---
language: en
license: apache-2.0
library_name: transformers
base_model: jhu-clsp/ettin-encoder-68m
model_name: cross-encoder-ettin-68m-ADR-MSE
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-68m-ADR-MSE

[![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-68m`. It was trained on Ms-Marco using loss `ADR` 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** ADR

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-68m-ADR-MSE")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-68m-ADR-MSE")

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        | 34.13     | 40.57     |
| trec2019           | 95.54     | 74.00     |
| trec2020           | 93.98     | 68.79     |
| fever              | 76.77     | 77.02     |
| arguana            | 12.56     | 18.62     |
| climate_fever      | 16.73     | 12.38     |
| dbpedia            | 70.93     | 41.64     |
| fiqa               | 42.21     | 34.62     |
| hotpotqa           | 82.32     | 63.86     |
| nfcorpus           | 53.16     | 32.11     |
| nq                 | 48.06     | 53.34     |
| quora              | 76.63     | 78.38     |
| scidocs            | 23.29     | 12.88     |
| scifact            | 60.29     | 61.83     |
| touche             | 62.63     | 33.60     |
| trec_covid         | 89.06     | 74.14     |
| robust04           | 66.57     | 42.06     |
| lotte_writing      | 69.14     | 60.07     |
| lotte_recreation   | 57.79     | 52.50     |
| lotte_science      | 45.91     | 38.86     |
| lotte_technology   | 52.18     | 43.12     |
| lotte_lifestyle    | 68.75     | 59.76     |
| **Mean In Domain** | **74.55** | **61.12** |
| **BEIR 13**        | **54.97** | **45.72** |
| **LoTTE (OOD)**    | **60.06** | **49.40** |