| --- |
| language: en |
| license: apache-2.0 |
| library_name: transformers |
| base_model: bert-base-uncased |
| model_name: cross-encoder-bert-base-ADR-MSE |
| source: https://github.com/xpmir/cross-encoders |
| paper: http://arxiv.org/abs/2603.03010 |
| tags: |
| - cross-encoder |
| - sequence-classification |
| - tensorboard |
| datasets: |
| - msmarco |
| pipeline_tag: text-classification |
| --- |
| |
| # cross-encoder-bert-base-ADR-MSE |
|
|
| [](http://arxiv.org/abs/2603.03010) |
| [](https://huggingface.co/collections/xpmir/reproducing-cross-encoders) |
| [](https://github.com/xpmir/cross-encoders) |
|
|
| This model is a cross-encoder based on `bert-base-uncased`. It was trained on Ms-Marco using loss `ADR` as part of a reproducibility paper for training cross encoders: "**[Reproducing and Comparing Distillation Techniques for Cross-Encoders](http://arxiv.org/abs/2603.03010)**", see the paper for more details. |
|
|
|
|
| ### Contents |
| - [Model Description](#model-description) |
| - [Usage](#usage) |
| - [Evals](#evaluations) |
|
|
|
|
| ## Model Description |
|
|
| This model is intended for **re-ranking** the top results returned by a retrieval system (like BM25, Bi-Encoders or SPLADE). |
|
|
| - **Training Data:** MS MARCO Passage |
| - **Language:** English |
| - **Loss** ADR |
|
|
| Training can be easily reproduced using the assiciated repository. |
| The exact training configuration used for this model is also detailed in [config.yaml](./config.yaml). |
|
|
| ## Usage |
|
|
| Quick Start: |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-bert-base-ADR-MSE") |
| model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-ADR-MSE") |
| |
| features = tokenizer("What is experimaestro ?", "Experimaestro is a powerful framework for ML experiments management...", padding=True, truncation=True, return_tensors="pt") |
| |
| model.eval() |
| with torch.no_grad(): |
| scores = model(**features).logits |
| print(scores) |
| ``` |
|
|
| ## Evaluations |
|
|
| We provide evaluations of this cross-encoder re-ranking the top `1000` documents retrieved by `naver/splade-v3-distilbert`. |
|
|
| | dataset | RR@10 | nDCG@10 | |
| |:-------------------|:----------|:----------| |
| | msmarco_dev | 36.50 | 42.98 | |
| | trec2019 | 97.29 | 74.07 | |
| | trec2020 | 92.87 | 71.74 | |
| | fever | 81.06 | 81.04 | |
| | arguana | 23.00 | 34.49 | |
| | climate_fever | 27.78 | 20.52 | |
| | dbpedia | 76.55 | 46.14 | |
| | fiqa | 42.55 | 34.79 | |
| | hotpotqa | 90.03 | 73.39 | |
| | nfcorpus | 55.59 | 34.20 | |
| | nq | 53.32 | 58.11 | |
| | quora | 80.84 | 82.20 | |
| | scidocs | 28.26 | 15.66 | |
| | scifact | 66.07 | 69.12 | |
| | touche | 62.66 | 33.81 | |
| | trec_covid | 84.83 | 65.90 | |
| | robust04 | 70.37 | 48.02 | |
| | lotte_writing | 65.26 | 56.58 | |
| | lotte_recreation | 58.83 | 53.28 | |
| | lotte_science | 43.66 | 36.67 | |
| | lotte_technology | 50.56 | 42.04 | |
| | lotte_lifestyle | 68.66 | 59.78 | |
| | **Mean In Domain** | **75.55** | **62.93** | |
| | **BEIR 13** | **59.43** | **49.95** | |
| | **LoTTE (OOD)** | **59.56** | **49.39** | |
