| --- |
| language: en |
| license: apache-2.0 |
| library_name: transformers |
| base_model: microsoft/MiniLM-L12-H384-uncased |
| model_name: cross-encoder-MiniLM-L12-DistillRankNET |
| 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-MiniLM-L12-DistillRankNET |
|
|
| [](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 `microsoft/MiniLM-L12-H384-uncased`. It was trained on Ms-Marco using loss `distillRankNET` 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** distillRankNET |
|
|
| 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-MiniLM-L12-DistillRankNET") |
| model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-MiniLM-L12-DistillRankNET") |
| |
| 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 | 37.40 | 43.98 | |
| | trec2019 | 96.12 | 74.57 | |
| | trec2020 | 93.83 | 73.48 | |
| | fever | 81.21 | 80.95 | |
| | arguana | 18.48 | 27.97 | |
| | climate_fever | 27.52 | 20.31 | |
| | dbpedia | 75.81 | 46.06 | |
| | fiqa | 43.71 | 36.25 | |
| | hotpotqa | 85.35 | 66.48 | |
| | nfcorpus | 57.75 | 34.59 | |
| | nq | 53.19 | 58.21 | |
| | quora | 76.34 | 78.62 | |
| | scidocs | 28.06 | 15.79 | |
| | scifact | 66.12 | 69.34 | |
| | touche | 64.33 | 34.46 | |
| | trec_covid | 87.17 | 70.74 | |
| | robust04 | 75.25 | 52.28 | |
| | lotte_writing | 66.66 | 58.11 | |
| | lotte_recreation | 60.60 | 55.12 | |
| | lotte_science | 46.01 | 38.34 | |
| | lotte_technology | 53.36 | 44.41 | |
| | lotte_lifestyle | 71.62 | 61.69 | |
| | **Mean In Domain** | **75.78** | **64.01** | |
| | **BEIR 13** | **58.85** | **49.21** | |
| | **LoTTE (OOD)** | **62.25** | **51.66** | |
