---
language: en
license: apache-2.0
library_name: transformers
base_model: jhu-clsp/ettin-encoder-32m
model_name: cross-encoder-ettin-32m-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-ettin-32m-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 `jhu-clsp/ettin-encoder-32m`. 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("xpmir/cross-encoder-ettin-32m-Hinge")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-32m-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        | 33.98     | 40.04     |
| trec2019           | 91.81     | 66.69     |
| trec2020           | 90.39     | 65.52     |
| fever              | 69.95     | 70.72     |
| arguana            | 12.21     | 18.58     |
| climate_fever      | 20.13     | 14.67     |
| dbpedia            | 60.87     | 34.71     |
| fiqa               | 39.77     | 31.69     |
| hotpotqa           | 79.42     | 62.45     |
| nfcorpus           | 51.64     | 30.62     |
| nq                 | 42.88     | 47.91     |
| quora              | 76.94     | 78.36     |
| scidocs            | 24.01     | 13.22     |
| scifact            | 61.83     | 64.79     |
| touche             | 65.31     | 32.77     |
| trec_covid         | 88.00     | 67.20     |
| robust04           | 61.43     | 37.53     |
| lotte_writing      | 65.38     | 55.76     |
| lotte_recreation   | 54.31     | 49.21     |
| lotte_science      | 44.43     | 36.64     |
| lotte_technology   | 49.41     | 41.33     |
| lotte_lifestyle    | 65.28     | 56.09     |
| **Mean In Domain** | **72.06** | **57.42** |
| **BEIR 13**        | **53.30** | **43.67** |
| **LoTTE (OOD)**    | **56.71** | **46.09** |