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--- |
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license: mit |
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datasets: |
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- philipphager/baidu-ultr-pretrain |
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- philipphager/baidu-ultr_uva-mlm-ctr |
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metrics: |
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- log-likelihood |
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- dcg@1 |
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- dcg@3 |
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- dcg@5 |
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- dcg@10 |
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- ndcg@10 |
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- mrr@10 |
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co2_eq_emissions: |
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emissions: 2090 |
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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" |
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training_type: "Pre-training" |
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geographical_location: "Grenoble, France" |
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hardware_used: "4 NVIDIA H100-80GB GPUs" |
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--- |
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# Listwise MonoBERT trained on Baidu-ULTR using the Dual Learning Algorithm (DLA) |
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A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with a **listwise DLA objective on clicks**. Following [Ai et al.](https://arxiv.org/abs/1804.05938), 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](https://arxiv.org/abs/2404.02543) and [find the code for this model here](https://github.com/philipphager/baidu-bert-model). |
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## Test Results on Baidu-ULTR |
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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). |
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| Model | Log-likelihood | DCG@1 | DCG@3 | DCG@5 | DCG@10 | nDCG@10 | MRR@10 | |
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|------------------------------------------------------------------------------------------------|----------------|-------|-------|-------|--------|---------|--------| |
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| [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 | |
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| [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 | |
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| [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 | |
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| [Listwise Naive](https://huggingface.co/philipphager/baidu-ultr_uva-bert_naive-listwise) | - | 1.947 | 4.108 | 5.614 | 8.478 | 0.405 | 0.639 | |
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| [Listwise IPS](https://huggingface.co/philipphager/baidu-ultr_uva-bert_ips-listwise) | - | 1.671 | 3.530 | 4.873 | 7.450 | 0.361 | 0.603 | |
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| [Listwise DLA](https://huggingface.co/philipphager/baidu-ultr_uva-bert_dla) | - | 1.796 | 3.730 | 5.125 | 7.802 | 0.377 | 0.615 | |
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## Usage |
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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. |
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```Python |
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import jax.numpy as jnp |
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from src.model import DLACrossEncoder |
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model = DLACrossEncoder.from_pretrained( |
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"philipphager/baidu-ultr_uva-bert_dla", |
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) |
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# Mock batch following Baidu-ULTR with 4 documents, each with 8 tokens |
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batch = { |
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# Query_id for each document |
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"query_id": jnp.array([1, 1, 1, 1]), |
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# Document position in SERP |
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"positions": jnp.array([1, 2, 3, 4]), |
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# Token ids for: [CLS] Query [SEP] Document |
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"tokens": jnp.array([ |
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[2, 21448, 21874, 21436, 1, 20206, 4012, 2860], |
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[2, 21448, 21874, 21436, 1, 16794, 4522, 2082], |
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[2, 21448, 21874, 21436, 1, 20206, 10082, 9773], |
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[2, 21448, 21874, 21436, 1, 2618, 8520, 2860], |
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]), |
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# Specify if a token id belongs to the query (0) or document (1) |
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"token_types": jnp.array([ |
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[0, 0, 0, 0, 1, 1, 1, 1], |
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[0, 0, 0, 0, 1, 1, 1, 1], |
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[0, 0, 0, 0, 1, 1, 1, 1], |
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[0, 0, 0, 0, 1, 1, 1, 1], |
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]), |
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# Marks if a token should be attended to (True) or ignored, e.g., padding tokens (False): |
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"attention_mask": jnp.array([ |
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[True, True, True, True, True, True, True, True], |
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[True, True, True, True, True, True, True, True], |
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[True, True, True, True, True, True, True, True], |
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[True, True, True, True, True, True, True, True], |
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]), |
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} |
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outputs = model(batch, train=False) |
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print(outputs) |
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``` |
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## Reference |
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``` |
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@inproceedings{Hager2024BaiduULTR, |
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author = {Philipp Hager and Romain Deffayet and Jean-Michel Renders and Onno Zoeter and Maarten de Rijke}, |
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title = {Unbiased Learning to Rank Meets Reality: Lessons from Baidu’s Large-Scale Search Dataset}, |
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booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`24)}, |
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organization = {ACM}, |
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year = {2024}, |
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} |
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``` |
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