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---
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"
---
# Two Tower MonoBERT trained on Baidu-ULTR
A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with an **additivie two tower architecture** as suggested by [Yan et al](https://research.google/pubs/revisiting-two-tower-models-for-unbiased-learning-to-rank/). Similar to a position-based click model (PBM), a two tower model jointly learns item relevance (with a BERT model) and position bias (in our case using a single embedding per rank). For more info, [read our paper](https://arxiv.org/abs/2404.02543) and [find the code for this model here](https://github.com/philipphager/baidu-bert-model).
## 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](https://huggingface.co/philipphager/baidu-ultr_uva-bert_naive-pointwise) | 0.227 | 1.641 | 3.462 | 4.752 | 7.251 | 0.357 | 0.609 |
| [Pointwise Two-Tower](https://huggingface.co/philipphager/baidu-ultr_uva-bert_twotower) | 0.218 | 1.629 | 3.471 | 4.822 | 7.456 | 0.367 | 0.607 |
| [Pointwise IPS](https://huggingface.co/philipphager/baidu-ultr_uva-bert_ips-pointwise) | 0.222 | 1.295 | 2.811 | 3.977 | 6.296 | 0.307 | 0.534 |
| [Listwise Naive](https://huggingface.co/philipphager/baidu-ultr_uva-bert_naive-listwise) | - | 1.947 | 4.108 | 5.614 | 8.478 | 0.405 | 0.639 |
| [Listwise IPS](https://huggingface.co/philipphager/baidu-ultr_uva-bert_ips-listwise) | - | 1.671 | 3.530 | 4.873 | 7.450 | 0.361 | 0.603 |
| [Listwise DLA](https://huggingface.co/philipphager/baidu-ultr_uva-bert_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](https://github.com/philipphager/baidu-bert-model/blob/main/main.py) and [evaluation scripts](https://github.com/philipphager/baidu-bert-model/blob/main/eval.py) in our code repository.
```Python
import jax.numpy as jnp
from src.model import PBMCrossEncoder
model = PBMCrossEncoder.from_pretrained(
"philipphager/baidu-ultr_uva-bert_twotower",
)
# 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},
}
```
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