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

[![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 `hingeLoss` 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** hingeLoss

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("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-Hinge")

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        | 38.05     | 44.38     |
| trec2019           | 97.09     | 73.12     |
| trec2020           | 93.52     | 70.83     |
| fever              | 79.83     | 79.96     |
| arguana            | 22.79     | 33.87     |
| climate_fever      | 33.40     | 24.97     |
| dbpedia            | 73.46     | 43.13     |
| fiqa               | 42.20     | 34.97     |
| hotpotqa           | 88.41     | 72.40     |
| nfcorpus           | 54.88     | 33.45     |
| nq                 | 51.22     | 56.13     |
| quora              | 77.95     | 79.84     |
| scidocs            | 26.64     | 15.26     |
| scifact            | 66.32     | 69.18     |
| touche             | 59.68     | 32.27     |
| trec_covid         | 89.62     | 63.75     |
| robust04           | 71.38     | 46.18     |
| lotte_writing      | 64.17     | 55.35     |
| lotte_recreation   | 59.69     | 54.46     |
| lotte_science      | 42.39     | 35.07     |
| lotte_technology   | 51.80     | 42.35     |
| lotte_lifestyle    | 69.76     | 60.15     |
| **Mean In Domain** | **76.22** | **62.78** |
| **BEIR 13**        | **58.95** | **49.17** |
| **LoTTE (OOD)**    | **59.86** | **48.93** |