at our training and evaluation scripts in our code repository.

import jax.numpy as jnp

from src.model import DLACrossEncoder

model = DLACrossEncoder.from_pretrained(
    "philipphager/baidu-ultr_uva-bert_dla",
)

# Mock batch following Baidu-ULTR with 4 documents, each with 8 tokens
batch = {
    # Query_id for each document
    "query_id": jnp.array([1, 1, 1, 1]),
    # Document position in SERP
    "positions": jnp.array([1, 2, 3, 4]),
    # Token ids for: [CLS] Query [SEP] Document
    "tokens": jnp.array([
        [2, 21448, 21874, 21436, 1, 20206, 4012, 2860],
        [2, 21448, 21874, 21436, 1, 16794, 4522, 2082],
        [2, 21448, 21874, 21436, 1, 20206, 10082, 9773],
        [2, 21448, 21874, 21436, 1, 2618, 8520, 2860],
  ]),
    # Specify if a token id belongs to the query (0) or document (1)
    "token_types": jnp.array([
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
        [0, 0, 0, 0, 1, 1, 1, 1],
    ]),
    # Marks if a token should be attended to (True) or ignored, e.g., padding tokens (False):
    "attention_mask": jnp.array([
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
        [True, True, True, True, True, True, True, True],
    ]),
}

outputs = model(batch, train=False)
print(outputs)

Reference

@inproceedings{Hager2024BaiduULTR,
  author = {Philipp Hager and Romain Deffayet and Jean-Michel Renders and Onno Zoeter and Maarten de Rijke},
  title = {Unbiased Learning to Rank Meets Reality: Lessons from Baidu’s Large-Scale Search Dataset},
  booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`24)},
  organization = {ACM},
  year = {2024},
}