| --- |
| language: en |
| license: apache-2.0 |
| library_name: transformers |
| base_model: google/electra-base-discriminator |
| model_name: cross-encoder-ELECTRA-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-ELECTRA-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 `google/electra-base-discriminator`. 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-ELECTRA-DistillRankNET") |
| model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ELECTRA-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 | 37.50 | 44.08 | |
| | trec2019 | 100.00 | 77.88 | |
| | trec2020 | 95.00 | 74.82 | |
| | fever | 79.89 | 80.03 | |
| | arguana | 15.87 | 24.53 | |
| | climate_fever | 22.70 | 17.38 | |
| | dbpedia | 77.35 | 47.24 | |
| | fiqa | 46.89 | 38.68 | |
| | hotpotqa | 86.53 | 67.52 | |
| | nfcorpus | 55.78 | 34.33 | |
| | nq | 55.00 | 60.02 | |
| | quora | 77.07 | 79.32 | |
| | scidocs | 27.87 | 15.98 | |
| | scifact | 62.64 | 65.76 | |
| | touche | 68.69 | 35.77 | |
| | trec_covid | 87.97 | 70.20 | |
| | robust04 | 70.36 | 49.20 | |
| | lotte_writing | 70.07 | 61.35 | |
| | lotte_recreation | 62.44 | 56.76 | |
| | lotte_science | 47.24 | 40.02 | |
| | lotte_technology | 55.93 | 47.04 | |
| | lotte_lifestyle | 74.60 | 64.90 | |
| | **Mean In Domain** | **77.50** | **65.59** | |
| | **BEIR 13** | **58.79** | **48.98** | |
| | **LoTTE (OOD)** | **63.44** | **53.21** | |
