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

[![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 `infoNCE` 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** infoNCE

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

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        | 40.21     | 46.90     |
| trec2019           | 95.04     | 73.74     |
| trec2020           | 94.75     | 72.76     |
| fever              | 77.53     | 77.61     |
| arguana            | 15.59     | 22.32     |
| climate_fever      | 22.02     | 16.08     |
| dbpedia            | 72.45     | 43.43     |
| fiqa               | 47.94     | 39.50     |
| hotpotqa           | 86.41     | 70.27     |
| nfcorpus           | 53.89     | 33.28     |
| nq                 | 52.46     | 57.73     |
| quora              | 78.53     | 80.60     |
| scidocs            | 28.38     | 15.73     |
| scifact            | 67.40     | 70.03     |
| touche             | 63.39     | 36.10     |
| trec_covid         | 89.83     | 75.08     |
| robust04           | 67.90     | 45.38     |
| lotte_writing      | 72.76     | 64.23     |
| lotte_recreation   | 63.21     | 57.33     |
| lotte_science      | 52.26     | 42.76     |
| lotte_technology   | 56.39     | 48.19     |
| lotte_lifestyle    | 72.71     | 63.47     |
| **Mean In Domain** | **76.67** | **64.47** |
| **BEIR 13**        | **58.14** | **49.06** |
| **LoTTE (OOD)**    | **64.21** | **53.56** |