README.md

---
language: en
license: apache-2.0
library_name: transformers
base_model: bert-base-uncased
model_name: cross-encoder-bert-base-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-bert-base-DistillRankNET

[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](http://arxiv.org/abs/2603.03010)
[![All Models](https://img.shields.io/badge/🤗%20Hugging%20Face%20Models-blue)](https://huggingface.co/collections/xpmir/reproducing-cross-encoders)
[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](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 `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-bert-base-DistillRankNET")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-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        | 36.42     | 42.84     |
| trec2019           | 95.74     | 74.15     |
| trec2020           | 94.25     | 72.10     |
| fever              | 81.04     | 80.99     |
| arguana            | 22.80     | 34.31     |
| climate_fever      | 29.17     | 21.50     |
| dbpedia            | 76.58     | 45.80     |
| fiqa               | 43.41     | 35.34     |
| hotpotqa           | 89.45     | 72.86     |
| nfcorpus           | 56.85     | 34.36     |
| nq                 | 52.57     | 57.27     |
| quora              | 76.95     | 78.94     |
| scidocs            | 28.31     | 15.65     |
| scifact            | 67.81     | 70.21     |
| touche             | 63.22     | 34.36     |
| trec_covid         | 89.83     | 68.52     |
| robust04           | 69.69     | 47.75     |
| lotte_writing      | 64.88     | 55.85     |
| lotte_recreation   | 58.11     | 52.84     |
| lotte_science      | 43.32     | 36.06     |
| lotte_technology   | 49.62     | 41.06     |
| lotte_lifestyle    | 70.00     | 60.53     |
| **Mean In Domain** | **75.47** | **63.03** |
| **BEIR 13**        | **59.85** | **50.01** |
| **LoTTE (OOD)**    | **59.27** | **49.01** |