---
language: en
license: apache-2.0
library_name: transformers
base_model: jhu-clsp/ettin-encoder-32m
model_name: cross-encoder-ettin-32m-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-ettin-32m-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 `jhu-clsp/ettin-encoder-32m`. 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-ettin-32m-DistillRankNET")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-32m-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        | 29.69     | 35.29     |
| trec2019           | 91.86     | 62.04     |
| trec2020           | 85.57     | 63.47     |
| fever              | 70.41     | 71.33     |
| arguana            | 8.61      | 13.20     |
| climate_fever      | 16.04     | 11.98     |
| dbpedia            | 61.21     | 34.43     |
| fiqa               | 32.94     | 25.37     |
| hotpotqa           | 74.34     | 57.33     |
| nfcorpus           | 40.43     | 23.10     |
| nq                 | 38.18     | 42.81     |
| quora              | 72.61     | 73.97     |
| scidocs            | 21.50     | 11.66     |
| scifact            | 51.45     | 54.28     |
| touche             | 64.88     | 31.23     |
| trec_covid         | 88.83     | 64.72     |
| robust04           | 52.38     | 31.19     |
| lotte_writing      | 59.75     | 50.70     |
| lotte_recreation   | 48.66     | 43.92     |
| lotte_science      | 38.10     | 32.33     |
| lotte_technology   | 42.30     | 34.81     |
| lotte_lifestyle    | 59.83     | 50.72     |
| **Mean In Domain** | **69.04** | **53.60** |
| **BEIR 13**        | **49.34** | **39.65** |
| **LoTTE (OOD)**    | **50.17** | **40.61** |