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

[![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 `bce` 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** bce

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

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        | 34.09     | 40.72     |
| trec2019           | 88.98     | 65.52     |
| trec2020           | 86.88     | 64.25     |
| fever              | 74.36     | 74.93     |
| arguana            | 13.58     | 19.34     |
| climate_fever      | 13.37     | 9.62      |
| dbpedia            | 62.96     | 34.72     |
| fiqa               | 42.67     | 34.21     |
| hotpotqa           | 80.90     | 63.82     |
| nfcorpus           | 43.92     | 24.92     |
| nq                 | 46.00     | 51.23     |
| quora              | 71.33     | 73.68     |
| scidocs            | 22.35     | 12.45     |
| scifact            | 57.45     | 59.11     |
| touche             | 53.79     | 29.25     |
| trec_covid         | 90.29     | 67.40     |
| robust04           | 46.58     | 27.32     |
| lotte_writing      | 66.33     | 57.78     |
| lotte_recreation   | 57.76     | 52.61     |
| lotte_science      | 41.95     | 36.62     |
| lotte_technology   | 49.58     | 41.97     |
| lotte_lifestyle    | 66.95     | 58.78     |
| **Mean In Domain** | **69.98** | **56.83** |
| **BEIR 13**        | **51.77** | **42.67** |
| **LoTTE (OOD)**    | **54.86** | **45.85** |